Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
pcl::_AxisA point structure representing an Axis using its normal coordinates
pcl::_NormalA point structure representing normal coordinates and the surface curvature estimate
pcl::tracking::_ParticleXYZRPY
pcl::_PointWithViewpoint
pcl::_PointXYZ
pcl::_PointXYZHSV
pcl::_PointXYZIA point structure representing Euclidean xyz coordinates, and the intensity value
pcl::_PointXYZRGB
pcl::_PointXYZRGBAA point structure representing Euclidean xyz coordinates, and the RGBA color
pcl::_PointXYZRGBL
pcl::_PointXYZRGBNormalA point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate
pcl::_ReferenceFrameA structure representing the Local Reference Frame of a point
pcl::AdaptiveRangeCoderAdaptiveRangeCoder compression class
pcl::ApproximateVoxelGrid< PointT >ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::tracking::ApproxNearestPairPointCloudCoherence< PointInT >ApproxNearestPairPointCloudCoherence computes coherence between two pointclouds using the approximate nearest point pairs
pcl::traits::asEnum< T >
pcl::traits::asEnum< double >
pcl::traits::asEnum< float >
pcl::traits::asEnum< int16_t >
pcl::traits::asEnum< int32_t >
pcl::traits::asEnum< int8_t >
pcl::traits::asEnum< uint16_t >
pcl::traits::asEnum< uint32_t >
pcl::traits::asEnum< uint8_t >
pcl::traits::asType< int >
pcl::traits::asType< sensor_msgs::PointField::FLOAT32 >
pcl::traits::asType< sensor_msgs::PointField::FLOAT64 >
pcl::traits::asType< sensor_msgs::PointField::INT16 >
pcl::traits::asType< sensor_msgs::PointField::INT32 >
pcl::traits::asType< sensor_msgs::PointField::INT8 >
pcl::traits::asType< sensor_msgs::PointField::UINT16 >
pcl::traits::asType< sensor_msgs::PointField::UINT32 >
pcl::traits::asType< sensor_msgs::PointField::UINT8 >
pcl::Axis
pcl::BilateralFilter< PointT >A bilateral filter implementation for point cloud data
pcl::BivariatePolynomialT< real >This represents a bivariate polynomial and provides some functionality for it
pcl::BorderDescriptionA structure to store if a point in a range image lies on a border between an obstacle and the background
pcl::BoundaryA point structure representing a description of whether a point is lying on a surface boundary or not
pcl::BoundaryEstimation< PointInT, PointNT, PointOutT >BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion
pcl::BoundaryEstimation< PointInT, PointNT, Eigen::MatrixXf >BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion
pcl::search::BruteForce< PointT >Implementation of a simple brute force search algorithm
pcl::visualization::CameraCamera class holds a set of camera parameters together with the window pos/size
pcl::io::ply::cameraWrapper for PLY camera structure to ease read/write
pcl::ChannelPropertiesChannelProperties stores the properties of each channel in a cloud, namely:
pcl::Clipper3D< PointT >Base class for 3D clipper objects
cloud_point_index_idx
pcl::visualization::CloudActor
pcl::CloudPropertiesCloudProperties stores a list of optional point cloud properties such as:
pcl::visualization::CloudViewerSimple point cloud visualization class
pcl::octree::ColorCoding< PointT >ColorCoding class
pcl::ComparisonBase< PointT >The (abstract) base class for the comparison object
pcl::ComputeFailedException
pcl::ConcaveHull< PointInT >ConcaveHull (alpha shapes) using libqhull library
pcl::ConditionalRemoval< PointT >ConditionalRemoval filters data that satisfies certain conditions
pcl::ConditionAnd< PointT >AND condition
pcl::ConditionBase< PointT >Base condition class
pcl::ConditionOr< PointT >OR condition
pcl::octree::configurationProfile_t
pcl::ConvexHull< PointInT >ConvexHull using libqhull library
pcl::common::Convolution< PointOperatorsType >Class Convolution Convolution is a mathematical operation on two functions f and g, producing a third function that is typically viewed as a modified version of one of the original functions
pcl::CopyIfFieldExists< PointInT, OutT >A helper functor that can copy a specific value if the given field exists
pcl::CorrespondenceCorrespondence represents a match between two entities (e.g., points, descriptors, etc)
pcl::registration::CorrespondenceEstimation< PointSource, PointTarget >CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features
pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT >CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud
pcl::registration::CorrespondenceRejectorCorrespondenceRejector represents the base class for correspondence rejection methods
pcl::registration::CorrespondenceRejectorDistanceCorrespondenceRejectorDistance implements a simple correspondence rejection method based on thresholding the distances between the correspondences
pcl::registration::CorrespondenceRejectorFeaturesCorrespondenceRejectorFeatures implements a correspondence rejection method based on a set of feature descriptors
pcl::registration::CorrespondenceRejectorOneToOneCorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences
pcl::registration::CorrespondenceRejectorSampleConsensus< PointT >CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
pcl::registration::CorrespondenceRejectorTrimmedCorrespondenceRejectorTrimmed implements a correspondence rejection for ICP-like registration algorithms that uses only the best 'k' correspondences where 'k' is some estimate of the overlap between the two point clouds being registered
pcl::CropBox< PointT >CropBox is a filter that allows the user to filter all the data inside of a given box
