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Namespaces</h2></td></tr>
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<tr class="memitem:namespacesmall__gicp_1_1traits"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp_1_1traits.html">traits</a></td></tr>
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Classes</h2></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Point container with a flat vector. <a href="structsmall__gicp_1_1FlatContainer.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1GaussianVoxel.html">GaussianVoxel</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Gaussian voxel that computes and stores voxel mean and covariance. <a href="structsmall__gicp_1_1GaussianVoxel.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1VoxelInfo.html">VoxelInfo</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Voxel meta information. <a href="structsmall__gicp_1_1VoxelInfo.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1IncrementalVoxelMap.html">IncrementalVoxelMap</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Incremental voxelmap. This class supports incremental point cloud insertion and LRU-based voxel deletion that removes voxels that are not recently referenced. <a href="structsmall__gicp_1_1IncrementalVoxelMap.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1KdTreeNode.html">KdTreeNode</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1KdTree.html" title=""Safe" KdTree that holds the ownership of the input points.">KdTree</a> node. <a href="structsmall__gicp_1_1KdTreeNode.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1KdTreeBuilder.html">KdTreeBuilder</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Single thread Kd-tree builder. <a href="structsmall__gicp_1_1KdTreeBuilder.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1UnsafeKdTree.html">UnsafeKdTree</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">"Unsafe" <a class="el" href="structsmall__gicp_1_1KdTree.html" title=""Safe" KdTree that holds the ownership of the input points.">KdTree</a>. <a href="structsmall__gicp_1_1UnsafeKdTree.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">"Safe" <a class="el" href="structsmall__gicp_1_1KdTree.html" title=""Safe" KdTree that holds the ownership of the input points.">KdTree</a> that holds the ownership of the input points. <a href="structsmall__gicp_1_1KdTree.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1KdTreeBuilderOMP.html">KdTreeBuilderOMP</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Kd-tree builder with OpenMP. <a href="structsmall__gicp_1_1KdTreeBuilderOMP.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1KdTreeBuilderTBB.html">KdTreeBuilderTBB</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Kd-tree builder with TBB. <a href="structsmall__gicp_1_1KdTreeBuilderTBB.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1KnnSetting.html">KnnSetting</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">K-nearest neighbor search setting. <a href="structsmall__gicp_1_1KnnSetting.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1identity__transform.html">identity_transform</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Identity transform (alternative to std::identity in C++20). <a href="structsmall__gicp_1_1identity__transform.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1KnnResult.html">KnnResult</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">K-nearest neighbor search result container. <a href="structsmall__gicp_1_1KnnResult.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1ProjectionSetting.html">ProjectionSetting</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Parameters to control the projection axis search. <a href="structsmall__gicp_1_1ProjectionSetting.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1AxisAlignedProjection.html">AxisAlignedProjection</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Conventional axis-aligned projection (i.e., selecting any of XYZ axes with the largest variance). <a href="structsmall__gicp_1_1AxisAlignedProjection.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1NormalProjection.html">NormalProjection</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Normal projection (i.e., selecting the 3D direction with the largest variance of the points). <a href="structsmall__gicp_1_1NormalProjection.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1NullFactor.html">NullFactor</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Null factor that gives no constraints. <a href="structsmall__gicp_1_1NullFactor.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1RestrictDoFFactor.html">RestrictDoFFactor</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Factor to restrict the degrees of freedom of optimization (e.g., fixing roll, pitch rotation). <a href="structsmall__gicp_1_1RestrictDoFFactor.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1GICPFactor.html">GICPFactor</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">GICP (distribution-to-distribution) per-point error factor. <a href="structsmall__gicp_1_1GICPFactor.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1ICPFactor.html">ICPFactor</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Point-to-point per-point error factor. <a href="structsmall__gicp_1_1ICPFactor.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1PointToPlaneICPFactor.html">PointToPlaneICPFactor</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Point-to-plane per-point error factor. <a href="structsmall__gicp_1_1PointToPlaneICPFactor.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1Huber.html">Huber</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1Huber.html" title="Huber robust kernel.">Huber</a> robust kernel. <a href="structsmall__gicp_1_1Huber.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1Cauchy.html">Cauchy</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1Cauchy.html" title="Cauchy robust kernel.">Cauchy</a> robust kernel. <a href="structsmall__gicp_1_1Cauchy.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1RobustFactor.html">RobustFactor</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Robustify a factor with a robust kernel. <a href="structsmall__gicp_1_1RobustFactor.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1PointCloudProxy.html">PointCloudProxy</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Proxy class to access PCL point cloud with external covariance matrices. <a href="structsmall__gicp_1_1PointCloudProxy.html#details">More...</a><br /></td></tr>
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="classsmall__gicp_1_1RegistrationPCL.html">RegistrationPCL</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">PCL registration interfaces. <a href="classsmall__gicp_1_1RegistrationPCL.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Point cloud. <a href="structsmall__gicp_1_1PointCloud.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1GaussNewtonOptimizer.html">GaussNewtonOptimizer</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">GaussNewton optimizer. <a href="structsmall__gicp_1_1GaussNewtonOptimizer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1LevenbergMarquardtOptimizer.html">LevenbergMarquardtOptimizer</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">LevenbergMarquardt optimizer. <a href="structsmall__gicp_1_1LevenbergMarquardtOptimizer.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1SerialReduction.html">SerialReduction</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Single-thread reduction. <a href="structsmall__gicp_1_1SerialReduction.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1ParallelReductionOMP.html">ParallelReductionOMP</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Parallel reduction with OpenMP backend. <a href="structsmall__gicp_1_1ParallelReductionOMP.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1LinearizeSum.html">LinearizeSum</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Summation for linearized systems. <a href="structsmall__gicp_1_1LinearizeSum.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1ErrorSum.html">ErrorSum</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Summation for evaluated errors. <a href="structsmall__gicp_1_1ErrorSum.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1ParallelReductionTBB.html">ParallelReductionTBB</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Parallel reduction with TBB backend. <a href="structsmall__gicp_1_1ParallelReductionTBB.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1Registration.html">Registration</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Point cloud registration. <a href="structsmall__gicp_1_1Registration.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> setting. <a href="structsmall__gicp_1_1RegistrationSetting.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1RegistrationResult.html">RegistrationResult</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> result. <a href="structsmall__gicp_1_1RegistrationResult.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1NullRejector.html">NullRejector</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Null correspondence rejector. This class accepts all input correspondences. <a href="structsmall__gicp_1_1NullRejector.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1DistanceRejector.html">DistanceRejector</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Rejecting correspondences with large distances. <a href="structsmall__gicp_1_1DistanceRejector.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1TerminationCriteria.html">TerminationCriteria</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> termination criteria. <a href="structsmall__gicp_1_1TerminationCriteria.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1NormalSetter.html">NormalSetter</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Computes point normals from eigenvectors and sets them to the point cloud. <a href="structsmall__gicp_1_1NormalSetter.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1CovarianceSetter.html">CovarianceSetter</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Computes point covariances from eigenvectors and sets them to the point cloud. <a href="structsmall__gicp_1_1CovarianceSetter.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1NormalCovarianceSetter.html">NormalCovarianceSetter</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Computes point normals and covariances from eigenvectors and sets them to the point cloud. <a href="structsmall__gicp_1_1NormalCovarianceSetter.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1RadixSortBuffers.html">RadixSortBuffers</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Temporal buffers for radix sort. <a href="structsmall__gicp_1_1RadixSortBuffers.html#details">More...</a><br /></td></tr>
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct  </td><td class="memItemRight" valign="bottom"><a class="el" href="structsmall__gicp_1_1XORVector3iHash.html">XORVector3iHash</a></td></tr>
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<tr class="memdesc:"><td class="mdescLeft"> </td><td class="mdescRight">Spatial hashing function. Teschner et al., "Optimized Spatial Hashing for Collision Detection of Deformable Objects", VMV2003. <a href="structsmall__gicp_1_1XORVector3iHash.html#details">More...</a><br /></td></tr>
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Typedefs</h2></td></tr>
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<tr class="memitem:a55b1d2331cfcdfeaeb494862cb2390ae"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a55b1d2331cfcdfeaeb494862cb2390ae">FlatContainerPoints</a> = <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a>< false, false ></td></tr>
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<tr class="memdesc:a55b1d2331cfcdfeaeb494862cb2390ae"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> that stores only points. <a href="namespacesmall__gicp.html#a55b1d2331cfcdfeaeb494862cb2390ae">More...</a><br /></td></tr>
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<tr class="memitem:aec427d082aeabc51dd991316f4e5b0ea"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#aec427d082aeabc51dd991316f4e5b0ea">FlatContainerNormal</a> = <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a>< true, false ></td></tr>
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<tr class="memdesc:aec427d082aeabc51dd991316f4e5b0ea"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> with normals. <a href="namespacesmall__gicp.html#aec427d082aeabc51dd991316f4e5b0ea">More...</a><br /></td></tr>
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<tr class="memitem:a073a085485230911389f63d46e5547dd"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a073a085485230911389f63d46e5547dd">FlatContainerCov</a> = <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a>< false, true ></td></tr>
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<tr class="memdesc:a073a085485230911389f63d46e5547dd"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> with covariances. <a href="namespacesmall__gicp.html#a073a085485230911389f63d46e5547dd">More...</a><br /></td></tr>
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<tr class="memitem:ac0ce4510ed4baa8bc08f120c4a46fd12"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#ac0ce4510ed4baa8bc08f120c4a46fd12">FlatContainerNormalCov</a> = <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a>< true, true ></td></tr>
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<tr class="memdesc:ac0ce4510ed4baa8bc08f120c4a46fd12"><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> with normals and covariances. <a href="namespacesmall__gicp.html#ac0ce4510ed4baa8bc08f120c4a46fd12">More...</a><br /></td></tr>
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<tr class="memitem:adaa11356e0b17e2958ad077dab99ea9c"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#adaa11356e0b17e2958ad077dab99ea9c">GaussianVoxelMap</a> = <a class="el" href="structsmall__gicp_1_1IncrementalVoxelMap.html">IncrementalVoxelMap</a>< <a class="el" href="structsmall__gicp_1_1GaussianVoxel.html">GaussianVoxel</a> ></td></tr>
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<tr class="memitem:a0fcc071f1f9cc9ea2f79068231c9053a"><td class="memItemLeft" align="right" valign="top">using </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a0fcc071f1f9cc9ea2f79068231c9053a">NodeIndexType</a> = std::uint32_t</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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Functions</h2></td></tr>
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<tr class="memitem:ae507f2dc845993bd13809aa91b2a48bf"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#ae507f2dc845993bd13809aa91b2a48bf">omp_get_thread_num</a> ()</td></tr>
