Point Cloud Library (PCL)  1.7.0
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
pcl::_Axis
pcl::_Intensity
pcl::_Intensity32u
pcl::_Intensity8u
pcl::_Normal
pcl::tracking::_ParticleXYR
pcl::tracking::_ParticleXYRP
pcl::tracking::_ParticleXYRPY
pcl::tracking::_ParticleXYZR
pcl::tracking::_ParticleXYZRPY
pcl::_PointNormal
pcl::_PointSurfel
pcl::_PointWithRange
pcl::_PointWithScale
pcl::_PointWithViewpoint
pcl::_PointXYZ
pcl::_PointXYZHSV
pcl::_PointXYZIA point structure representing Euclidean xyz coordinates, and the intensity value
pcl::_PointXYZINormal
pcl::_PointXYZL
pcl::_PointXYZRGB
pcl::_PointXYZRGBA
pcl::_PointXYZRGBL
pcl::_PointXYZRGBNormal
pcl::_ReferenceFrameA structure representing the Local Reference Frame of a point
pcl::_RGB
pcl::keypoints::agast::AbstractAgastDetectorAbstract detector class for AGAST corner point detectors
AbstractMetadataAbstract interface for outofcore metadata file types
pcl::AdaptiveRangeCoderAdaptiveRangeCoder compression class
pcl::keypoints::internal::AgastApplyNonMaxSuppresion< Out >
pcl::keypoints::internal::AgastApplyNonMaxSuppresion< pcl::PointUV >
pcl::keypoints::internal::AgastDetector< Out >
pcl::keypoints::agast::AgastDetector5_8Detector class for AGAST corner point detector (5_8)
pcl::keypoints::agast::AgastDetector7_12sDetector class for AGAST corner point detector (7_12s)
pcl::keypoints::internal::AgastDetector< pcl::PointUV >
pcl::AgastKeypoint2D< PointInT, PointOutT >Detects 2D AGAST corner points
pcl::AgastKeypoint2D< pcl::PointXYZ, pcl::PointUV >Detects 2D AGAST corner points
pcl::AgastKeypoint2DBase< PointInT, PointOutT, IntensityT >Detects 2D AGAST corner points
pcl::poisson::Allocator< T >This templated class assists in memory allocation and is well suited for instances when it is known that the sequence of memory allocations is performed in a stack-based manner, so that memory allocated last is released first
pcl::poisson::AllocatorState
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::visualization::AreaPickingEvent/brief Class representing 3D area picking events
pcl::FastBilateralFilter< PointT >::Array3D
pcl::ASCIIReaderAscii Point Cloud Reader
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< pcl::PCLPointField::FLOAT32 >
pcl::traits::asType< pcl::PCLPointField::FLOAT64 >
pcl::traits::asType< pcl::PCLPointField::INT16 >
pcl::traits::asType< pcl::PCLPointField::INT32 >
pcl::traits::asType< pcl::PCLPointField::INT8 >
pcl::traits::asType< pcl::PCLPointField::UINT16 >
pcl::traits::asType< pcl::PCLPointField::UINT32 >
pcl::traits::asType< pcl::PCLPointField::UINT8 >
Axes
pcl::AxisA point structure representing an Axis using its normal coordinates
BFGS< FunctorType >BFGS stands for Broyden–Fletcher–Goldfarb–Shanno (BFGS) method for solving unconstrained nonlinear optimization problems
BFGSDummyFunctor< _Scalar, NX >
pcl::BilateralFilter< PointT >A bilateral filter implementation for point cloud data
pcl::BilateralUpsampling< PointInT, PointOutT >Bilateral filtering implementation, based on the following paper: * Kopf, Johannes and Cohen, Michael F
pcl::poisson::BinaryNode< Real >
pcl::BivariatePolynomialT< real >This represents a bivariate polynomial and provides some functionality for it
pcl::BOARDLocalReferenceFrameEstimation< PointInT, PointNT, PointOutT >BOARDLocalReferenceFrameEstimation implements the BOrder Aware Repeatable Directions algorithm for local reference frame estimation as described here:
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::recognition::BVH< UserData >::BoundedObject
pcl::BoundingBoxXYZ
pcl::BoxClipper3D< PointT >Implementation of a box clipper in 3D. Actually it allows affine transformations, thus any parallelepiped in general pose. The affine transformation is used to transform the point before clipping it using the unit cube centered at origin and with an extend of -1 to +1 in each dimension
pcl::segmentation::grabcut::BoykovKolmogorovBoost implementation of Boykov and Kolmogorov's maxflow algorithm doesn't support negative flows which makes it inappropriate for this conext
pcl::search::BruteForce< PointT >Implementation of a simple brute force search algorithm
pcl::poisson::BSplineData< Degree, Real >::BSplineComponents
pcl::poisson::BSplineData< Degree, Real >
pcl::poisson::BSplineElementCoefficients< Degree >
pcl::poisson::BSplineElements< Degree >
pcl::octree::BufferedBranchNode< ContainerT >
pcl::recognition::BVH< UserData >This class is an implementation of bounding volume hierarchies
Camera