pcl::CropBox< sensor_msgs::PointCloud2 >CropBox is a filter that allows the user to filter all the data inside of a given box
pcl::CropHull< PointT >Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes
pcl::CustomPointRepresentation< PointDefault >CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point
pcl::CVFHEstimation< PointInT, PointNT, PointOutT >CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in:

  • CAD-Model Recognition and 6 DOF Pose Estimation A
pcl::traits::datatype< PointT, Tag >
pcl::traits::decomposeArray< T >
pcl::DefaultFeatureRepresentation< PointDefault >DefaulFeatureRepresentation extends PointRepresentation and is intended to be used when defining the default behavior for feature descriptor types (i.e., copy each element of each field into a float array)
pcl::DefaultPointRepresentation< PointDefault >DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types
pcl::DefaultPointRepresentation< FPFHSignature33 >
pcl::DefaultPointRepresentation< NormalBasedSignature12 >
pcl::DefaultPointRepresentation< PFHRGBSignature250 >
pcl::DefaultPointRepresentation< PFHSignature125 >
pcl::DefaultPointRepresentation< PointNormal >
pcl::DefaultPointRepresentation< PointXYZ >
pcl::DefaultPointRepresentation< PointXYZI >
pcl::DefaultPointRepresentation< PPFSignature >
pcl::DefaultPointRepresentation< SHOT >
pcl::DefaultPointRepresentation< VFHSignature308 >
openni_wrapper::DepthImageThis class provides methods to fill a depth or disparity image
openni_wrapper::DeviceKinectConcrete implementation of the interface OpenNIDevice for a MS Kinect device
openni_wrapper::DeviceONIConcrete implementation of the interface OpenNIDevice for a virtual device playing back an ONI file
openni_wrapper::DevicePrimesenseConcrete implementation of the interface OpenNIDevice for a Primesense device
openni_wrapper::DeviceXtionProConcrete implementation of the interface OpenNIDevice for a Asus Xtion Pro device
pcl::tracking::DistanceCoherence< PointInT >DistanceCoherence computes coherence between two points from the distance between them
pcl::apps::DominantPlaneSegmentation< PointType >DominantPlaneSegmentation performs euclidean segmentation on a scene assuming that a dominant plane exists
pcl::EarClippingThe ear clipping triangulation algorithm
pcl::registration::ELCH< PointT >ELCH (Explicit Loop Closing Heuristic) class
pcl::io::ply::element
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctor
pcl::EuclideanClusterExtraction< PointT >EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
pcl::RangeImage::ExtractedPlaneHelper struct to return the results of a plane extraction
pcl::ExtractIndices< PointT >ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud
pcl::ExtractIndices< sensor_msgs::PointCloud2 >ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud
pcl::ExtractPolygonalPrismData< PointT >ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism
pcl::Feature< PointInT, PointOutT >Feature represents the base feature class
pcl::FeatureFromLabels< PointInT, PointLT, PointOutT >
pcl::FeatureFromNormals< PointInT, PointNT, PointOutT >
pcl::Narf::FeaturePointRepresentation
pcl::detail::FieldAdder< PointT >
pcl::FieldComparison< PointT >The field-based specialization of the comparison object
pcl::traits::fieldList< PointT >
pcl::detail::FieldMapper< PointT >
pcl::detail::FieldMapping
pcl::FileReaderPoint Cloud Data (FILE) file format reader interface
pcl::FileWriterPoint Cloud Data (FILE) file format writer
pcl::Filter< PointT >Filter represents the base filter class
pcl::Filter< sensor_msgs::PointCloud2 >Filter represents the base filter class
pcl::FilterIndices< PointT >Filter represents the base filter class
pcl::FilterIndices< sensor_msgs::PointCloud2 >FilterIndices represents the base filter with indices class
pcl::visualization::FloatImageUtilsProvide some gerneral functionalities regarding 2d float arrays, e.g., for visualization purposes
pcl::for_each_type_impl< done >
pcl::for_each_type_impl< false >
pcl::FPFHEstimation< PointInT, PointNT, PointOutT >FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
pcl::FPFHEstimation< PointInT, PointNT, Eigen::MatrixXf >FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
pcl::FPFHEstimationOMP< PointInT, PointNT, PointOutT >FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard
pcl::FPFHSignature33A point structure representing the Fast Point Feature Histogram (FPFH)
pcl::visualization::FPSCallback
pcl::Functor< _Scalar, NX, NY >Base functor all the models that need non linear optimization must define their own one and implement operator() (const Eigen::VectorXd& x, Eigen::VectorXd& fvec) or operator() (const Eigen::VectorXf& x, Eigen::VectorXf& fvec) dependening on the choosen _Scalar
pcl::GaussianKernelClass GaussianKernel assembles all the method for computing, convolving, smoothing, gradients computing an image using a gaussian kernel
pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al
pcl::GFPFHSignature16A point structure representing the GFPFH descriptor with 16 bins
pcl::GrabberGrabber interface for PCL 1.x device drivers
pcl::GreedyProjectionTriangulation< PointInT >GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections
pcl::GridProjection< PointNT >Grid projection surface reconstruction method
pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals
pcl::PPFHashMapSearch::HashKeyStructData structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class
pcl::he
std_msgs::Header
pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT, NrDims >
pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT[NrDims], NrDims >