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<tr class="memitem:a30d5bdb69c71e494dc85a8556faae960"><td class="memItemLeft" align="right" valign="top">std::pair< <a class="el" href="structsmall__gicp_1_1PointCloud.html#ae146e0c215d5e7d89a69cc8a1358f434">PointCloud::Ptr</a>, std::shared_ptr< <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a>< <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> > > > </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a30d5bdb69c71e494dc85a8556faae960">preprocess_points</a> (const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &points, double downsampling_resolution, int num_neighbors=10, int num_threads=4)</td></tr>
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<tr class="memdesc:a30d5bdb69c71e494dc85a8556faae960"><td class="mdescLeft"> </td><td class="mdescRight">Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation). <a href="namespacesmall__gicp.html#a30d5bdb69c71e494dc85a8556faae960">More...</a><br /></td></tr>
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<tr class="memitem:aae1dd13209cbd2d115f1a0f2e5561822"><td class="memTemplParams" colspan="2">template<typename T , int D> </td></tr>
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<tr class="memitem:aae1dd13209cbd2d115f1a0f2e5561822"><td class="memTemplItemLeft" align="right" valign="top">std::pair< <a class="el" href="structsmall__gicp_1_1PointCloud.html#ae146e0c215d5e7d89a69cc8a1358f434">PointCloud::Ptr</a>, std::shared_ptr< <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a>< <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> > > > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#aae1dd13209cbd2d115f1a0f2e5561822">preprocess_points</a> (const std::vector< Eigen::Matrix< T, D, 1 >> &points, double downsampling_resolution, int num_neighbors=10, int num_threads=4)</td></tr>
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<tr class="memdesc:aae1dd13209cbd2d115f1a0f2e5561822"><td class="mdescLeft"> </td><td class="mdescRight">Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation) <a href="namespacesmall__gicp.html#aae1dd13209cbd2d115f1a0f2e5561822">More...</a><br /></td></tr>
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<tr class="memitem:a99e4cfa9b94a792b53670478502cabfe"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsmall__gicp_1_1IncrementalVoxelMap.html#aa3359e10645a4a9418161590aa55fe0c">GaussianVoxelMap::Ptr</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a99e4cfa9b94a792b53670478502cabfe">create_gaussian_voxelmap</a> (const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &points, double voxel_resolution)</td></tr>
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<tr class="memdesc:a99e4cfa9b94a792b53670478502cabfe"><td class="mdescLeft"> </td><td class="mdescRight">Create an incremental Gaussian voxel map. <a href="namespacesmall__gicp.html#a99e4cfa9b94a792b53670478502cabfe">More...</a><br /></td></tr>
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<tr class="memitem:aaaac24cfd2f82c7978ccc8cb66474cf4"><td class="memTemplParams" colspan="2">template<typename T , int D> </td></tr>
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<tr class="memitem:aaaac24cfd2f82c7978ccc8cb66474cf4"><td class="memTemplItemLeft" align="right" valign="top"><a class="el" href="structsmall__gicp_1_1RegistrationResult.html">RegistrationResult</a> </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#aaaac24cfd2f82c7978ccc8cb66474cf4">align</a> (const std::vector< Eigen::Matrix< T, D, 1 >> &target, const std::vector< Eigen::Matrix< T, D, 1 >> &source, const Eigen::Isometry3d &init_T=Eigen::Isometry3d::Identity(), const <a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a> &setting=<a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a>())</td></tr>
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<tr class="memdesc:aaaac24cfd2f82c7978ccc8cb66474cf4"><td class="mdescLeft"> </td><td class="mdescRight">Align point clouds. <a href="namespacesmall__gicp.html#aaaac24cfd2f82c7978ccc8cb66474cf4">More...</a><br /></td></tr>
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<tr class="memitem:a4f2f329a192d4effb0c81e15f28b8000"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsmall__gicp_1_1RegistrationResult.html">RegistrationResult</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a4f2f329a192d4effb0c81e15f28b8000">align</a> (const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &target, const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &source, const <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a>< <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> > &target_tree, const Eigen::Isometry3d &init_T=Eigen::Isometry3d::Identity(), const <a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a> &setting=<a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a>())</td></tr>
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<tr class="memdesc:a4f2f329a192d4effb0c81e15f28b8000"><td class="mdescLeft"> </td><td class="mdescRight">Align preprocessed point clouds. <a href="namespacesmall__gicp.html#a4f2f329a192d4effb0c81e15f28b8000">More...</a><br /></td></tr>
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<tr class="memitem:a165555d9cdfd6138f598538fbb07f194"><td class="memItemLeft" align="right" valign="top"><a class="el" href="structsmall__gicp_1_1RegistrationResult.html">RegistrationResult</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a165555d9cdfd6138f598538fbb07f194">align</a> (const <a class="el" href="namespacesmall__gicp.html#adaa11356e0b17e2958ad077dab99ea9c">GaussianVoxelMap</a> &target, const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &source, const Eigen::Isometry3d &init_T=Eigen::Isometry3d::Identity(), const <a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a> &setting=<a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a>())</td></tr>
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<tr class="memdesc:a165555d9cdfd6138f598538fbb07f194"><td class="mdescLeft"> </td><td class="mdescRight">Align preprocessed point clouds with VGICP. <a href="namespacesmall__gicp.html#a165555d9cdfd6138f598538fbb07f194">More...</a><br /></td></tr>
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<tr class="memitem:a1f96f233873fdc58bc241a8d26b0b774"><td class="memTemplParams" colspan="2">template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud> </td></tr>
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<tr class="memitem:a1f96f233873fdc58bc241a8d26b0b774"><td class="memTemplItemLeft" align="right" valign="top">std::shared_ptr< OutputPointCloud > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a1f96f233873fdc58bc241a8d26b0b774">voxelgrid_sampling</a> (const InputPointCloud &points, double leaf_size)</td></tr>
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<tr class="memdesc:a1f96f233873fdc58bc241a8d26b0b774"><td class="mdescLeft"> </td><td class="mdescRight">Voxelgrid downsampling. This function computes exact average of points in each voxel, and each voxel can contain arbitrary number of points. <a href="namespacesmall__gicp.html#a1f96f233873fdc58bc241a8d26b0b774">More...</a><br /></td></tr>
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<tr class="memitem:a9aafc25321a0484261582ac5160770ae"><td class="memTemplParams" colspan="2">template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud, typename RNG = std::mt19937> </td></tr>
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<tr class="memitem:a9aafc25321a0484261582ac5160770ae"><td class="memTemplItemLeft" align="right" valign="top">std::shared_ptr< OutputPointCloud > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a9aafc25321a0484261582ac5160770ae">random_sampling</a> (const InputPointCloud &points, size_t num_samples, RNG &rng)</td></tr>
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<tr class="memdesc:a9aafc25321a0484261582ac5160770ae"><td class="mdescLeft"> </td><td class="mdescRight">Random downsampling. <a href="namespacesmall__gicp.html#a9aafc25321a0484261582ac5160770ae">More...</a><br /></td></tr>
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<tr class="separator:a9aafc25321a0484261582ac5160770ae"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:afedf95a15244b9cb2aa0e105b9a501ee"><td class="memTemplParams" colspan="2">template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud> </td></tr>
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<tr class="memitem:afedf95a15244b9cb2aa0e105b9a501ee"><td class="memTemplItemLeft" align="right" valign="top">std::shared_ptr< OutputPointCloud > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#afedf95a15244b9cb2aa0e105b9a501ee">voxelgrid_sampling_omp</a> (const InputPointCloud &points, double leaf_size, int num_threads=4)</td></tr>
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<tr class="memdesc:afedf95a15244b9cb2aa0e105b9a501ee"><td class="mdescLeft"> </td><td class="mdescRight">Voxel grid downsampling with OpenMP backend. <a href="namespacesmall__gicp.html#afedf95a15244b9cb2aa0e105b9a501ee">More...</a><br /></td></tr>
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<tr class="separator:afedf95a15244b9cb2aa0e105b9a501ee"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af99df9601fa3d294463327902f51669b"><td class="memTemplParams" colspan="2">template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud> </td></tr>
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<tr class="memitem:af99df9601fa3d294463327902f51669b"><td class="memTemplItemLeft" align="right" valign="top">std::shared_ptr< OutputPointCloud > </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#af99df9601fa3d294463327902f51669b">voxelgrid_sampling_tbb</a> (const InputPointCloud &points, double leaf_size)</td></tr>
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<tr class="memdesc:af99df9601fa3d294463327902f51669b"><td class="mdescLeft"> </td><td class="mdescRight">Voxel grid downsampling with TBB backend. <a href="namespacesmall__gicp.html#af99df9601fa3d294463327902f51669b">More...</a><br /></td></tr>
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<tr class="separator:af99df9601fa3d294463327902f51669b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aa4cea49ae3c02a77305e4efdcf21280a"><td class="memItemLeft" align="right" valign="top">Eigen::Array4i </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#aa4cea49ae3c02a77305e4efdcf21280a">fast_floor</a> (const Eigen::Array4d &pt)</td></tr>
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<tr class="memdesc:aa4cea49ae3c02a77305e4efdcf21280a"><td class="mdescLeft"> </td><td class="mdescRight">Fast floor (<a href="https://stackoverflow.com/questions/824118/why-is-floor-so-slow">https://stackoverflow.com/questions/824118/why-is-floor-so-slow</a>). <a href="namespacesmall__gicp.html#aa4cea49ae3c02a77305e4efdcf21280a">More...</a><br /></td></tr>
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<tr class="separator:aa4cea49ae3c02a77305e4efdcf21280a"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a5d86f81a31b152060f1b1f72585fdaf5"><td class="memItemLeft" align="right" valign="top">Eigen::Matrix3d </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a5d86f81a31b152060f1b1f72585fdaf5">skew</a> (const Eigen::Vector3d &x)</td></tr>
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<tr class="memdesc:a5d86f81a31b152060f1b1f72585fdaf5"><td class="mdescLeft"> </td><td class="mdescRight">Create skew symmetric matrix. <a href="namespacesmall__gicp.html#a5d86f81a31b152060f1b1f72585fdaf5">More...</a><br /></td></tr>
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<tr class="separator:a5d86f81a31b152060f1b1f72585fdaf5"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a5c5f249a06b1a660d2e519a9936da3f7"><td class="memItemLeft" align="right" valign="top">Eigen::Quaterniond </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a5c5f249a06b1a660d2e519a9936da3f7">so3_exp</a> (const Eigen::Vector3d &omega)</td></tr>
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<tr class="memdesc:a5c5f249a06b1a660d2e519a9936da3f7"><td class="mdescLeft"> </td><td class="mdescRight">SO3 expmap. <a href="namespacesmall__gicp.html#a5c5f249a06b1a660d2e519a9936da3f7">More...</a><br /></td></tr>
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<tr class="separator:a5c5f249a06b1a660d2e519a9936da3f7"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a0eae106c70deac5c20205c70c4bdaf3c"><td class="memItemLeft" align="right" valign="top">Eigen::Isometry3d </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a0eae106c70deac5c20205c70c4bdaf3c">se3_exp</a> (const Eigen::Matrix< double, 6, 1 > &a)</td></tr>
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<tr class="memdesc:a0eae106c70deac5c20205c70c4bdaf3c"><td class="mdescLeft"> </td><td class="mdescRight">SE3 expmap (Rotation-first). <a href="namespacesmall__gicp.html#a0eae106c70deac5c20205c70c4bdaf3c">More...</a><br /></td></tr>
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<tr class="separator:a0eae106c70deac5c20205c70c4bdaf3c"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a501687927eb1e20bc346907c925f9d7d"><td class="memTemplParams" colspan="2">template<typename Setter , typename PointCloud , typename Tree > </td></tr>
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<tr class="memitem:a501687927eb1e20bc346907c925f9d7d"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a501687927eb1e20bc346907c925f9d7d">estimate_local_features</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, Tree &kdtree, int num_neighbors, size_t point_index)</td></tr>
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<tr class="separator:a501687927eb1e20bc346907c925f9d7d"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a94b5c528b2407e7892765d500ac4bc68"><td class="memTemplParams" colspan="2">template<typename Setter , typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a94b5c528b2407e7892765d500ac4bc68"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a94b5c528b2407e7892765d500ac4bc68">estimate_local_features</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, const <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors)</td></tr>
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<tr class="separator:a94b5c528b2407e7892765d500ac4bc68"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a429f8aa891c416007816c83a09566600"><td class="memTemplParams" colspan="2">template<typename Setter , typename PointCloud > </td></tr>
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<tr class="memitem:a429f8aa891c416007816c83a09566600"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a429f8aa891c416007816c83a09566600">estimate_local_features</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors)</td></tr>
