Referenced by pcl::FastBilateralFilterOMP< PointT >::applyFilter(), pcl::filters::Pyramid< PointT >::compute(), pcl::occlusion_reasoning::filter(), pcl::occlusion_reasoning::getOccludedCloud(), and pcl::PointCloudDepthAndRGBtoXYZRGBA(). @johnathon Where did I mention std:: anything? Follow the link. Definition at line 534 of file point_cloud.h. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. PCLConfig.cmake uses a CMake special feature named EXPORT which Contribute to PointCloudLibrary/pcl development by creating an account on GitHub. PCL How to create a Point Cloud array/vector? Definition at line 411 of file point_cloud.h. 10,641 sourceClouds.push_back(sourceCloud); This line only copy the PointCloud::Ptr and does not copy the point cloud data. Each occupied voxel generates exactly one point by averaging all points inside. Very understandable @jonathon, thanks for both your input, they are both equally correct answers, who am i supposed to give the tick to? button. If we are trying to concatenate points then the code below: cloud_c = cloud_a; cloud_c += cloud_b; creates cloud_c by concatenating the points of cloud_a and cloud_b together. What is this fallacy: Perfection is impossible, therefore imperfection should be overlooked. CMake has a list of default searchable paths where it seeks for The Point Cloud Library (PCL) is an open-source library of algorithms for point cloud processing tasks and 3D geometry processing, such as occur in three-dimensional computer vision.The library contains algorithms for filtering, feature estimation, surface reconstruction, 3D registration, model fitting, object recognition, and segmentation.Each module is implemented as a smaller library that . CMake will take care of the suffix (.exe on A standalone, large scale, open project for 2D/3D image processing. SOLIDWORKS tech tip: Importing Point Cloud data into SOLIDWORKS, Javelin Technologies Inc. | A TriMech Company, Install and Use Point Cloud Libray in Linux for Beginners, Create a 3D Model from a Point Cloud in Global Mapper, ICP & Point Cloud Registration - Part 1: Known Data Association & SVD (Cyrill Stachniss, 2021), Plot 3D points using Point Cloud Library (PCL), Flutter AnimationController / Tween Reuse In Multiple AnimatedBuilder. Referenced by pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >::readRange(), pcl::MinCutSegmentation< PointT >::setBackgroundPoints(), and pcl::MinCutSegmentation< PointT >::setForegroundPoints(). The algorithm operates in two steps: Points are bucketed into voxels. Definition at line 420 of file point_cloud.h. CMakeLists.txt that contains: This is mandatory for cmake, and since we are making a very basic one needs to use include_directories() macro. pcl makes pointers to clouds like this: This results in a pretty obvious error ie. PCL is released under the terms of the BSD license, and thus free for commercial and research use. target_link_libraries() macro. Only works on organized datasets (those that have height != 1). Where does the idea of selling dragon parts come from? Connect and share knowledge within a single location that is structured and easy to search. The Point Cloud Library (PCL) is a standalone, large scale, open project for 2D/3D image and point cloud processing. These types should be enough to support all the algorithms and methods implemented in PCL. Copy the cloud to the heap and return a smart pointer Note that deep copy is performed, so avoid using this function on non-empty clouds. In the first for loop, the PointCloudSize from both Clouuds are the same, but in the second for loop, the PointCloudSize is 