pcl::Histogram< N >A point structure representing an N-D histogram
pcl::tracking::HSVColorCoherence< PointInT >HSVColorCoherence computes coherence between the two points from the color difference between them
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::HuberPenalty
openni_wrapper::ImageImage class containing just a reference to image meta data
sensor_msgs::Image
openni_wrapper::ImageBayerGRBGThis class provides methods to fill a RGB or Grayscale image buffer from underlying Bayer pattern image
openni_wrapper::ImageRGB24This class provides methods to fill a RGB or Grayscale image buffer from underlying RGB24 image
pcl::visualization::ImageViewerImageViewer is a class for 2D image visualization
openni_wrapper::ImageYUV422Concrete implementation of the interface Image for a YUV 422 image used by Primesense devices
pcl::registration::IncrementalRegistration< PointT >
pcl::InitFailedExceptionAn exception thrown when init can not be performed should be used in all the PCLBase class inheritants
pcl::IntegralImage2D< DataType, Dimension >Determines an integral image representation for a given organized data array
pcl::IntegralImage2D< DataType, 1 >Partial template specialization for integral images with just one channel
pcl::IntegralImageNormalEstimation< PointInT, PointOutT >Surface normal estimation on dense data using integral images
pcl::IntegralImageTypeTraits< DataType >
pcl::IntegralImageTypeTraits< char >
pcl::IntegralImageTypeTraits< float >
pcl::IntegralImageTypeTraits< int >
pcl::IntegralImageTypeTraits< short >
pcl::IntegralImageTypeTraits< unsigned char >
pcl::IntegralImageTypeTraits< unsigned int >
pcl::IntegralImageTypeTraits< unsigned short >
pcl::IntensityGradientA point structure representing the intensity gradient of an XYZI point cloud
pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT >IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values
pcl::IntensityGradientEstimation< PointInT, PointNT, Eigen::MatrixXf >IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values
pcl::IntensitySpinEstimation< PointInT, PointOutT >IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity
pcl::IntensitySpinEstimation< PointInT, Eigen::MatrixXf >IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity
pcl::InterestPointA point structure representing an interest point with Euclidean xyz coordinates, and an interest value
pcl::intersect< Sequence1, Sequence2 >
pcl::InvalidConversionExceptionAn exception that is thrown when a PointCloud2 message cannot be converted into a PCL type
pcl::InvalidSACModelTypeExceptionAn exception that is thrown when a sample consensus model doesn't have the correct number of samples defined in model_types.h
pcl::IOExceptionAn exception that is thrown during an IO error (typical read/write errors)
openni_wrapper::IRImageClass containing just a reference to IR meta data
pcl::PosesFromMatches::PoseEstimate::IsBetter
pcl::IsNotDenseExceptionAn exception that is thrown when a PointCloud is not dense but is attemped to be used as dense
pcl::IterativeClosestPoint< PointSource, PointTarget >IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm
pcl::IterativeClosestPointNonLinear< PointSource, PointTarget >IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend
pcl::KdTree< PointT >KdTree represents the base spatial locator class for kd-tree implementations
pcl::search::KdTree< PointT >search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure
pcl::KdTreeFLANN< PointT, Dist >KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures
pcl::KdTreeFLANN< Eigen::MatrixXf >KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures
pcl::KernelWidthTooSmallExceptionAn exception that is thrown when the kernel size is too small
pcl::visualization::KeyboardEvent/brief Class representing key hit/release events
pcl::Keypoint< PointInT, PointOutT >Keypoint represents the base class for key points
pcl::tracking::KLDAdaptiveParticleFilterOMPTracker< PointInT, StateT >KLDAdaptiveParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method
pcl::tracking::KLDAdaptiveParticleFilterTracker< PointInT, StateT >KLDAdaptiveParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method
pcl::Label
pcl::LabeledEuclideanClusterExtraction< PointT >LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info
pcl::GridProjection< PointNT >::LeafData leaf
pcl::MarchingCubes< PointNT >::LeafSimple structure to hold a voxel
pcl::LeastMedianSquares< PointT >LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm
pcl::io::ply::list_property
pcl::io::ply::ply_parser::list_property_begin_callback_type< SizeType, ScalarType >
pcl::io::ply::ply_parser::list_property_definition_callback_type< SizeType, ScalarType >
pcl::io::ply::ply_parser::list_property_definition_callbacks_type
pcl::io::ply::ply_parser::list_property_element_callback_type< SizeType, ScalarType >
pcl::io::ply::ply_parser::list_property_end_callback_type< SizeType, ScalarType >
pcl::RangeImageBorderExtractor::LocalSurfaceStores some information extracted from the neighborhood of a point
pcl::MarchingCubes< PointNT >The marching cubes surface reconstruction algorithm
pcl::MarchingCubesGreedy< PointNT >The marching cubes surface reconstruction algorithm, using a "greedy" voxelization algorithm
pcl::MarchingCubesGreedyDot< PointNT >The marching cubes surface reconstruction algorithm, using a "greedy" voxelization algorithm combined with a dot product, to remove the double surface effect
pcl::MaximumLikelihoodSampleConsensus< PointT >MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S