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<tr class="separator:a429f8aa891c416007816c83a09566600"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a0dbfb10fa4698c80cda854ceb6561e50"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:a0dbfb10fa4698c80cda854ceb6561e50"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a0dbfb10fa4698c80cda854ceb6561e50">estimate_normals</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a0dbfb10fa4698c80cda854ceb6561e50"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals. <a href="namespacesmall__gicp.html#a0dbfb10fa4698c80cda854ceb6561e50">More...</a><br /></td></tr>
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<tr class="separator:a0dbfb10fa4698c80cda854ceb6561e50"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af962c06fc787c664b3647b53ec05d3d4"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:af962c06fc787c664b3647b53ec05d3d4"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#af962c06fc787c664b3647b53ec05d3d4">estimate_normals</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20)</td></tr>
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<tr class="memdesc:af962c06fc787c664b3647b53ec05d3d4"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals. <a href="namespacesmall__gicp.html#af962c06fc787c664b3647b53ec05d3d4">More...</a><br /></td></tr>
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<tr class="separator:af962c06fc787c664b3647b53ec05d3d4"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a273f7ce3257fad200bcf76d2f2357d34"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:a273f7ce3257fad200bcf76d2f2357d34"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a273f7ce3257fad200bcf76d2f2357d34">estimate_covariances</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a273f7ce3257fad200bcf76d2f2357d34"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point covariances. <a href="namespacesmall__gicp.html#a273f7ce3257fad200bcf76d2f2357d34">More...</a><br /></td></tr>
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<tr class="separator:a273f7ce3257fad200bcf76d2f2357d34"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a45e5a9a3ca76f402d8203c9d0c9d341b"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a45e5a9a3ca76f402d8203c9d0c9d341b"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a45e5a9a3ca76f402d8203c9d0c9d341b">estimate_covariances</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a45e5a9a3ca76f402d8203c9d0c9d341b"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point covariances. <a href="namespacesmall__gicp.html#a45e5a9a3ca76f402d8203c9d0c9d341b">More...</a><br /></td></tr>
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<tr class="separator:a45e5a9a3ca76f402d8203c9d0c9d341b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a1be100924358411fa177c79973642b1b"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:a1be100924358411fa177c79973642b1b"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a1be100924358411fa177c79973642b1b">estimate_normals_covariances</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a1be100924358411fa177c79973642b1b"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals and covariances. <a href="namespacesmall__gicp.html#a1be100924358411fa177c79973642b1b">More...</a><br /></td></tr>
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<tr class="separator:a1be100924358411fa177c79973642b1b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a15382a59a6d46a77bf335387fb81477d"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a15382a59a6d46a77bf335387fb81477d"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a15382a59a6d46a77bf335387fb81477d">estimate_normals_covariances</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a15382a59a6d46a77bf335387fb81477d"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals and covariances. <a href="namespacesmall__gicp.html#a15382a59a6d46a77bf335387fb81477d">More...</a><br /></td></tr>
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<tr class="separator:a15382a59a6d46a77bf335387fb81477d"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a029e1e8224da1a270fb547a06155ad79"><td class="memTemplParams" colspan="2">template<typename Setter , typename PointCloud > </td></tr>
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<tr class="memitem:a029e1e8224da1a270fb547a06155ad79"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a029e1e8224da1a270fb547a06155ad79">estimate_local_features_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors, int num_threads)</td></tr>
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<tr class="separator:a029e1e8224da1a270fb547a06155ad79"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a38cc32a3601cb35feaeeca061ebf043c"><td class="memTemplParams" colspan="2">template<typename Setter , typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a38cc32a3601cb35feaeeca061ebf043c"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a38cc32a3601cb35feaeeca061ebf043c">estimate_local_features_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors, int num_threads)</td></tr>
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<tr class="separator:a38cc32a3601cb35feaeeca061ebf043c"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a27c3fd7738e24b14f18bd5d4723275a9"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:a27c3fd7738e24b14f18bd5d4723275a9"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a27c3fd7738e24b14f18bd5d4723275a9">estimate_normals_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20, int num_threads=4)</td></tr>
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<tr class="memdesc:a27c3fd7738e24b14f18bd5d4723275a9"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals with OpenMP. <a href="namespacesmall__gicp.html#a27c3fd7738e24b14f18bd5d4723275a9">More...</a><br /></td></tr>
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<tr class="separator:a27c3fd7738e24b14f18bd5d4723275a9"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aefbe0a08b752e2c4d187406e33926b16"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:aefbe0a08b752e2c4d187406e33926b16"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#aefbe0a08b752e2c4d187406e33926b16">estimate_normals_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20, int num_threads=4)</td></tr>
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<tr class="memdesc:aefbe0a08b752e2c4d187406e33926b16"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals with OpenMP. <a href="namespacesmall__gicp.html#aefbe0a08b752e2c4d187406e33926b16">More...</a><br /></td></tr>
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<tr class="separator:aefbe0a08b752e2c4d187406e33926b16"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:adf8333664b03248cbcb20a9f17346b72"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:adf8333664b03248cbcb20a9f17346b72"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#adf8333664b03248cbcb20a9f17346b72">estimate_covariances_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20, int num_threads=4)</td></tr>
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<tr class="memdesc:adf8333664b03248cbcb20a9f17346b72"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point covariances with OpenMP. <a href="namespacesmall__gicp.html#adf8333664b03248cbcb20a9f17346b72">More...</a><br /></td></tr>
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<tr class="separator:adf8333664b03248cbcb20a9f17346b72"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a961795840ac85cd4c2f8180af8a660c0"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a961795840ac85cd4c2f8180af8a660c0"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a961795840ac85cd4c2f8180af8a660c0">estimate_covariances_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20, int num_threads=4)</td></tr>
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<tr class="memdesc:a961795840ac85cd4c2f8180af8a660c0"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point covariances with OpenMP. <a href="namespacesmall__gicp.html#a961795840ac85cd4c2f8180af8a660c0">More...</a><br /></td></tr>
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<tr class="separator:a961795840ac85cd4c2f8180af8a660c0"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a9532a70de332d497b8fc9a0f75abdbf7"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:a9532a70de332d497b8fc9a0f75abdbf7"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a9532a70de332d497b8fc9a0f75abdbf7">estimate_normals_covariances_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20, int num_threads=4)</td></tr>
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<tr class="memdesc:a9532a70de332d497b8fc9a0f75abdbf7"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals and covariances with OpenMP. <a href="namespacesmall__gicp.html#a9532a70de332d497b8fc9a0f75abdbf7">More...</a><br /></td></tr>
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<tr class="separator:a9532a70de332d497b8fc9a0f75abdbf7"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a31a6fc72889826267f8db3c39b76d5a5"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a31a6fc72889826267f8db3c39b76d5a5"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a31a6fc72889826267f8db3c39b76d5a5">estimate_normals_covariances_omp</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20, int num_threads=4)</td></tr>
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<tr class="memdesc:a31a6fc72889826267f8db3c39b76d5a5"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals and covariances with OpenMP. <a href="namespacesmall__gicp.html#a31a6fc72889826267f8db3c39b76d5a5">More...</a><br /></td></tr>
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<tr class="separator:a31a6fc72889826267f8db3c39b76d5a5"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a40bbe12c440d6f144e15e73128418237"><td class="memTemplParams" colspan="2">template<typename Setter , typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a40bbe12c440d6f144e15e73128418237"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a40bbe12c440d6f144e15e73128418237">estimate_local_features_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors)</td></tr>
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<tr class="separator:a40bbe12c440d6f144e15e73128418237"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:accf40c75aa6326ba3ba8c85b733bba6d"><td class="memTemplParams" colspan="2">template<typename Setter , typename PointCloud > </td></tr>
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<tr class="memitem:accf40c75aa6326ba3ba8c85b733bba6d"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#accf40c75aa6326ba3ba8c85b733bba6d">estimate_local_features_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors)</td></tr>
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<tr class="memitem:ac2eac24e628e90858eaedc86e01322c0"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:ac2eac24e628e90858eaedc86e01322c0"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#ac2eac24e628e90858eaedc86e01322c0">estimate_normals_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20)</td></tr>
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<tr class="memdesc:ac2eac24e628e90858eaedc86e01322c0"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals with TBB. <a href="namespacesmall__gicp.html#ac2eac24e628e90858eaedc86e01322c0">More...</a><br /></td></tr>
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<tr class="memitem:a769f42f0a105216f40d028a19934ad51"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a769f42f0a105216f40d028a19934ad51"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a769f42f0a105216f40d028a19934ad51">estimate_normals_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a769f42f0a105216f40d028a19934ad51"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals with TBB. <a href="namespacesmall__gicp.html#a769f42f0a105216f40d028a19934ad51">More...</a><br /></td></tr>
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<tr class="separator:a769f42f0a105216f40d028a19934ad51"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a2984ef6c86de9c245e68717de21177c1"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:a2984ef6c86de9c245e68717de21177c1"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a2984ef6c86de9c245e68717de21177c1">estimate_covariances_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a2984ef6c86de9c245e68717de21177c1"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point covariances with TBB. <a href="namespacesmall__gicp.html#a2984ef6c86de9c245e68717de21177c1">More...</a><br /></td></tr>
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<tr class="separator:a2984ef6c86de9c245e68717de21177c1"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a90803b9a6f4abcc1d1ce4e55d14745ca"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a90803b9a6f4abcc1d1ce4e55d14745ca"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a90803b9a6f4abcc1d1ce4e55d14745ca">estimate_covariances_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a90803b9a6f4abcc1d1ce4e55d14745ca"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point covariances with TBB. <a href="namespacesmall__gicp.html#a90803b9a6f4abcc1d1ce4e55d14745ca">More...</a><br /></td></tr>
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<tr class="separator:a90803b9a6f4abcc1d1ce4e55d14745ca"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab420344af77cce8d3345b1d36dd6ed12"><td class="memTemplParams" colspan="2">template<typename PointCloud > </td></tr>
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<tr class="memitem:ab420344af77cce8d3345b1d36dd6ed12"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#ab420344af77cce8d3345b1d36dd6ed12">estimate_normals_covariances_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, int num_neighbors=20)</td></tr>
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<tr class="memdesc:ab420344af77cce8d3345b1d36dd6ed12"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals and covariances with TBB. <a href="namespacesmall__gicp.html#ab420344af77cce8d3345b1d36dd6ed12">More...</a><br /></td></tr>
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<tr class="separator:ab420344af77cce8d3345b1d36dd6ed12"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a7a3c4ec69d8f168e7581134bd417cbc9"><td class="memTemplParams" colspan="2">template<typename PointCloud , typename KdTree > </td></tr>
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<tr class="memitem:a7a3c4ec69d8f168e7581134bd417cbc9"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a7a3c4ec69d8f168e7581134bd417cbc9">estimate_normals_covariances_tbb</a> (<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> &cloud, <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> &kdtree, int num_neighbors=20)</td></tr>