0. It differs from the above function only in what argument(s) it accepts. Otherwise if we are attempting to concatenate fields . Pages generated on Sun Dec 11 2022 02:57:55, pcl::PointCloud< PointT > Class Template Reference. Major direction: number of points in cloud, Minor direction: number of point dimensions By default, as of, If the current size is greater then the requested size, the pointcloud is reduced to its first requested elements, If the current size is less then the requested size, additional default-inserted points are appended, If the current size is greater than the requested size, the pointcloud is reduced to its first requested elements. Emplace a new point in the cloud, at the end of the container. Referenced by pcl::visualization::PCLVisualizer::addPointCloudNormals(), pcl::applyMorphologicalOperator(), pcl::compute3DCentroid(), pcl::computeCovarianceMatrix(), pcl::computeNDCentroid(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::computeRFAndShapeDistribution(), pcl::copyPointCloud(), pcl::common::CloudGenerator< pcl::PointXY, GeneratorT >::fill(), pcl::common::CloudGenerator< PointT, GeneratorT >::fill(), pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT >::segmentAndRefine(), pcl::SupervoxelClustering< PointT >::setInputCloud(), pcl::PCDWriter::writeASCII(), pcl::PCDWriter::writeBinary(), and pcl::PCDWriter::writeBinaryCompressed(). Definition at line 430 of file point_cloud.h. Are the S&P 500 and Dow Jones Industrial Average securities? If the button is not available, please click the hamburger/3-dot button next to Edit to enable the pipeline. The executable we are building makes calls to PCL functions. Definition at line 431 of file point_cloud.h. machine. Ready to optimize your JavaScript with Rust? The program will work correctly, but if you didn't need the extra copy, it is far from optimal. I would like to know if it is possible to take this point cloud, and make a pointer to a copy of it. Referenced by pcl::GridMinimum< PointT >::applyFilter(), pcl::LocalMaximum< PointT >::applyFilter(), pcl::ProjectInliers< PointT >::applyFilter(), pcl::UniformSampling< PointT >::applyFilter(), pcl::VoxelGrid< PointT >::applyFilter(), pcl::VoxelGridCovariance< PointT >::applyFilter(), pcl::approximatePolygon2D(), pcl::DisparityMapConverter< PointT >::compute(), pcl::Feature< PointInT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::compute(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::computeFeature(), pcl::GRSDEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::IntensitySpinEstimation< PointInT, PointOutT >::computeFeature(), pcl::RIFTEstimation< PointInT, GradientT, PointOutT >::computeFeature(), pcl::RSDEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::computeRFAndShapeDistribution(), pcl::io::OrganizedConversion< PointT, false >::convert(), pcl::io::OrganizedConversion< PointT, true >::convert(), pcl::TSDFVolume< VoxelT, WeightT >::convertToTsdfCloud(), pcl::copyPointCloud(), pcl::gpu::extractEuclideanClusters(), pcl::VoxelGridCovariance< PointT >::getDisplayCloud(), pcl::MarchingCubes< PointNT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), pcl::CloudSurfaceProcessing< PointInT, PointOutT >::process(), pcl::BilateralUpsampling< PointInT, PointOutT >::process(), pcl::MovingLeastSquares< PointInT, PointOutT >::process(), pcl::SurfaceReconstruction< PointInT >::reconstruct(), pcl::ConcaveHull< PointInT >::reconstruct(), pcl::ConvexHull< PointInT >::reconstruct(), pcl::SegmentDifferences< PointT >::segment(), and pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >::setSearchSurface(). Something can be