pcl::MeshConstruction< PointInT >MeshConstruction represents a base surface reconstruction class
pcl::MeshProcessingMeshProcessing represents the base class for mesh processing algorithms
pcl::MeshSmoothingLaplacianVTKPCL mesh smoothing based on the vtkSmoothPolyDataFilter algorithm from the VTK library
pcl::MeshSmoothingWindowedSincVTKPCL mesh smoothing based on the vtkWindowedSincPolyDataFilter algorithm from the VTK library
pcl::MeshSubdivisionVTKPCL mesh smoothing based on the vtkLinearSubdivisionFilter, vtkLoopSubdivisionFilter, vtkButterflySubdivisionFilter depending on the selected MeshSubdivisionVTKFilterType algorithm from the VTK library
pcl::MEstimatorSampleConsensus< PointT >MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S
pcl::ModelCoefficients
pcl::MomentInvariantsA point structure representing the three moment invariants
pcl::MomentInvariantsEstimation< PointInT, PointOutT >MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
pcl::MomentInvariantsEstimation< PointInT, Eigen::MatrixXf >MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
pcl::visualization::MouseEvent
pcl::MovingLeastSquares< PointInT, NormalOutT >MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation
pcl::MovingLeastSquaresOMP< PointInT, NormalOutT >MovingLeastSquaresOMP represent an OpenMP implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation
pcl::MultiscaleFeaturePersistence< PointSource, PointFeature >Generic class for extracting the persistent features from an input point cloud It can be given any Feature estimator instance and will compute the features of the input over a multiscale representation of the cloud and output the unique ones over those scales
pcl::traits::name< PointT, Tag, dummy >
pcl::NarfNARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data
pcl::Narf36A point structure representing the Narf descriptor
pcl::NarfDescriptorComputes NARF feature descriptors for points in a range image
pcl::NarfKeypointNARF (Normal Aligned Radial Feature) keypoints
pcl::NdCentroidFunctor< PointT >Helper functor structure for n-D centroid estimation
pcl::NdConcatenateFunctor< PointInT, PointOutT >Helper functor structure for concatenate
pcl::NdCopyEigenPointFunctor< PointOutT >Helper functor structure for copying data between an Eigen type and a PointT
pcl::NdCopyPointEigenFunctor< PointInT >Helper functor structure for copying data between an Eigen type and a PointT
pcl::tracking::NearestPairPointCloudCoherence< PointInT >NearestPairPointCloudCoherence computes coherence between two pointclouds using the nearest point pairs
pcl::NNClassification< PointT >Nearest neighbor search based classification of PCL point type features
pcl::Normal
pcl::NormalBasedSignature12A point structure representing the Normal Based Signature for a feature matrix of 4-by-3
pcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature >Normal-based feature signature estimation class
pcl::tracking::NormalCoherence< PointInT >NormalCoherence computes coherence between two points from the angle between their normals
pcl::NormalEstimation< PointInT, PointOutT >NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point
pcl::NormalEstimation< PointInT, Eigen::MatrixXf >NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures
pcl::NormalEstimationOMP< PointInT, PointOutT >NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
pcl::NormalEstimationOMP< PointInT, Eigen::MatrixXf >NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
pcl::NormalSpaceSampling< PointT, NormalT >NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point
pcl::NotEnoughPointsExceptionAn exception that is thrown when the number of correspondants is not equal to the minimum required
pcl::io::ply::obj_info
pcl::search::Octree< PointT, LeafTWrap, OctreeT >search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure
pcl::octree::Octree2BufBase< DataT, LeafT >Octree double buffer class
pcl::octree::OctreeBase< DataT, LeafT, OctreeBranchT >Octree class
pcl::octree::OctreeBaseWithState< DataT, LeafT >Octree Branch with state
pcl::octree::OctreeBranchOctree branch class
pcl::octree::OctreeBranchWithState
pcl::octree::OctreeBreadthFirstIterator< DataT, LeafT, OctreeT >Octree iterator class
pcl::octree::OctreeDepthFirstIterator< DataT, LeafT, OctreeT >Octree iterator class
pcl::octree::OctreeIteratorBase< DataT, LeafT, OctreeT >Abstract octree iterator class
pcl::octree::OctreeLeafAbstract< DataT >Abstract octree leaf class
pcl::octree::OctreeLeafDataT< DataT >Octree leaf class that does store a single DataT element
pcl::octree::OctreeLeafDataTVector< DataT >Octree leaf class that does store a vector of DataT elements
pcl::octree::OctreeLeafEmpty< DataT >Octree leaf class that does not store any information
pcl::octree::OctreeLeafNodeIterator< DataT, LeafT, OctreeT >Octree leaf node iterator class
pcl::octree::OctreeLowMemBase< DataT, LeafT >Octree class
pcl::octree::OctreeNodeAbstract octree node class
pcl::octree::OctreePointCloud< PointT, LeafT, OctreeT >Octree pointcloud class
pcl::octree::OctreePointCloudChangeDetector< PointT, LeafT >Octree pointcloud change detector class
pcl::octree::OctreePointCloudDensity< PointT, LeafT, OctreeT >Octree pointcloud density class
pcl::octree::OctreePointCloudDensityLeaf< DataT >Octree pointcloud density leaf node class
pcl::octree::OctreePointCloudOccupancy< PointT, LeafT, OctreeT >Octree pointcloud occupancy class
pcl::octree::OctreePointCloudPointVector< PointT, LeafT, OctreeT >Octree pointcloud point vector class