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<tr class="memdesc:a7a3c4ec69d8f168e7581134bd417cbc9"><td class="mdescLeft"> </td><td class="mdescRight">Estimate point normals and covariances with TBB. <a href="namespacesmall__gicp.html#a7a3c4ec69d8f168e7581134bd417cbc9">More...</a><br /></td></tr>
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<tr class="separator:a7a3c4ec69d8f168e7581134bd417cbc9"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a32dc0ab954d5744d37183747a2356476"><td class="memTemplParams" colspan="2">template<typename RandomAccessIterator , typename Compare > </td></tr>
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<tr class="memitem:a32dc0ab954d5744d37183747a2356476"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a32dc0ab954d5744d37183747a2356476">merge_sort_omp_impl</a> (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp)</td></tr>
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<tr class="memdesc:a32dc0ab954d5744d37183747a2356476"><td class="mdescLeft"> </td><td class="mdescRight">Implementation of merge sort with OpenMP parallelism. Do not call this directly. Use merge_sort_omp instead. <a href="namespacesmall__gicp.html#a32dc0ab954d5744d37183747a2356476">More...</a><br /></td></tr>
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<tr class="separator:a32dc0ab954d5744d37183747a2356476"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aceb1d8349e6c67a22d37c7bfda7e6d3b"><td class="memTemplParams" colspan="2">template<typename RandomAccessIterator , typename Compare > </td></tr>
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<tr class="memitem:aceb1d8349e6c67a22d37c7bfda7e6d3b"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#aceb1d8349e6c67a22d37c7bfda7e6d3b">merge_sort_omp</a> (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp, int num_threads)</td></tr>
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<tr class="memdesc:aceb1d8349e6c67a22d37c7bfda7e6d3b"><td class="mdescLeft"> </td><td class="mdescRight">Merge sort with OpenMP parallelism. <a href="namespacesmall__gicp.html#aceb1d8349e6c67a22d37c7bfda7e6d3b">More...</a><br /></td></tr>
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<tr class="separator:aceb1d8349e6c67a22d37c7bfda7e6d3b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a5d3e11647aed6d5f464a3daf45586a1b"><td class="memTemplParams" colspan="2">template<typename RandomAccessIterator , typename Compare > </td></tr>
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<tr class="memitem:a5d3e11647aed6d5f464a3daf45586a1b"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a5d3e11647aed6d5f464a3daf45586a1b">quick_sort_omp_impl</a> (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp)</td></tr>
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<tr class="memdesc:a5d3e11647aed6d5f464a3daf45586a1b"><td class="mdescLeft"> </td><td class="mdescRight">Implementation of quick sort with OpenMP parallelism. Do not call this directly. Use quick_sort_omp instead. <a href="namespacesmall__gicp.html#a5d3e11647aed6d5f464a3daf45586a1b">More...</a><br /></td></tr>
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<tr class="separator:a5d3e11647aed6d5f464a3daf45586a1b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a96f8d8685abd1a79630e0ca7b34bd50f"><td class="memTemplParams" colspan="2">template<typename RandomAccessIterator , typename Compare > </td></tr>
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<tr class="memitem:a96f8d8685abd1a79630e0ca7b34bd50f"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a96f8d8685abd1a79630e0ca7b34bd50f">quick_sort_omp</a> (RandomAccessIterator first, RandomAccessIterator last, const Compare &comp, int num_threads)</td></tr>
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<tr class="memdesc:a96f8d8685abd1a79630e0ca7b34bd50f"><td class="mdescLeft"> </td><td class="mdescRight">Quick sort with OpenMP parallelism. <a href="namespacesmall__gicp.html#a96f8d8685abd1a79630e0ca7b34bd50f">More...</a><br /></td></tr>
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<tr class="separator:a96f8d8685abd1a79630e0ca7b34bd50f"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a32f0523ba72ba84906680696221f61b9"><td class="memTemplParams" colspan="2">template<typename T , typename KeyFunc , int bits = 8, int tile_size = 256> </td></tr>
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<tr class="memitem:a32f0523ba72ba84906680696221f61b9"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#a32f0523ba72ba84906680696221f61b9">radix_sort_tbb</a> (T *first_, T *last_, const KeyFunc &key_, <a class="el" href="structsmall__gicp_1_1RadixSortBuffers.html">RadixSortBuffers</a>< T > &buffers)</td></tr>
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<tr class="memdesc:a32f0523ba72ba84906680696221f61b9"><td class="mdescLeft"> </td><td class="mdescRight">Radix sort with TBB parallelization. <a href="namespacesmall__gicp.html#a32f0523ba72ba84906680696221f61b9">More...</a><br /></td></tr>
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<tr class="memitem:adede4866e7e22d9f0494e3d3c00de1a3"><td class="memTemplParams" colspan="2">template<typename T , typename KeyFunc , int bits = 4, int tile_size = 256> </td></tr>
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<tr class="memitem:adede4866e7e22d9f0494e3d3c00de1a3"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacesmall__gicp.html#adede4866e7e22d9f0494e3d3c00de1a3">radix_sort_tbb</a> (T *first_, T *last_, const KeyFunc &key_)</td></tr>
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<tr class="memdesc:adede4866e7e22d9f0494e3d3c00de1a3"><td class="mdescLeft"> </td><td class="mdescRight">Radix sort with TBB parallelization. <a href="namespacesmall__gicp.html#adede4866e7e22d9f0494e3d3c00de1a3">More...</a><br /></td></tr>
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<h2 class="groupheader">Typedef Documentation</h2>
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<a id="a073a085485230911389f63d46e5547dd"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a073a085485230911389f63d46e5547dd">◆ </a></span>FlatContainerCov</h2>
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<td class="memname">using <a class="el" href="namespacesmall__gicp.html#a073a085485230911389f63d46e5547dd">small_gicp::FlatContainerCov</a> = typedef <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a><false, true></td>
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<p><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> with covariances. </p>
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<a id="aec427d082aeabc51dd991316f4e5b0ea"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#aec427d082aeabc51dd991316f4e5b0ea">◆ </a></span>FlatContainerNormal</h2>
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<td class="memname">using <a class="el" href="namespacesmall__gicp.html#aec427d082aeabc51dd991316f4e5b0ea">small_gicp::FlatContainerNormal</a> = typedef <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a><true, false></td>
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<p><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> with normals. </p>
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<a id="ac0ce4510ed4baa8bc08f120c4a46fd12"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#ac0ce4510ed4baa8bc08f120c4a46fd12">◆ </a></span>FlatContainerNormalCov</h2>
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<td class="memname">using <a class="el" href="namespacesmall__gicp.html#ac0ce4510ed4baa8bc08f120c4a46fd12">small_gicp::FlatContainerNormalCov</a> = typedef <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a><true, true></td>
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<p><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> with normals and covariances. </p>
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<a id="a55b1d2331cfcdfeaeb494862cb2390ae"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a55b1d2331cfcdfeaeb494862cb2390ae">◆ </a></span>FlatContainerPoints</h2>
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<td class="memname">using <a class="el" href="namespacesmall__gicp.html#a55b1d2331cfcdfeaeb494862cb2390ae">small_gicp::FlatContainerPoints</a> = typedef <a class="el" href="structsmall__gicp_1_1FlatContainer.html">FlatContainer</a><false, false></td>
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<p><a class="el" href="structsmall__gicp_1_1FlatContainer.html" title="Point container with a flat vector.">FlatContainer</a> that stores only points. </p>
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<h2 class="memtitle"><span class="permalink"><a href="#adaa11356e0b17e2958ad077dab99ea9c">◆ </a></span>GaussianVoxelMap</h2>
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<td class="memname">using <a class="el" href="namespacesmall__gicp.html#adaa11356e0b17e2958ad077dab99ea9c">small_gicp::GaussianVoxelMap</a> = typedef <a class="el" href="structsmall__gicp_1_1IncrementalVoxelMap.html">IncrementalVoxelMap</a><<a class="el" href="structsmall__gicp_1_1GaussianVoxel.html">GaussianVoxel</a>></td>
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<h2 class="memtitle"><span class="permalink"><a href="#a0fcc071f1f9cc9ea2f79068231c9053a">◆ </a></span>NodeIndexType</h2>
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<td class="memname">using <a class="el" href="namespacesmall__gicp.html#a0fcc071f1f9cc9ea2f79068231c9053a">small_gicp::NodeIndexType</a> = typedef std::uint32_t</td>
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<h2 class="groupheader">Function Documentation</h2>
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<a id="a165555d9cdfd6138f598538fbb07f194"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a165555d9cdfd6138f598538fbb07f194">◆ </a></span>align() <span class="overload">[1/3]</span></h2>
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<td class="memname"><a class="el" href="structsmall__gicp_1_1RegistrationResult.html">RegistrationResult</a> small_gicp::align </td>
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<td>(</td>
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<td class="paramtype">const <a class="el" href="namespacesmall__gicp.html#adaa11356e0b17e2958ad077dab99ea9c">GaussianVoxelMap</a> & </td>
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<td class="paramname"><em>target</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>source</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const Eigen::Isometry3d & </td>
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<td class="paramname"><em>init_T</em> = <code>Eigen::Isometry3d::Identity()</code>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a> & </td>
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<td class="paramname"><em>setting</em> = <code><a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a>()</code> </td>
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<td></td>
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<p>Align preprocessed point clouds with VGICP. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">target</td><td>Target Gaussian voxelmap </td></tr>
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<tr><td class="paramname">source</td><td>Source point cloud </td></tr>
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<tr><td class="paramname">init_T</td><td>Initial guess of T_target_source </td></tr>
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<tr><td class="paramname">setting</td><td><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> setting </td></tr>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> result </dd></dl>
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<a id="a4f2f329a192d4effb0c81e15f28b8000"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a4f2f329a192d4effb0c81e15f28b8000">◆ </a></span>align() <span class="overload">[2/3]</span></h2>
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<td class="memname"><a class="el" href="structsmall__gicp_1_1RegistrationResult.html">RegistrationResult</a> small_gicp::align </td>
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<td>(</td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>target</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>source</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a>< <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> > & </td>
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<td class="paramname"><em>target_tree</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const Eigen::Isometry3d & </td>
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<td class="paramname"><em>init_T</em> = <code>Eigen::Isometry3d::Identity()</code>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a> & </td>
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<td class="paramname"><em>setting</em> = <code><a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a>()</code> </td>
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<td></td>
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<td>)</td>
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<p>Align preprocessed point clouds. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">target</td><td>Target point cloud </td></tr>
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<tr><td class="paramname">source</td><td>Source point cloud </td></tr>
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<tr><td class="paramname">target_tree</td><td>Nearest neighbor search for the target point cloud </td></tr>
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<tr><td class="paramname">init_T</td><td>Initial guess of T_target_source </td></tr>
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<tr><td class="paramname">setting</td><td><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> setting </td></tr>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> result </dd></dl>
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<a id="aaaac24cfd2f82c7978ccc8cb66474cf4"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#aaaac24cfd2f82c7978ccc8cb66474cf4">◆ </a></span>align() <span class="overload">[3/3]</span></h2>
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<td class="memname"><a class="el" href="structsmall__gicp_1_1RegistrationResult.html">RegistrationResult</a> small_gicp::align </td>
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<td>(</td>
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<td class="paramtype">const std::vector< Eigen::Matrix< T, D, 1 >> & </td>
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<td class="paramname"><em>target</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const std::vector< Eigen::Matrix< T, D, 1 >> & </td>
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<td class="paramname"><em>source</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const Eigen::Isometry3d & </td>
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<td class="paramname"><em>init_T</em> = <code>Eigen::Isometry3d::Identity()</code>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a> & </td>