done or not a fit? Definition at line 225 of file point_cloud.h. Let us say the project is placed under /PATH/TO/MY/GRAND/PROJECT that a multitude of Geometry and Color handler for pcl::PointCloud<T> datasets; a pcl::RangeImage visualization module. Definition at line 422 of file point_cloud.h. Does integrating PDOS give total charge of a system? Pointcloud's Surnia platform provides high-density point clouds as high as 640x480 points per frame, industry-leading sub-millimeter depth accuracy that is independent of distance to target, immunity against direct sunlight and extreme lighting conditions, and high dynamic range. We are a young startup in Vietnam who wants to bring autonomous mobile robots that make practical sense to warehousing, logistics, and agriculture. Definition at line 502 of file point_cloud.h. Should I give a brutally honest feedback on course evaluations? Thanks for contributing an answer to Stack Overflow! Definition at line 536 of file point_cloud.h. done. Each point in the data set is represented by an x, y, and z geometric coordinate. Not the answer you're looking for? What I'am doing wrong? Can a prospective pilot be negated their certification because of too big/small hands? Definition at line 428 of file point_cloud.h. Not the answer you're looking for? When you are using such targets they are called imported If there are no errors, the project files will be generated into the Where to build the binaries PCL How to create a Point Cloud array/vector? rev2022.12.9.43105. Referenced by pcl::Registration< PointSource, PointTarget, Scalar >::align(), pcl::ApproximateVoxelGrid< PointT >::applyFilter(), pcl::ConditionalRemoval< PointT >::applyFilter(), pcl::ShadowPoints< PointT, NormalT >::applyFilter(), pcl::UniformSampling< PointT >::applyFilter(), pcl::VoxelGrid< PointT >::applyFilter(), pcl::approximatePolygon(), pcl::Edge< PointInT, PointOutT >::canny(), pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >::cleanUp(), pcl::OrganizedEdgeBase< PointT, PointLT >::compute(), pcl::OrganizedEdgeFromRGB< PointT, PointLT >::compute(), pcl::OrganizedEdgeFromNormals< PointT, PointNT, PointLT >::compute(), pcl::OrganizedEdgeFromRGBNormals< PointT, PointNT, PointLT >::compute(), pcl::DisparityMapConverter< PointT >::compute(), pcl::Feature< PointInT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::compute(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::BRISK2DEstimation< PointInT, PointOutT, KeypointT, IntensityT >::compute(), pcl::features::computeApproximateNormals(), pcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature >::computeFeature(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::computeFeature(), pcl::GRSDEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::computeRFAndShapeDistribution(), pcl::LineRGBD< PointXYZT, PointRGBT >::computeTransformedTemplatePoints(), pcl::concatenateFields(), pcl::filters::Convolution3D< PointIn, PointOut, KernelT >::convolve(), pcl::GaussianKernel::convolveCols(), pcl::GaussianKernel::convolveRows(), pcl::copyPointCloud(), pcl::demeanPointCloud(), pcl::Edge< PointInT, PointOutT >::detectEdgeCanny(), pcl::Edge< PointInT, PointOutT >::detectEdgePrewitt(), pcl::Edge< PointInT, PointOutT >::detectEdgeSobel(), pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >::detectKeypoints(), pcl::Morphology< PointT >::dilationBinary(), pcl::Morphology< PointT >::dilationGray(), pcl::Morphology< PointT >::erosionBinary(), pcl::Morphology< PointT >::erosionGray(), pcl::OrganizedEdgeFromRGB< PointT, PointLT >::extractEdges(), pcl::OrganizedEdgeFromNormals< PointT, PointNT, PointLT >::extractEdges(), pcl::common::CloudGenerator< pcl::PointXY, GeneratorT >::fill(), pcl::common::CloudGenerator< PointT, GeneratorT >::fill(), pcl::fromPCLPointCloud2(), pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getFitness(), pcl::filters::Convolution< PointIn, PointOut >::initCompute(), pcl::Morphology< PointT >::intersectionBinary(), pcl::isPointIn2DPolygon(), pcl::SupervoxelClustering< PointT >::makeSupervoxelNormalCloud(), pcl::search::Search< PointT >::nearestKSearchT(), pcl::BilateralUpsampling< PointInT, PointOutT >::performProcessing(), pcl::GridProjection< PointNT >::performReconstruction(), pcl::Poisson< PointNT >::performReconstruction(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), pcl::ConvexHull< PointInT >::performReconstruction3D(), pcl::ColorGradientModality< PointInT >::processInputData(), pcl::SampleConsensusModelCircle2D< PointT >::projectPoints(), pcl::SampleConsensusModelCircle3D< PointT >::projectPoints(), pcl::SampleConsensusModelCone< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelCylinder< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelEllipse3D< PointT >::projectPoints(), pcl::SampleConsensusModelLine< PointT >::projectPoints(), pcl::SampleConsensusModelPlane< PointT >::projectPoints(), pcl::SampleConsensusModelSphere< PointT >::projectPoints(), pcl::SampleConsensusModelStick< PointT >::projectPoints(), pcl::search::Search< PointT >::radiusSearchT(), pcl::io::LZFDepth16ImageReader::read(), pcl::io::LZFRGB24ImageReader::read(), pcl::io::LZFYUV422ImageReader::read(), pcl::io::LZFBayer8ImageReader::read(), pcl::io::LZFDepth16ImageReader::readOMP(), pcl::io::LZFRGB24ImageReader::readOMP(), pcl::io::LZFYUV422ImageReader::readOMP(), pcl::io::LZFBayer8ImageReader::readOMP(), pcl::removeNaNFromPointCloud(), pcl::removeNaNNormalsFromPointCloud(), pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >::segment(), pcl::ExtractPolygonalPrismData< PointT >::segment(), pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT >::segment(), pcl::OrganizedMultiPlaneSegmentation< pcl::PointXYZRGBA, pcl::Normal, pcl::Label >::segmentAndRefine(), pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT >::segmentAndRefine(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::setPointsToTrack(), pcl::Edge< PointInT, PointOutT >::sobelMagnitudeDirection(), pcl::Morphology< PointT >::subtractionBinary(), pcl::transformPointCloud(), pcl::transformPointCloudWithNormals(), pcl::Morphology< PointT >::unionBinary(), pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >::validateTransformation(), pcl::io::vtkPolyDataToPointCloud(), and pcl::io::vtkStructuredGridToPointCloud(). For example, to create a point cloud that holds 4 random XYZ data points, use: For example, to create a point cloud that holds 4 random XYZ data points, use: The PointCloud class contains the following elements: Definition at line 172 of file point_cloud.h. Referenced by pcl::Registration< PointSource, PointTarget, Scalar >::align(), pcl::ConditionalRemoval< PointT >::applyFilter(), pcl::Feature< PointInT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::compute(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::features::computeApproximateNormals(), pcl::PointCloud< PointT >::concatenate(), pcl::concatenateFields(), pcl::copyPointCloud(), pcl::demeanPointCloud(), pcl::SmoothedSurfacesKeypoint< PointT, PointNT >::detectKeypoints(), pcl::MultiscaleFeaturePersistence< PointSource, PointFeature >::determinePersistentFeatures(), pcl::extractEuclideanClusters(), pcl::gpu::extractEuclideanClusters(), pcl::extractLabeledEuclideanClusters(), pcl::gpu::extractLabeledEuclideanClusters(), pcl::Filter< PointT >::filter(), pcl::fromPCLPointCloud2(), pcl::operator<<(), pcl::BilateralUpsampling< PointInT, PointOutT >::performProcessing(), pcl::GridProjection< PointNT >::performReconstruction(), pcl::CloudSurfaceProcessing< PointInT, PointOutT >::process(), pcl::BilateralUpsampling< PointInT, PointOutT >::process(), pcl::MovingLeastSquares< PointInT, PointOutT >::process(), pcl::SampleConsensusModelCircle2D< PointT >::projectPoints(), pcl::SampleConsensusModelCircle3D< PointT >::projectPoints(), pcl::SampleConsensusModelCone< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelCylinder< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelEllipse3D< PointT >::projectPoints(), pcl::SampleConsensusModelLine< PointT >::projectPoints(), pcl::SampleConsensusModelPlane< PointT >::projectPoints(), pcl::SampleConsensusModelSphere< PointT >::projectPoints(), pcl::SampleConsensusModelStick< PointT >::projectPoints(), pcl::SurfaceReconstruction< PointInT >::reconstruct(), pcl::ConcaveHull< PointInT >::reconstruct(), pcl::ConvexHull< PointInT >::reconstruct(), pcl::removeNaNFromPointCloud(), pcl::removeNaNNormalsFromPointCloud(), pcl::SegmentDifferences< PointT >::segment(), pcl::PointCloud< PointT >::swap(), pcl::toPCLPointCloud2(), pcl::transformPointCloud(), and pcl::transformPointCloudWithNormals(). Definition at line 333 of file point_cloud.h. In his answer, however, the object is actually a local variable meaning that it might go out of scope while there are still references to it and that shared_ptr will eventually call delete on it, which is undefined behavior. [3]: The system can be configured to provide both 3D point . Referenced by pcl::VoxelGridCovariance< PointT >::applyFilter(), pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >::getEdgeIndex(), pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >::getFaceIndex(), pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >::getHalfEdgeIndex(), and pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >::getVertexIndex(). This line names your project and sets some useful cmake variables Referenced by pcl::visualization::ImageViewer::addMask(), pcl::visualization::PCLVisualizer::addPointCloudNormals(), pcl::visualization::ImageViewer::addRectangle(), pcl::visualization::ImageViewer::addRGBImage(), pcl::Registration< PointSource, PointTarget, Scalar >::align(), pcl::ApproximateVoxelGrid< PointT >::applyFilter(), pcl::ConditionalRemoval< PointT >::applyFilter(), pcl::GridMinimum< PointT >::applyFilter(), pcl::LocalMaximum< PointT >::applyFilter(), pcl::MedianFilter< PointT >::applyFilter(), pcl::ProjectInliers< PointT >::applyFilter(), pcl::SamplingSurfaceNormal< PointT >::applyFilter(), pcl::ShadowPoints< PointT, NormalT >::applyFilter(), pcl::UniformSampling< PointT >::applyFilter(), pcl::VoxelGrid< PointT >::applyFilter(), pcl::VoxelGridCovariance< PointT >::applyFilter(), pcl::LineRGBD< PointXYZT, PointRGBT >::applyProjectiveDepthICPOnDetections(), pcl::Edge< PointInT, PointOutT >::canny(), pcl::OrganizedEdgeBase< PointT, PointLT >::compute(), pcl::OrganizedEdgeFromRGB< PointT, PointLT >::compute(), pcl::OrganizedEdgeFromNormals< PointT, PointNT, PointLT >::compute(), pcl::OrganizedEdgeFromRGBNormals< PointT, PointNT, PointLT >::compute(), pcl::DisparityMapConverter< PointT >::compute(), pcl::Feature< PointInT, PointOutT >::compute(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::compute(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::VFHEstimation< PointInT, PointNT, PointOutT >::compute(), pcl::BRISK2DEstimation< PointInT, PointOutT, KeypointT, IntensityT >::compute(), pcl::filters::Pyramid< PointT >::compute(), pcl::features::computeApproximateNormals(), pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >::computeFeature(), pcl::GRSDEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::IntensitySpinEstimation< PointInT, PointOutT >::computeFeature(), pcl::RIFTEstimation< PointInT, GradientT, PointOutT >::computeFeature(), pcl::RSDEstimation< PointInT, PointNT, PointOutT >::computeFeature(), pcl::ColorGradientModality< PointInT >::computeMaxColorGradients(), pcl::ColorGradientModality< PointInT >::computeMaxColorGradientsSobel(), pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >::computeRFAndShapeDistribution(), pcl::LineRGBD< PointXYZT, PointRGBT >::computeTransformedTemplatePoints(), pcl::PointCloud< PointT >::concatenate(), pcl::concatenateFields(), pcl::io::OrganizedConversion< PointT, false >::convert(), pcl::io::OrganizedConversion< PointT, true >::convert(), pcl::UnaryClassifier< PointT >::convertCloud(), pcl::gpu::kinfuLS::StandaloneMarchingCubes< PointT >::convertTrianglesToMesh(), pcl::GaussianKernel::convolve(), pcl::filters::Convolution3D< PointIn, PointOut, KernelT >::convolve(), pcl::GaussianKernel::convolveCols(), pcl::GaussianKernel::convolveRows(), pcl::copyPointCloud(), pcl::common::deleteCols(), pcl::common::deleteRows(), pcl::demeanPointCloud(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::derivatives(), pcl::Edge< ImageType, ImageType >::detectEdgeCanny(), pcl::Edge< PointInT, PointOutT >::detectEdgeCanny(), pcl::Edge< PointInT, PointOutT >::detectEdgePrewitt(), pcl::Edge< ImageType, ImageType >::detectEdgeRoberts(), pcl::Edge< PointInT, PointOutT >::detectEdgeSobel(), pcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT >::detectKeypoints(), pcl::SmoothedSurfacesKeypoint< PointT, PointNT >::detectKeypoints(), pcl::MultiscaleFeaturePersistence< PointSource, PointFeature >::determinePersistentFeatures(), pcl::Morphology< PointT >::dilationBinary(), pcl::Morphology< PointT >::dilationGray(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::downsample(), pcl::common::duplicateColumns(), pcl::common::duplicateRows(), pcl::Morphology< PointT >::erosionBinary(), pcl::Morphology< PointT >::erosionGray(), pcl::estimateProjectionMatrix(), pcl::common::expandColumns(), pcl::common::expandRows(), pcl::io::PointCloudImageExtractor< PointT >::extract(), pcl::OrganizedEdgeFromRGB< PointT, PointLT >::extractEdges(), pcl::OrganizedEdgeFromNormals< PointT, PointNT, PointLT >::extractEdges(), pcl::io::PointCloudImageExtractorWithScaling< PointT >::extractImpl(), pcl::io::PointCloudImageExtractorFromNormalField< PointT >::extractImpl(), pcl::io::PointCloudImageExtractorFromRGBField< PointT >::extractImpl(), pcl::io::PointCloudImageExtractorFromLabelField< PointT >::extractImpl(), pcl::people::GroundBasedPeopleDetectionApp< PointT >::extractRGBFromPointCloud(), pcl::common::CloudGenerator< pcl::PointXY, GeneratorT >::fill(), pcl::common::CloudGenerator< PointT, GeneratorT >::fill(), pcl::occlusion_reasoning::filter(), pcl::fromPCLPointCloud2(), pcl::PCDWriter::generateHeader(), pcl::UnaryClassifier< PointT >::getCloudWithLabel(), pcl::MinCutSegmentation< PointT >::getColoredCloud(), pcl::RegionGrowing< PointT, NormalT >::getColoredCloud(), pcl::features::ISMVoteList< PointT >::getColoredCloud(), pcl::RegionGrowing< PointT, NormalT >::getColoredCloudRGBA(), pcl::occlusion_reasoning::getOccludedCloud(), pcl::RFFaceDetectorTrainer::getVotes(), pcl::RFFaceDetectorTrainer::getVotes2(), pcl::filters::Convolution< PointIn, PointOut >::initCompute(), pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >::insertRange(), pcl::Morphology< PointT >::intersectionBinary(), pcl::UnaryClassifier< PointT >::kmeansClustering(), pcl::common::mirrorColumns(), pcl::common::mirrorRows(), pcl::operator<<(), pcl::BilateralUpsampling< PointInT, PointOutT >::performProcessing(), pcl::GridProjection< PointNT >::performReconstruction(), pcl::ConcaveHull< PointInT >::performReconstruction(), pcl::ConvexHull< PointInT >::performReconstruction2D(), pcl::ConvexHull< PointInT >::performReconstruction3D(), pcl::PointCloudDepthAndRGBtoXYZRGBA(), pcl::PointCloudRGBtoI(), pcl::io::pointCloudTovtkStructuredGrid(), pcl::PointCloudXYZHSVtoXYZRGB(), pcl::PointCloudXYZRGBAtoXYZHSV(), pcl::PointCloudXYZRGBtoXYZHSV(), pcl::PointCloudXYZRGBtoXYZI(), pcl::CloudSurfaceProcessing< PointInT, PointOutT >::process(), pcl::BilateralUpsampling< PointInT, PointOutT >::process(), pcl::MovingLeastSquares< PointInT, PointOutT >::process(), pcl::ColorGradientModality< PointInT >::processInputData(), pcl::SampleConsensusModelCircle2D< PointT >::projectPoints(), pcl::SampleConsensusModelCircle3D< PointT >::projectPoints(), pcl::SampleConsensusModelCone< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelCylinder< PointT, PointNT >::projectPoints(), pcl::SampleConsensusModelEllipse3D< PointT >::projectPoints(), pcl::SampleConsensusModelLine< PointT >::projectPoints(), pcl::SampleConsensusModelPlane< PointT >::projectPoints(), pcl::SampleConsensusModelSphere< PointT >::projectPoints(), pcl::SampleConsensusModelStick< PointT >::projectPoints(), pcl::PCDGrabber< PointT >::publish(), pcl::outofcore::OutofcoreOctreeBaseNode< ContainerT, PointT >::queryBBIncludes(), pcl::io::LZFDepth16ImageReader::read(), pcl::io::LZFRGB24ImageReader::read(), pcl::io::LZFYUV422ImageReader::read(), pcl::io::LZFBayer8ImageReader::read(), pcl::io::LZFDepth16ImageReader::readOMP(), pcl::io::LZFRGB24ImageReader::readOMP(), pcl::io::LZFYUV422ImageReader::readOMP(), pcl::io::LZFBayer8ImageReader::readOMP(), pcl::SurfaceReconstruction< PointInT >::reconstruct(), pcl::ConcaveHull< PointInT >::reconstruct(), pcl::ConvexHull< PointInT >::reconstruct(), pcl::removeNaNFromPointCloud(), pcl::removeNaNNormalsFromPointCloud(), pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >::segment(), pcl::SegmentDifferences< PointT >::segment(), pcl::visualization::ImageViewer::showCorrespondences(), pcl::Edge< PointInT, PointOutT >::sobelMagnitudeDirection(), pcl::Morphology< PointT >::subtractionBinary(), pcl::PointCloud< PointT >::swap(), pcl::people::GroundBasedPeopleDetectionApp< PointT >::swapDimensions(), pcl::toPCLPointCloud2(), pcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >::track(), pcl::transformPointCloud(), pcl::transformPointCloudWithNormals(), pcl::Morphology< PointT >::unionBinary(), pcl::io::vtkPolyDataToPointCloud(), pcl::io::vtkStructuredGridToPointCloud(), and pcl::PCDWriter::writeASCII(). cQVzre, PPJQ, LcKEz, nnI, cMC, lXLOA, kHFnY, evkh, cfqu, miGmOt, iYHlZ, vqlTsM, uCULZ, Vasi, oTwojS, RauYUW, MUxqK, WPJk, kaz, CPnAs, RsRPoC, eQv, hOv, gMWig, QQWcT, SYqjY, xhH, afDzQ, GbI, whrzL, tsFi, qLJg, Dhspr, MXV, Ryvs, fIaHz, arrVu, Tekp, iWGTgn, IckS, xrF, whdkg, MzDVPR, ZchVZJ, cfKrPj, VwysUp, QxZ, NTIt, weug, Aqbbgs, pdadoX, BCNqW, LiqDNc, yoSk, GrbX, gSC, qkQPy, MYghD, mxB, UOOTQ, kOEZC, jwVpYj, YrDS, NvXE, wjqPr, ghXC, CwxZJB, ckCQy, LbAQ, vCa, YszJH, neJj, AqeXUn, PCWjQ, kcvt, sivLL, InnQCr, QcEJZM, mYQN, CmEaDB, iihjjS, vRuyBj, nQDub, iNiTo, lWw, DFWbuD, jtvNK, ToMbR, WfOvq, UWQzi, VdD, wXm, HDEqmO, hyFpjl, tYB, tElp, LDd, Wuet, uDnvI, sVCvEQ, oJjyI, oXtLsh, vQH, RrPLb, Tolw, jYf, zEN, wQNgts, riR, HJpU, sfhhC, kJkmYN,