pcl::octree::OctreePointCloudSearch< PointT, LeafT, OctreeT >Octree pointcloud search class
pcl::octree::OctreePointCloudSinglePoint< PointT, LeafT, OctreeT >Octree pointcloud single point class
pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafT, OctreeT >Octree pointcloud voxel centroid class
pcl::traits::offset< PointT, Tag >
pcl::ONIGrabber
openni_wrapper::OpenNIDeviceClass representing an astract device for OpenNI devices: Primesense PSDK, Microsoft Kinect, Asus Xtion Pro/Live
openni_wrapper::OpenNIDriverDriver class implemented as Singleton
openni_wrapper::OpenNIExceptionGeneral exception class
pcl::OpenNIGrabberGrabber for OpenNI devices (i.e., Primesense PSDK, Microsoft Kinect, Asus XTion Pro/Live)
pcl::OrganizedFastMesh< PointInT >Simple triangulation/surface reconstruction for organized point clouds
pcl::search::OrganizedNeighbor< PointT >OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds
pcl::PackedHSIComparison< PointT >A packed HSI specialization of the comparison object
pcl::PackedRGBComparison< PointT >A packed rgb specialization of the comparison object
pcl::RangeImageBorderExtractor::ParametersParameters used in this class
pcl::PosesFromMatches::ParametersParameters used in this class
pcl::NarfKeypoint::ParametersParameters used in this class
pcl::PolynomialCalculationsT< real >::ParametersParameters used in this class
pcl::NarfDescriptor::Parameters
pcl::io::ply::parser
pcl::tracking::ParticleFilterOMPTracker< PointInT, StateT >ParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method in parallel, using the OpenMP standard
pcl::tracking::ParticleFilterTracker< PointInT, StateT >ParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method
pcl::tracking::ParticleXYZRPY
pcl::PassThrough< PointT >PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
pcl::PassThrough< sensor_msgs::PointCloud2 >PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
pcl::PCA< PointT >Principal Component analysis (PCA) class
pcl::PCDGrabber< PointT >
pcl::PCDGrabberBaseBase class for PCD file grabber
pcl::PCDReaderPoint Cloud Data (PCD) file format reader
pcl::PCDWriterPoint Cloud Data (PCD) file format writer
pcl::PCLBase< PointT >PCL base class
pcl::PCLBase< sensor_msgs::PointCloud2 >
pcl::PCLExceptionA base class for all pcl exceptions which inherits from std::runtime_error
pcl::visualization::PCLHistogramVisualizerPCL histogram visualizer main class
pcl::visualization::PCLHistogramVisualizerInteractorStylePCL histogram visualizer interactory style class
pcl::PCLIOExceptionBase exception class for I/O operations
pcl::PCLSurfaceBase< PointInT >Pure abstract class
pcl::visualization::PCLVisualizerPCL Visualizer main class
pcl::visualization::PCLVisualizerInteractorThe PCLVisualizer interactor
pcl::visualization::PCLVisualizerInteractorStylePCLVisualizerInteractorStyle defines an unique, custom VTK based interactory style for PCL Visualizer applications
pcl::PFHEstimation< PointInT, PointNT, PointOutT >PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
pcl::PFHEstimation< PointInT, PointNT, Eigen::MatrixXf >PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
pcl::PFHRGBEstimation< PointInT, PointNT, PointOutT >
pcl::PFHRGBSignature250A point structure representing the Point Feature Histogram with colors (PFHRGB)
pcl::PFHSignature125A point structure representing the Point Feature Histogram (PFH)
pcl::PiecewiseLinearFunctionThis provides functionalities to efficiently return values for piecewise linear function
pcl::PlaneClipper3D< PointT >Implementation of a plane clipper in 3D
pcl::io::ply::ply_parserClass ply_parser parses a PLY file and generates appropriate atomic parsers for the body
pcl::PLYReaderPoint Cloud Data (PLY) file format reader
pcl::PLYWriterPoint Cloud Data (PLY) file format writer
pcl::traits::POD< PointT >
pcl::PointCloud< PointT >PointCloud represents the base class in PCL for storing collections of 3D points
sensor_msgs::PointCloud2
pcl::PointCloud< Eigen::MatrixXf >PointCloud specialization for Eigen matrices
pcl::tracking::PointCloudCoherence< PointInT >PointCloudCoherence is a base class to compute coherence between the two PointClouds
pcl::visualization::PointCloudColorHandler< PointT >Base Handler class for PointCloud colors
pcl::visualization::PointCloudColorHandler< sensor_msgs::PointCloud2 >Base Handler class for PointCloud colors
pcl::visualization::PointCloudColorHandlerCustom< PointT >Handler for predefined user colors
pcl::visualization::PointCloudColorHandlerCustom< sensor_msgs::PointCloud2 >Handler for predefined user colors
pcl::visualization::PointCloudColorHandlerGenericField< PointT >Generic field handler class for colors
pcl::visualization::PointCloudColorHandlerGenericField< sensor_msgs::PointCloud2 >Generic field handler class for colors
pcl::visualization::PointCloudColorHandlerHSVField< PointT >HSV handler class for colors
pcl::visualization::PointCloudColorHandlerHSVField< sensor_msgs::PointCloud2 >HSV handler class for colors
pcl::visualization::PointCloudColorHandlerRandom< PointT >Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen)
pcl::visualization::PointCloudColorHandlerRandom< sensor_msgs::PointCloud2 >Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen)
pcl::visualization::PointCloudColorHandlerRGBField< PointT >RGB handler class for colors
pcl::visualization::PointCloudColorHandlerRGBField< sensor_msgs::PointCloud2 >RGB handler class for colors
pcl::octree::PointCloudCompression< PointT, LeafT, OctreeT >Octree pointcloud compression class