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<td class="paramname"><em>setting</em> = <code><a class="el" href="structsmall__gicp_1_1RegistrationSetting.html">RegistrationSetting</a>()</code> </td>
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<tr>
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<td></td>
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<td>)</td>
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<p>Align point clouds. </p>
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<dl class="section note"><dt>Note</dt><dd>This function only accepts Eigen::Vector(3|4)(f|d) as input </dd></dl>
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<dl class="section see"><dt>See also</dt><dd><a class="el" href="namespacesmall__gicp.html#afedf95a15244b9cb2aa0e105b9a501ee" title="Voxel grid downsampling with OpenMP backend.">small_gicp::voxelgrid_sampling_omp</a>, <a class="el" href="namespacesmall__gicp.html#a9532a70de332d497b8fc9a0f75abdbf7" title="Estimate point normals and covariances with OpenMP.">small_gicp::estimate_normals_covariances_omp</a> </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">target</td><td>Target points </td></tr>
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<tr><td class="paramname">source</td><td>Source points </td></tr>
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<tr><td class="paramname">init_T</td><td>Initial guess of T_target_source </td></tr>
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<tr><td class="paramname">setting</td><td><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> setting </td></tr>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd><a class="el" href="structsmall__gicp_1_1Registration.html" title="Point cloud registration.">Registration</a> result </dd></dl>
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<h2 class="memtitle"><span class="permalink"><a href="#a99e4cfa9b94a792b53670478502cabfe">◆ </a></span>create_gaussian_voxelmap()</h2>
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<td class="memname"><a class="el" href="structsmall__gicp_1_1IncrementalVoxelMap.html#aa3359e10645a4a9418161590aa55fe0c">GaussianVoxelMap::Ptr</a> small_gicp::create_gaussian_voxelmap </td>
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<td>(</td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>points</em>, </td>
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<td class="paramtype">double </td>
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<td class="paramname"><em>voxel_resolution</em> </td>
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<td></td>
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<td>)</td>
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<p>Create an incremental Gaussian voxel map. </p>
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<dl class="section see"><dt>See also</dt><dd><a class="el" href="structsmall__gicp_1_1IncrementalVoxelMap.html" title="Incremental voxelmap. This class supports incremental point cloud insertion and LRU-based voxel delet...">small_gicp::IncrementalVoxelMap</a> </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">points</td><td>Input points </td></tr>
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<tr><td class="paramname">voxel_resolution</td><td>Voxel resolution </td></tr>
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</dd>
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<h2 class="memtitle"><span class="permalink"><a href="#a273f7ce3257fad200bcf76d2f2357d34">◆ </a></span>estimate_covariances() <span class="overload">[1/2]</span></h2>
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template<typename PointCloud > </div>
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<td class="memname">void small_gicp::estimate_covariances </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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<tr>
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<td></td>
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<td>)</td>
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<p>Estimate point covariances. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</dd>
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<a id="a45e5a9a3ca76f402d8203c9d0c9d341b"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a45e5a9a3ca76f402d8203c9d0c9d341b">◆ </a></span>estimate_covariances() <span class="overload">[2/2]</span></h2>
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template<typename PointCloud , typename KdTree > </div>
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<td class="memname">void small_gicp::estimate_covariances </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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<td></td>
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<td>)</td>
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<p>Estimate point covariances. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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<h2 class="memtitle"><span class="permalink"><a href="#adf8333664b03248cbcb20a9f17346b72">◆ </a></span>estimate_covariances_omp() <span class="overload">[1/2]</span></h2>
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template<typename PointCloud > </div>
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<td class="memname">void small_gicp::estimate_covariances_omp </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code>, </td>
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<td class="paramtype">int </td>
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<td></td>
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<p>Estimate point covariances with OpenMP. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
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</table>
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</dd>
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<a id="a961795840ac85cd4c2f8180af8a660c0"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a961795840ac85cd4c2f8180af8a660c0">◆ </a></span>estimate_covariances_omp() <span class="overload">[2/2]</span></h2>
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<td class="memname">void small_gicp::estimate_covariances_omp </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
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<td></td>
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<td>)</td>
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<p>Estimate point covariances with OpenMP. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
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</table>
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</dd>
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</dl>
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<a id="a2984ef6c86de9c245e68717de21177c1"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a2984ef6c86de9c245e68717de21177c1">◆ </a></span>estimate_covariances_tbb() <span class="overload">[1/2]</span></h2>
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<div class="memitem">
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template<typename PointCloud > </div>
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<td class="memname">void small_gicp::estimate_covariances_tbb </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</div><div class="memdoc">
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<p>Estimate point covariances with TBB. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a90803b9a6f4abcc1d1ce4e55d14745ca"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a90803b9a6f4abcc1d1ce4e55d14745ca">◆ </a></span>estimate_covariances_tbb() <span class="overload">[2/2]</span></h2>
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<div class="memitem">
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template<typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_covariances_tbb </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point covariances with TBB. </p>
|
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a94b5c528b2407e7892765d500ac4bc68"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a94b5c528b2407e7892765d500ac4bc68">◆ </a></span>estimate_local_features() <span class="overload">[1/3]</span></h2>
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template<typename Setter , typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_local_features </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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</div>
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<a id="a429f8aa891c416007816c83a09566600"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a429f8aa891c416007816c83a09566600">◆ </a></span>estimate_local_features() <span class="overload">[2/3]</span></h2>
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<div class="memtemplate">
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template<typename Setter , typename PointCloud > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_local_features </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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</div>
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<a id="a501687927eb1e20bc346907c925f9d7d"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a501687927eb1e20bc346907c925f9d7d">◆ </a></span>estimate_local_features() <span class="overload">[3/3]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename Setter , typename PointCloud , typename Tree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_local_features </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">Tree & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">size_t </td>
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<td class="paramname"><em>point_index</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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<a id="a029e1e8224da1a270fb547a06155ad79"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a029e1e8224da1a270fb547a06155ad79">◆ </a></span>estimate_local_features_omp() <span class="overload">[1/2]</span></h2>
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<div class="memitem">
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template<typename Setter , typename PointCloud > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_local_features_omp </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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</div>
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<a id="a38cc32a3601cb35feaeeca061ebf043c"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a38cc32a3601cb35feaeeca061ebf043c">◆ </a></span>estimate_local_features_omp() <span class="overload">[2/2]</span></h2>
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<div class="memitem">
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template<typename Setter , typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_local_features_omp </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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</div>
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<a id="accf40c75aa6326ba3ba8c85b733bba6d"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#accf40c75aa6326ba3ba8c85b733bba6d">◆ </a></span>estimate_local_features_tbb() <span class="overload">[1/2]</span></h2>
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template<typename Setter , typename PointCloud > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_local_features_tbb </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<a id="a40bbe12c440d6f144e15e73128418237"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a40bbe12c440d6f144e15e73128418237">◆ </a></span>estimate_local_features_tbb() <span class="overload">[2/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename Setter , typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_local_features_tbb </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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<a id="a0dbfb10fa4698c80cda854ceb6561e50"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a0dbfb10fa4698c80cda854ceb6561e50">◆ </a></span>estimate_normals() <span class="overload">[1/2]</span></h2>
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template<typename PointCloud > </div>
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<td class="memname">void small_gicp::estimate_normals </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</div><div class="memdoc">
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<p>Estimate point normals. </p>
|
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
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<table class="params">
|
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="af962c06fc787c664b3647b53ec05d3d4"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#af962c06fc787c664b3647b53ec05d3d4">◆ </a></span>estimate_normals() <span class="overload">[2/2]</span></h2>
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template<typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</div><div class="memdoc">
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<p>Estimate point normals. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a1be100924358411fa177c79973642b1b"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a1be100924358411fa177c79973642b1b">◆ </a></span>estimate_normals_covariances() <span class="overload">[1/2]</span></h2>
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<div class="memitem">
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template<typename PointCloud > </div>
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<tr>
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<td class="memname">void small_gicp::estimate_normals_covariances </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals and covariances. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a15382a59a6d46a77bf335387fb81477d"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a15382a59a6d46a77bf335387fb81477d">◆ </a></span>estimate_normals_covariances() <span class="overload">[2/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals_covariances </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals and covariances. </p>
|
|
<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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|
<table class="params">
|
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
|
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<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
|
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a9532a70de332d497b8fc9a0f75abdbf7"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a9532a70de332d497b8fc9a0f75abdbf7">◆ </a></span>estimate_normals_covariances_omp() <span class="overload">[1/2]</span></h2>
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<div class="memitem">
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<div class="memtemplate">
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template<typename PointCloud > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals_covariances_omp </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals and covariances with OpenMP. </p>
|
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
|
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
|
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a31a6fc72889826267f8db3c39b76d5a5"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a31a6fc72889826267f8db3c39b76d5a5">◆ </a></span>estimate_normals_covariances_omp() <span class="overload">[2/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals_covariances_omp </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
|
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<td class="paramname"><em>cloud</em>, </td>
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</tr>
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<tr>
|
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<td class="paramkey"></td>
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<td></td>
|
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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</tr>
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<tr>
|
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<td class="paramkey"></td>
|
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code>, </td>
|
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</tr>
|
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<tr>
|
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<td class="paramkey"></td>
|
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<td></td>
|
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<td class="paramtype">int </td>
|
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals and covariances with OpenMP. </p>
|
|
<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
|
<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
|
|
<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
|
|
<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
|
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="ab420344af77cce8d3345b1d36dd6ed12"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#ab420344af77cce8d3345b1d36dd6ed12">◆ </a></span>estimate_normals_covariances_tbb() <span class="overload">[1/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename PointCloud > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals_covariances_tbb </td>
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<td>(</td>
|
|
<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
|
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<td class="paramname"><em>cloud</em>, </td>
|
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</tr>
|
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<tr>
|
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<td class="paramkey"></td>
|
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<td></td>
|
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<td class="paramtype">int </td>
|
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
|
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</tr>
|
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<tr>
|
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals and covariances with TBB. </p>
|
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
|
<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
|
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a7a3c4ec69d8f168e7581134bd417cbc9"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a7a3c4ec69d8f168e7581134bd417cbc9">◆ </a></span>estimate_normals_covariances_tbb() <span class="overload">[2/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals_covariances_tbb </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
|
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<td class="paramname"><em>cloud</em>, </td>
|
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</tr>
|
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<tr>
|
|
<td class="paramkey"></td>
|
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<td></td>
|
|
<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
|
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<td class="paramname"><em>kdtree</em>, </td>
|
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</tr>
|
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<tr>
|
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<td class="paramkey"></td>
|
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals and covariances with TBB. </p>
|
|
<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
|
<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
|
|
<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
|
|
<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
|
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</table>
|
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</dd>
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</dl>
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</div>
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</div>
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<a id="a27c3fd7738e24b14f18bd5d4723275a9"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a27c3fd7738e24b14f18bd5d4723275a9">◆ </a></span>estimate_normals_omp() <span class="overload">[1/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename PointCloud > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals_omp </td>
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|
<td>(</td>
|
|
<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
|
|
<td class="paramname"><em>cloud</em>, </td>
|
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</tr>
|
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<tr>
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<td class="paramkey"></td>
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<td></td>
|
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
|
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
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</tr>
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<tr>
|
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals with OpenMP. </p>
|
|
<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
|
<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
|
|
<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
|
|
<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
|
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="aefbe0a08b752e2c4d187406e33926b16"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#aefbe0a08b752e2c4d187406e33926b16">◆ </a></span>estimate_normals_omp() <span class="overload">[2/2]</span></h2>
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<div class="memitem">
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<div class="memproto">
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<div class="memtemplate">
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template<typename PointCloud , typename KdTree > </div>
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<table class="memname">
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<tr>
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<td class="memname">void small_gicp::estimate_normals_omp </td>
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|
<td>(</td>
|
|
<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
|
|
<td class="paramname"><em>cloud</em>, </td>
|
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</tr>
|
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<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
|
|
<td class="paramname"><em>kdtree</em>, </td>
|
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</tr>
|
|
<tr>
|
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<td class="paramkey"></td>
|
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<td></td>
|
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<td class="paramtype">int </td>
|
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<td class="paramname"><em>num_neighbors</em> = <code>20</code>, </td>
|
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</tr>
|
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<tr>
|
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<td class="paramkey"></td>
|
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<td></td>
|
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<td class="paramtype">int </td>
|
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
|
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</tr>
|
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<tr>
|
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<td></td>
|
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Estimate point normals with OpenMP. </p>
|
|
<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
|
<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
|
|
<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
|
|
<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
|
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
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</table>
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</dd>
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<h2 class="memtitle"><span class="permalink"><a href="#ac2eac24e628e90858eaedc86e01322c0">◆ </a></span>estimate_normals_tbb() <span class="overload">[1/2]</span></h2>
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<td class="memname">void small_gicp::estimate_normals_tbb </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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<p>Estimate point normals with TBB. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</dd>
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<h2 class="memtitle"><span class="permalink"><a href="#a769f42f0a105216f40d028a19934ad51">◆ </a></span>estimate_normals_tbb() <span class="overload">[2/2]</span></h2>
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template<typename PointCloud , typename KdTree > </div>
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<td class="memname">void small_gicp::estimate_normals_tbb </td>
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<td>(</td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>cloud</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a> & </td>
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<td class="paramname"><em>kdtree</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>20</code> </td>
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<p>Estimate point normals with TBB. </p>
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<dl class="section note"><dt>Note</dt><dd>If a sufficient number of neighbor points for normal/covariance estimation (5 points) is not found, an invalid normal/covariance is set to the point (normal=zero vector, covariance=identity matrix). </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">cloud</td><td>[in/out] Point cloud </td></tr>
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<tr><td class="paramname">kdtree</td><td>Nearest neighbor search </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors used for attribute estimation </td></tr>
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</table>
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</dd>
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</dl>
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<h2 class="memtitle"><span class="permalink"><a href="#aa4cea49ae3c02a77305e4efdcf21280a">◆ </a></span>fast_floor()</h2>
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<td class="memname">Eigen::Array4i small_gicp::fast_floor </td>
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<td>(</td>
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<td class="paramtype">const Eigen::Array4d & </td>
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<td class="paramname"><em>pt</em></td><td>)</td>
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<p>Fast floor (<a href="https://stackoverflow.com/questions/824118/why-is-floor-so-slow">https://stackoverflow.com/questions/824118/why-is-floor-so-slow</a>). </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">pt</td><td>Double vector </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>Floored int vector </dd></dl>
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<a id="aceb1d8349e6c67a22d37c7bfda7e6d3b"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#aceb1d8349e6c67a22d37c7bfda7e6d3b">◆ </a></span>merge_sort_omp()</h2>
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template<typename RandomAccessIterator , typename Compare > </div>
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<td class="memname">void small_gicp::merge_sort_omp </td>
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<td>(</td>
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<td class="paramtype">RandomAccessIterator </td>
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<td class="paramname"><em>first</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">RandomAccessIterator </td>
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<td class="paramname"><em>last</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">const Compare & </td>
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<td class="paramname"><em>comp</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> </td>
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<td></td>
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<td>)</td>
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<p>Merge sort with OpenMP parallelism. </p>
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<dl class="section note"><dt>Note</dt><dd>This tends to be slower than quick_sort_omp. </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">first</td><td>First iterator </td></tr>
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<tr><td class="paramname">last</td><td>Last iterator </td></tr>
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<tr><td class="paramname">comp</td><td>Comparison function </td></tr>
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
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</dd>
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</dl>
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<a id="a32dc0ab954d5744d37183747a2356476"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a32dc0ab954d5744d37183747a2356476">◆ </a></span>merge_sort_omp_impl()</h2>