pcl::visualization::PointCloudGeometryHandler< PointT >Base handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandler< sensor_msgs::PointCloud2 >Base handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerCustom< PointT >Custom handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerCustom< sensor_msgs::PointCloud2 >Custom handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT >Surface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< sensor_msgs::PointCloud2 >Surface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZ< PointT >XYZ handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZ< sensor_msgs::PointCloud2 >XYZ handler class for PointCloud geometry
pcl::octree::PointCoding< PointT >PointCoding class
pcl::tracking::PointCoherence< PointInT >PointCoherence is a base class to compute coherence between the two points
pcl::PointCorrespondence3DRepresentation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g
pcl::PointCorrespondence6DRepresentation of a (possible) correspondence between two points (e.g
pcl::PointDataAtOffset< PointT >A datatype that enables type-correct comparisons
sensor_msgs::PointField
pcl::PointIndices
pcl::PointNormalA point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate
pcl::common::PointOperators< PointIN, PointOUT >PointOperators is a struct that provides basic arithmetic operations on points: addition, product and plus-assign operation
pcl::visualization::PointPickingCallback
pcl::visualization::PointPickingEvent/brief Class representing 3D point picking events
pcl::PointRepresentation< PointT >PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector
pcl::PointSurfelA surfel, that is, a point structure representing Euclidean xyz coordinates, together with normal coordinates, a RGBA color, a radius, a confidence value and the surface curvature estimate
pcl::PointWithRangeA point structure representing Euclidean xyz coordinates, padded with an extra range float
pcl::PointWithScaleA point structure representing a 3-D position and scale
pcl::PointWithViewpointA point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen
pcl::PointXYA 2D point structure representing Euclidean xy coordinates
pcl::PointXYZA point structure representing Euclidean xyz coordinates
pcl::PointXYZHSV
pcl::PointXYZI
pcl::PointXYZINormalA point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate
pcl::common::PointXYZItoIntensity
pcl::common::PointXYZItoPointXYZI
pcl::PointXYZL
pcl::PointXYZRGBA point structure representing Euclidean xyz coordinates, and the RGB color
pcl::PointXYZRGBA
pcl::PointXYZRGBL
pcl::PointXYZRGBNormal
pcl::common::PointXYZRGBtoIntensity
pcl::common::PointXYZRGBtoPointXYZI
pcl::common::PointXYZRGBtoPointXYZRGB
pcl::PolygonMesh
pcl::PolynomialCalculationsT< real >This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials
pcl::PosesFromMatches::PoseEstimateA result of the pose estimation process
pcl::PosesFromMatchesCalculate 3D transformation based on point correspondencdes
pcl::PPFRegistration< PointSource, PointTarget >::PoseWithVotesStructure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes
pcl::PPFEstimation< PointInT, PointNT, PointOutT >Class that calculates the "surflet" features for each pair in the given pointcloud
pcl::PPFEstimation< PointInT, PointNT, Eigen::MatrixXf >Class that calculates the "surflet" features for each pair in the given pointcloud
pcl::PPFHashMapSearch
pcl::PPFRegistration< PointSource, PointTarget >Class that registers two point clouds based on their sets of PPFSignatures
pcl::PPFRGBEstimation< PointInT, PointNT, PointOutT >
pcl::PPFRGBRegionEstimation< PointInT, PointNT, PointOutT >
pcl::PPFRGBSignatureA point structure for storing the Point Pair Color Feature (PPFRGB) values
pcl::PPFSignatureA point structure for storing the Point Pair Feature (PPF) values
pcl::PrincipalCurvaturesA point structure representing the principal curvatures and their magnitudes
pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals
pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, Eigen::MatrixXf >PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals
pcl::PrincipalRadiiRSDA point structure representing the minimum and maximum surface radii (in meters) computed using RSD
pcl::ProgressiveSampleConsensus< PointT >RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O
pcl::ProjectInliers< PointT >ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
pcl::ProjectInliers< sensor_msgs::PointCloud2 >ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
pcl::io::ply::property
pcl::PyramidFeatureHistogram< PointFeature >Class that compares two sets of features by using a multiscale representation of the features inside a pyramid
pcl::RadiusOutlierRemoval< PointT >RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K
pcl::RadiusOutlierRemoval< sensor_msgs::PointCloud2 >RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K
pcl::RandomizedMEstimatorSampleConsensus< PointT >RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus)
pcl::RandomizedRandomSampleConsensus< PointT >RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O
pcl::RandomSample< PointT >RandomSample applies a random sampling with uniform probability
pcl::RandomSample< sensor_msgs::PointCloud2 >RandomSample applies a random sampling with uniform probability
pcl::RandomSampleConsensus< PointT >RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A
pcl::io::ply::range_grid
pcl::RangeImageRangeImage is derived from pcl/PointCloud and provides functionalities with focus on situations where a 3D scene was captured from a specific view point
pcl::RangeImageBorderExtractorExtract obstacle borders from range images, meaning positions where there is a transition from foreground to background
pcl::RangeImagePlanarRangeImagePlanar is derived from the original range image and differs from it because it's not a spherical projection, but using a projection plane (as normal cameras do), therefore being better applicable for range sensors that already provide a range image by themselves (stereo cameras, ToF-cameras), so that a conversion to point cloud and then to a spherical range image becomes unnecessary
pcl::visualization::RangeImageVisualizerRange image visualizer class
pcl::ReferenceFrame
pcl::Registration< PointSource, PointTarget >Registration represents the base registration class
pcl::RegistrationVisualizer< PointSource, PointTarget >RegistrationVisualizer represents the base class for rendering the intermediate positions ocupied by the source point cloud during it's registration to the target point cloud
pcl::visualization::RenWinInteract
pcl::RGBA structure representing RGB color information
pcl::TexMaterial::RGB
pcl::tracking::RGBValue
pcl::RIFTEstimation< PointInT, GradientT, PointOutT >RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity
pcl::RIFTEstimation< PointInT, GradientT, Eigen::MatrixXf >RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity
pcl::RSDEstimation< PointInT, PointNT, PointOutT >RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals
pcl::SACSegmentation< PointT >SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation
pcl::SACSegmentationFromNormals< PointT, PointNT >SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation
pcl::SampleConsensus< T >SampleConsensus represents the base class
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al
pcl::SampleConsensusModel< PointT >SampleConsensusModel represents the base model class
pcl::SampleConsensusModelCircle2D< PointT >SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane
pcl::SampleConsensusModelCylinder< PointT, PointNT >SampleConsensusModelCylinder defines a model for 3D cylinder segmentation
pcl::SampleConsensusModelFromNormals< PointT, PointNT >SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation
pcl::SampleConsensusModelLine< PointT >SampleConsensusModelLine defines a model for 3D line segmentation
pcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT >SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints
pcl::SampleConsensusModelNormalPlane< PointT, PointNT >SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints
pcl::SampleConsensusModelParallelLine< PointT >SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints
pcl::SampleConsensusModelParallelPlane< PointT >SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints
pcl::SampleConsensusModelPerpendicularPlane< PointT >SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints
pcl::SampleConsensusModelPlane< PointT >SampleConsensusModelPlane defines a model for 3D plane segmentation
pcl::SampleConsensusModelRegistration< PointT >SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection
pcl::SampleConsensusModelSphere< PointT >SampleConsensusModelSphere defines a model for 3D sphere segmentation
pcl::SampleConsensusModelStick< PointT >SampleConsensusModelStick defines a model for 3D stick segmentation
pcl::io::ply::ply_parser::scalar_property_callback_type< ScalarType >
pcl::io::ply::ply_parser::scalar_property_definition_callback_type< ScalarType >
pcl::io::ply::ply_parser::scalar_property_definition_callbacks_type
pcl::ScopeTimeClass to measure the time spent in a scope
pcl::search::Search< PointT >Generic search class
pcl::SearchPoint
pcl::SegmentDifferences< PointT >SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold
pcl::RangeImageBorderExtractor::ShadowBorderIndicesStores the indices of the shadow border corresponding to obstacle borders
pcl::ShapeContextA point structure representing a Shape Context
pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT >ShapeContext3DEstimation implements the 3D shape context descriptor as described in:

  • Andrea Frome, Daniel Huber, Ravi Kolluri and Thomas Bülow, Jitendra Malik Recognizing Objects in Range Data Using Regional Point Descriptors, In proceedings of the 8th European Conference on Computer Vision (ECCV), Prague, May 11-14, 2004
pcl::ShapeContext3DEstimation< PointInT, PointNT, Eigen::MatrixXf >ShapeContext3DEstimation implements the 3D shape context descriptor as described in:

  • Andrea Frome, Daniel Huber, Ravi Kolluri and Thomas Bülow, Jitendra Malik Recognizing Objects in Range Data Using Regional Point Descriptors, In proceedings of the 8th European Conference on Computer Vision (ECCV), Prague, May 11-14, 2004
pcl::SHOTA point structure representing the generic Signature of Histograms of OrienTations (SHOT)
pcl::SHOTEstimation< PointInT, PointNT, PointOutT >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimation< pcl::PointXYZRGBA, PointNT, Eigen::MatrixXf >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimation< pcl::PointXYZRGBA, PointNT, PointOutT >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimation< PointInT, PointNT, Eigen::MatrixXf >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimationBase< PointInT, PointNT, PointOutT >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimationBase< PointInT, PointNT, Eigen::MatrixXf >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard
pcl::SHOTEstimationOMP< pcl::PointXYZRGBA, PointNT, PointOutT >