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template<typename RandomAccessIterator , typename Compare > </div>
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<td class="memname">void small_gicp::merge_sort_omp_impl </td>
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<td>(</td>
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<td class="paramtype">RandomAccessIterator </td>
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<td class="paramname"><em>first</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">RandomAccessIterator </td>
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<td class="paramname"><em>last</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const Compare & </td>
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<td class="paramname"><em>comp</em> </td>
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<tr>
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<td></td>
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<td>)</td>
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<p>Implementation of merge sort with OpenMP parallelism. Do not call this directly. Use merge_sort_omp instead. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">first</td><td>First iterator </td></tr>
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<tr><td class="paramname">last</td><td>Last iterator </td></tr>
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<tr><td class="paramname">comp</td><td>Comparison function </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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<a id="ae507f2dc845993bd13809aa91b2a48bf"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#ae507f2dc845993bd13809aa91b2a48bf">◆ </a></span>omp_get_thread_num()</h2>
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<td class="memname">int small_gicp::omp_get_thread_num </td>
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<td>(</td>
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<td class="paramname"></td><td>)</td>
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<h2 class="memtitle"><span class="permalink"><a href="#a30d5bdb69c71e494dc85a8556faae960">◆ </a></span>preprocess_points() <span class="overload">[1/2]</span></h2>
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<td class="memname">std::pair<<a class="el" href="structsmall__gicp_1_1PointCloud.html#ae146e0c215d5e7d89a69cc8a1358f434">PointCloud::Ptr</a>, std::shared_ptr<<a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a><<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a>> > > small_gicp::preprocess_points </td>
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<td>(</td>
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<td class="paramtype">const <a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a> & </td>
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<td class="paramname"><em>points</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">double </td>
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<td class="paramname"><em>downsampling_resolution</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>10</code>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
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<td></td>
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<td>)</td>
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<p>Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation). </p>
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<dl class="section note"><dt>Note</dt><dd>When num_threads >= 2, this function has minor run-by-run non-determinism due to the parallel downsampling. </dd></dl>
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<dl class="section see"><dt>See also</dt><dd><a class="el" href="namespacesmall__gicp.html#afedf95a15244b9cb2aa0e105b9a501ee" title="Voxel grid downsampling with OpenMP backend.">small_gicp::voxelgrid_sampling_omp</a>, <a class="el" href="namespacesmall__gicp.html#a9532a70de332d497b8fc9a0f75abdbf7" title="Estimate point normals and covariances with OpenMP.">small_gicp::estimate_normals_covariances_omp</a> </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">points</td><td>Input points </td></tr>
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<tr><td class="paramname">downsampling_resolution</td><td>Downsample resolution </td></tr>
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<tr><td class="paramname">num_neighbors</td><td>Number of neighbors for normal/covariance estimation </td></tr>
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
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</table>
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</dd>
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</dl>
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<a id="aae1dd13209cbd2d115f1a0f2e5561822"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#aae1dd13209cbd2d115f1a0f2e5561822">◆ </a></span>preprocess_points() <span class="overload">[2/2]</span></h2>
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template<typename T , int D> </div>
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<td class="memname">std::pair<<a class="el" href="structsmall__gicp_1_1PointCloud.html#ae146e0c215d5e7d89a69cc8a1358f434">PointCloud::Ptr</a>, std::shared_ptr<<a class="el" href="structsmall__gicp_1_1KdTree.html">KdTree</a><<a class="el" href="structsmall__gicp_1_1PointCloud.html">PointCloud</a>> > > small_gicp::preprocess_points </td>
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<td>(</td>
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<td class="paramtype">const std::vector< Eigen::Matrix< T, D, 1 >> & </td>
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<td class="paramname"><em>points</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">double </td>
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<td class="paramname"><em>downsampling_resolution</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_neighbors</em> = <code>10</code>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</div><div class="memdoc">
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<p>Preprocess point cloud (downsampling, kdtree creation, and normal and covariance estimation) </p>
|
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<dl class="section note"><dt>Note</dt><dd>This function only accepts Eigen::Vector(3|4)(f|d) as input </dd>
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<dd>
|
|
When num_threads >= 2, this function has minor run-by-run non-determinism due to the parallel downsampling. </dd></dl>
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<dl class="section see"><dt>See also</dt><dd><a class="el" href="namespacesmall__gicp.html#afedf95a15244b9cb2aa0e105b9a501ee" title="Voxel grid downsampling with OpenMP backend.">small_gicp::voxelgrid_sampling_omp</a>, <a class="el" href="namespacesmall__gicp.html#a9532a70de332d497b8fc9a0f75abdbf7" title="Estimate point normals and covariances with OpenMP.">small_gicp::estimate_normals_covariances_omp</a> </dd></dl>
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<h2 class="memtitle"><span class="permalink"><a href="#a96f8d8685abd1a79630e0ca7b34bd50f">◆ </a></span>quick_sort_omp()</h2>
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<td class="memname">void small_gicp::quick_sort_omp </td>
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<td>(</td>
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<td class="paramtype">RandomAccessIterator </td>
|
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<td class="paramname"><em>first</em>, </td>
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</tr>
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<tr>
|
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<td class="paramkey"></td>
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<td></td>
|
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<td class="paramtype">RandomAccessIterator </td>
|
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<td class="paramname"><em>last</em>, </td>
|
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const Compare & </td>
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<td class="paramname"><em>comp</em>, </td>
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<td class="paramkey"></td>
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<td></td>
|
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<td class="paramtype">int </td>
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<td class="paramname"><em>num_threads</em> </td>
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</tr>
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<td></td>
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<td>)</td>
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<p>Quick sort with OpenMP parallelism. </p>
|
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<dl class="params"><dt>Parameters</dt><dd>
|
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<table class="params">
|
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<tr><td class="paramname">first</td><td>First iterator </td></tr>
|
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<tr><td class="paramname">last</td><td>Last iterator </td></tr>
|
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<tr><td class="paramname">comp</td><td>Comparison function </td></tr>
|
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<tr><td class="paramname">num_threads</td><td>Number of threads </td></tr>
|
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</table>
|
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</dd>
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</dl>
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<h2 class="memtitle"><span class="permalink"><a href="#a5d3e11647aed6d5f464a3daf45586a1b">◆ </a></span>quick_sort_omp_impl()</h2>
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template<typename RandomAccessIterator , typename Compare > </div>
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<td class="memname">void small_gicp::quick_sort_omp_impl </td>
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<td>(</td>
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<td class="paramtype">RandomAccessIterator </td>
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<td class="paramname"><em>first</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">RandomAccessIterator </td>
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<td class="paramname"><em>last</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const Compare & </td>
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<td class="paramname"><em>comp</em> </td>
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<td></td>
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<td>)</td>
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<p>Implementation of quick sort with OpenMP parallelism. Do not call this directly. Use quick_sort_omp instead. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">first</td><td>First iterator </td></tr>
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<tr><td class="paramname">last</td><td>Last iterator </td></tr>
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<tr><td class="paramname">comp</td><td>Comparison function </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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<a id="adede4866e7e22d9f0494e3d3c00de1a3"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#adede4866e7e22d9f0494e3d3c00de1a3">◆ </a></span>radix_sort_tbb() <span class="overload">[1/2]</span></h2>
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template<typename T , typename KeyFunc , int bits = 4, int tile_size = 256> </div>
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<td class="memname">void small_gicp::radix_sort_tbb </td>
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<td>(</td>
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<td class="paramtype">T * </td>
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<td class="paramname"><em>first_</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">T * </td>
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<td class="paramname"><em>last_</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">const KeyFunc & </td>
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<td class="paramname"><em>key_</em> </td>
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<p>Radix sort with TBB parallelization. </p>
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<dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>Data type (must be unsigned integral type) </td></tr>
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<tr><td class="paramname">KeyFunc</td><td>Key function </td></tr>
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<tr><td class="paramname">bits</td><td>Number of bits per step </td></tr>
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<tr><td class="paramname">tile_size</td><td>Tile size </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">first_</td><td>[in/out] First iterator </td></tr>
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<tr><td class="paramname">last_</td><td>[in/out] Last iterator </td></tr>
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<tr><td class="paramname">key_</td><td>Key function (T => uint) </td></tr>
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</table>
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</dd>
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</dl>
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<a id="a32f0523ba72ba84906680696221f61b9"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a32f0523ba72ba84906680696221f61b9">◆ </a></span>radix_sort_tbb() <span class="overload">[2/2]</span></h2>
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template<typename T , typename KeyFunc , int bits = 8, int tile_size = 256> </div>
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<td class="memname">void small_gicp::radix_sort_tbb </td>
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<td>(</td>
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<td class="paramtype">T * </td>
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<td class="paramname"><em>first_</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">T * </td>
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<td class="paramname"><em>last_</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">const KeyFunc & </td>
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<td class="paramname"><em>key_</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"><a class="el" href="structsmall__gicp_1_1RadixSortBuffers.html">RadixSortBuffers</a>< T > & </td>
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<td class="paramname"><em>buffers</em> </td>