pcl::SIFTKeypoint< PointInT, PointOutT >SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity
pcl::SIFTKeypointFieldSelector< PointT >
pcl::SIFTKeypointFieldSelector< PointNormal >
pcl::SIFTKeypointFieldSelector< PointXYZRGB >
pcl::SIFTKeypointFieldSelector< PointXYZRGBA >
pcl::surface::SimplificationRemoveUnusedVertices
pcl::SmoothedSurfacesKeypoint< PointT, PointNT >Based on the paper: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria
pcl::SolverDidntConvergeExceptionAn exception that is thrown when the non linear solver didn't converge
pcl::registration::sortCorrespondencesByDistancesortCorrespondencesByDistance : a functor for sorting correspondences by distance
pcl::registration::sortCorrespondencesByMatchIndexsortCorrespondencesByMatchIndex : a functor for sorting correspondences by match index
pcl::registration::sortCorrespondencesByMatchIndexAndDistancesortCorrespondencesByMatchIndexAndDistance : a functor for sorting correspondences by match index _and_ distance
pcl::registration::sortCorrespondencesByQueryIndexsortCorrespondencesByQueryIndex : a functor for sorting correspondences by query index
pcl::registration::sortCorrespondencesByQueryIndexAndDistancesortCorrespondencesByQueryIndexAndDistance : a functor for sorting correspondences by query index _and_ distance
pcl::SpinImageEstimation< PointInT, PointNT, PointOutT >Estimates spin-image descriptors in the given input points
pcl::SpinImageEstimation< PointInT, PointNT, Eigen::MatrixXf >Estimates spin-image descriptors in the given input points
pcl::StaticRangeCoderStaticRangeCoder compression class
pcl::StatisticalMultiscaleInterestRegionExtraction< PointT >Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach
pcl::StatisticalOutlierRemoval< PointT >StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
pcl::StatisticalOutlierRemoval< sensor_msgs::PointCloud2 >StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
pcl::StopWatchSimple stopwatch
pcl::SurfaceReconstruction< PointInT >SurfaceReconstruction represents a base surface reconstruction class
pcl::SurfelSmoothing< PointT, PointNT >
pcl::Synchronizer< T1, T2 >/brief This template class synchronizes two data streams of different types
pcl::TexMaterial
pcl::TextureMapping< PointInT >The texture mapping algorithm
pcl::TextureMesh
pcl::console::TicToc
pcl::TimeTriggerTimer class that invokes registered callback methods periodically
pcl::tracking::Tracker< PointInT, StateT >Tracker represents the base tracker class
pcl::registration::TransformationEstimation< PointSource, PointTarget >TransformationEstimation represents the base class for methods for transformation estimation based on:

  • correspondence vectors
  • two point clouds (source and target) of the same size
  • a point cloud with a set of indices (source), and another point cloud (target)
  • two point clouds with two sets of indices (source and target) of the same size
pcl::registration::TransformationEstimationLM< PointSource, PointTarget >TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences
pcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget >TransformationEstimationPointToPlane uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences
pcl::registration::TransformationEstimationPointToPlaneLLS< PointSource, PointTarget >TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals
pcl::registration::TransformationEstimationSVD< PointSource, PointTarget >TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences
pcl::TransformationFromCorrespondencesCalculates a transformation based on corresponding 3D points
pcl::registration::TransformationValidation< PointSource, PointTarget >TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation
pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget >TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::TruncatedError
pcl::UnhandledPointTypeException
pcl::UniformSampling< PointInT >UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::UniqueShapeContext< PointInT, PointOutT >UniqueShapeContext implements the Unique Shape Descriptor described here:
pcl::UniqueShapeContext< PointInT, Eigen::MatrixXf >UniqueShapeContext implements the Unique Shape Descriptor described here:
pcl::UnorganizedPointCloudExceptionAn exception that is thrown when an organized point cloud is needed but not provided
pcl::VectorAverage< real, dimension >Calculates the weighted average and the covariance matrix
pcl::registration::ELCH< PointT >::Vertex
pcl::VerticesDescribes a set of vertices in a polygon mesh, by basically storing an array of indices
pcl::VFHClassifierNNUtility class for nearest neighbor search based classification of VFH features
pcl::VFHEstimation< PointInT, PointNT, PointOutT >VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals
pcl::VFHSignature308A point structure representing the Viewpoint Feature Histogram (VFH)
pcl::VoxelGrid< PointT >VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::VoxelGrid< sensor_msgs::PointCloud2 >VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::surface::VTKSmootherVTKSmoother is a wrapper around some subdivision and filter methods from VTK
pcl::VTKUtils
pcl::WarpPointRigid< PointSourceT, PointTargetT >
pcl::WarpPointRigid3D< PointSourceT, PointTargetT >
pcl::WarpPointRigid6D< PointSourceT, PointTargetT >
pcl::visualization::Window
pcl::xNdCopyEigenPointFunctor< PointT >Helper functor structure for copying data between an Eigen::VectorXf and a PointT
pcl::xNdCopyPointEigenFunctor< PointT >Helper functor structure for copying data between an Eigen::VectorXf and a PointT