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<td></td>
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<td>)</td>
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<p>Radix sort with TBB parallelization. </p>
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<dl class="section note"><dt>Note</dt><dd>This function outperforms tbb::parallel_sort only in case with many elements and threads. For usual data size and number of threads, use tbb::parallel_sort. </dd></dl>
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<dl class="tparams"><dt>Template Parameters</dt><dd>
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<table class="tparams">
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<tr><td class="paramname">T</td><td>Data type (must be unsigned integral type) </td></tr>
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<tr><td class="paramname">KeyFunc</td><td>Key function </td></tr>
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<tr><td class="paramname">bits</td><td>Number of bits per step </td></tr>
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<tr><td class="paramname">tile_size</td><td>Tile size </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">first_</td><td>[in/out] First iterator </td></tr>
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<tr><td class="paramname">last_</td><td>[in/out] Last iterator </td></tr>
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<tr><td class="paramname">key_</td><td>Key function (T => uint) </td></tr>
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<tr><td class="paramname">buffers</td><td>Temporal buffers </td></tr>
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</table>
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</dd>
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</dl>
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</div>
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</div>
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<a id="a9aafc25321a0484261582ac5160770ae"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a9aafc25321a0484261582ac5160770ae">◆ </a></span>random_sampling()</h2>
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template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud, typename RNG = std::mt19937> </div>
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<table class="memname">
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<td class="memname">std::shared_ptr<OutputPointCloud> small_gicp::random_sampling </td>
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<td>(</td>
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<td class="paramtype">const InputPointCloud & </td>
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<td class="paramname"><em>points</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">size_t </td>
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<td class="paramname"><em>num_samples</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">RNG & </td>
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<td class="paramname"><em>rng</em> </td>
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<td></td>
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</div><div class="memdoc">
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<p>Random downsampling. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">points</td><td>Input points </td></tr>
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<tr><td class="paramname">num_samples</td><td>Number of samples to be drawn </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>Downsampled points </dd></dl>
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</div>
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</div>
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<a id="a0eae106c70deac5c20205c70c4bdaf3c"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a0eae106c70deac5c20205c70c4bdaf3c">◆ </a></span>se3_exp()</h2>
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<td class="memname">Eigen::Isometry3d small_gicp::se3_exp </td>
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<td>(</td>
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<td class="paramtype">const Eigen::Matrix< double, 6, 1 > & </td>
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<td class="paramname"><em>a</em></td><td>)</td>
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<td></td>
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</div><div class="memdoc">
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<p>SE3 expmap (Rotation-first). </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">a</td><td>Twist vector [rx, ry, rz, tx, ty, tz] </td></tr>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>SE3 matrix </dd></dl>
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<p>Note: That is an accurate expansion!</p>
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</div>
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<a id="a5d86f81a31b152060f1b1f72585fdaf5"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a5d86f81a31b152060f1b1f72585fdaf5">◆ </a></span>skew()</h2>
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<td class="memname">Eigen::Matrix3d small_gicp::skew </td>
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<td>(</td>
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<td class="paramtype">const Eigen::Vector3d & </td>
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<td class="paramname"><em>x</em></td><td>)</td>
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<p>Create skew symmetric matrix. </p>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
|
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<tr><td class="paramname">x</td><td>Rotation vector </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>Skew symmetric matrix </dd></dl>
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<a id="a5c5f249a06b1a660d2e519a9936da3f7"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a5c5f249a06b1a660d2e519a9936da3f7">◆ </a></span>so3_exp()</h2>
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<td class="memname">Eigen::Quaterniond small_gicp::so3_exp </td>
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<td>(</td>
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<td class="paramtype">const Eigen::Vector3d & </td>
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<td class="paramname"><em>omega</em></td><td>)</td>
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<td></td>
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<span class="mlabels"><span class="mlabel">inline</span></span> </td>
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<p>SO3 expmap. </p>
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<dl class="params"><dt>Parameters</dt><dd>
|
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<table class="params">
|
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<tr><td class="paramname">omega</td><td>[rx, ry, rz] </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>Quaternion </dd></dl>
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</div>
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<a id="a1f96f233873fdc58bc241a8d26b0b774"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a1f96f233873fdc58bc241a8d26b0b774">◆ </a></span>voxelgrid_sampling()</h2>
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template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud> </div>
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<td class="memname">std::shared_ptr<OutputPointCloud> small_gicp::voxelgrid_sampling </td>
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<td>(</td>
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<td class="paramtype">const InputPointCloud & </td>
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<td class="paramname"><em>points</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype">double </td>
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<td class="paramname"><em>leaf_size</em> </td>
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<td></td>
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<td>)</td>
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</div><div class="memdoc">
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<p>Voxelgrid downsampling. This function computes exact average of points in each voxel, and each voxel can contain arbitrary number of points. </p>
|
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<dl class="section note"><dt>Note</dt><dd>Discretized voxel coords must be in 21bit range [-1048576, 1048575]. For example, if the downsampling resolution is 0.01 m, point coordinates must be in [-10485.76, 10485.75] m. Points outside the valid range will be ignored. </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
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<tr><td class="paramname">points</td><td>Input points </td></tr>
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|
<tr><td class="paramname">leaf_size</td><td>Downsampling resolution </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>Downsampled points </dd></dl>
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</div>
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</div>
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<a id="afedf95a15244b9cb2aa0e105b9a501ee"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#afedf95a15244b9cb2aa0e105b9a501ee">◆ </a></span>voxelgrid_sampling_omp()</h2>
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template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud> </div>
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<td class="memname">std::shared_ptr<OutputPointCloud> small_gicp::voxelgrid_sampling_omp </td>
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<td>(</td>
|
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<td class="paramtype">const InputPointCloud & </td>
|
|
<td class="paramname"><em>points</em>, </td>
|
|
</tr>
|
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<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
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<td class="paramtype">double </td>
|
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<td class="paramname"><em>leaf_size</em>, </td>
|
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</tr>
|
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<tr>
|
|
<td class="paramkey"></td>
|
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<td></td>
|
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<td class="paramtype">int </td>
|
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<td class="paramname"><em>num_threads</em> = <code>4</code> </td>
|
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</tr>
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<tr>
|
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</div><div class="memdoc">
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<p>Voxel grid downsampling with OpenMP backend. </p>
|
|
<dl class="section note"><dt>Note</dt><dd>This function has minor run-by-run non-deterministic behavior due to parallel data collection that results in a deviation of the number of points in the downsampling results (up to 10% increase from the single-thread version). </dd>
|
|
<dd>
|
|
Discretized voxel coords must be in 21bit range [-1048576, 1048575]. For example, if the downsampling resolution is 0.01 m, point coordinates must be in [-10485.76, 10485.75] m. Points outside the valid range will be ignored. </dd></dl>
|
|
<dl class="params"><dt>Parameters</dt><dd>
|
|
<table class="params">
|
|
<tr><td class="paramname">points</td><td>Input points </td></tr>
|
|
<tr><td class="paramname">leaf_size</td><td>Downsampling resolution </td></tr>
|
|
</table>
|
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</dd>
|
|
</dl>
|
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<dl class="section return"><dt>Returns</dt><dd>Downsampled points </dd></dl>
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</div>
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</div>
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<a id="af99df9601fa3d294463327902f51669b"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#af99df9601fa3d294463327902f51669b">◆ </a></span>voxelgrid_sampling_tbb()</h2>
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template<typename InputPointCloud , typename OutputPointCloud = InputPointCloud> </div>
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<td class="memname">std::shared_ptr<OutputPointCloud> small_gicp::voxelgrid_sampling_tbb </td>
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<td>(</td>
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<td class="paramtype">const InputPointCloud & </td>
|
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<td class="paramname"><em>points</em>, </td>
|
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</tr>
|
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<tr>
|
|
<td class="paramkey"></td>
|
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<td></td>
|
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<td class="paramtype">double </td>
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<td class="paramname"><em>leaf_size</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<p>Voxel grid downsampling with TBB backend. </p>
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<dl class="section note"><dt>Note</dt><dd>This function has minor run-by-run non-deterministic behavior due to parallel data collection that results in a deviation of the number of points in the downsampling results (up to 10% increase from the single-thread version). </dd>
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<dd>
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Discretized voxel coords must be in 21bit range [-1048576, 1048575]. For example, if the downsampling resolution is 0.01 m, point coordinates must be in [-10485.76, 10485.75] m. Points outside the valid range will be ignored. </dd></dl>
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<dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">points</td><td>Input points </td></tr>
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<tr><td class="paramname">leaf_size</td><td>Downsampling resolution </td></tr>
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</table>
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</dd>
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</dl>
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<dl class="section return"><dt>Returns</dt><dd>Downsampled points </dd></dl>
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</div>
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</div>
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