pcl::texture_mapping::CameraStructure to store camera pose and focal length
pcl::visualization::CameraCamera class holds a set of camera parameters together with the window pos/size
pcl::io::CameraParametersBasic camera parameters placeholder
pcl::ColorGradientModality< PointInT >::CandidateCandidate for a feature (used in feature extraction methods)
pcl::ColorGradientDOTModality< PointInT >::Candidate
pcl::ColorModality< PointInT >::Candidate
pcl::SurfaceNormalModality< PointInT >::CandidateCandidate for a feature (used in feature extraction methods)
pcl::visualization::context_items::Circle
cJSON
cJSON_Hooks
pcl::Clipper3D< PointT >Base class for 3D clipper objects
cloud_point_index_idx
pcl::visualization::CloudActor
OutofcoreCloud::CloudDataCacheItem
pcl::common::CloudGenerator< PointT, GeneratorT >
pcl::common::CloudGenerator< pcl::PointXY, GeneratorT >
pcl::CloudIterator< PointT >Iterator class for point clouds with or without given indices
pcl::CloudSurfaceProcessing< PointInT, PointOutT >CloudSurfaceProcessing represents the base class for algorithms that takes a point cloud as input and produces a new output cloud that has been modified towards a better surface representation
pcl::visualization::CloudViewerSimple point cloud visualization class
code
pcl::segmentation::grabcut::ColorStructure to save RGB colors into floats
pcl::octree::ColorCoding< PointT >ColorCoding class
pcl::ColorGradientDOTModality< PointInT >
pcl::ColorGradientModality< PointInT >Modality based on max-RGB gradients
pcl::ColorModality< PointInT >
pcl::Comparator< PointT >Comparator is the base class for comparators that compare two points given some function
pcl::keypoints::agast::AbstractAgastDetector::CompareScoreIndexScore index comparator
pcl::ComparisonBase< PointT >The (abstract) base class for the comparison object
pcl::io::CompressionPointTraits< PointT >
pcl::io::CompressionPointTraits< PointXYZRGB >
pcl::io::CompressionPointTraits< PointXYZRGBA >
pcl::ComputeFailedException
pcl::ConcaveHull< PointInT >ConcaveHull (alpha shapes) using libqhull library
pcl::ConditionalEuclideanClustering< PointT >ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition
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::io::configurationProfile_t
pcl::ConstCloudIterator< PointT >Iterator class for point clouds with or without given indices
pcl::ConstCloudIterator< PointT >::ConstIteratorIdx
pcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey3
pcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey5
pcl::poisson::OctNode< NodeData, Real >::ConstNeighbors3
pcl::poisson::OctNode< NodeData, Real >::ConstNeighbors5
boost::container_gen< eigen_listS, ValueType >
boost::container_gen< eigen_vecS, ValueType >
pcl::registration::ConvergenceCriteriaConvergenceCriteria represents an abstract base class for different convergence criteria used in registration loops
pcl::ConvexHull< PointInT >ConvexHull using libqhull library
pcl::filters::Convolution< PointIn, PointOut >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::filters::Convolution3D< PointIn, PointOut, KernelT >Convolution3D handles the non organized case where width and height are unknown or if you are only interested in convolving based on local neighborhood information
pcl::filters::ConvolvingKernel< PointInT, PointOutT >Class ConvolvingKernel base class for all convolving kernels
pcl::filters::ConvolvingKernel< PointT, pcl::Normal >
pcl::filters::ConvolvingKernel< PointT, pcl::PointXY >
pcl::CopyIfFieldExists< PointInT, OutT >A helper functor that can copy a specific value if the given field exists
pcl::poisson::CoredEdgeIndex
pcl::poisson::CoredFileMeshData
pcl::poisson::CoredFileMeshData2
pcl::poisson::CoredMeshData
pcl::poisson::CoredMeshData2
pcl::poisson::CoredPointIndex
pcl::poisson::CoredVectorMeshData
pcl::poisson::CoredVectorMeshData2
pcl::poisson::CoredVertexIndex
pcl::poisson::SortedTreeNodes::CornerIndices
pcl::poisson::SortedTreeNodes::CornerTableData
pcl::CorrespondenceCorrespondence represents a match between two entities (e.g., points, descriptors, etc)
pcl::registration::CorrespondenceEstimation< PointSource, PointTarget, Scalar >CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features
pcl::registration::CorrespondenceEstimationBackProjection< PointSource, PointTarget, NormalT, Scalar >CorrespondenceEstimationBackprojection computes correspondences as points in the target cloud which have minimum
pcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, Scalar >Abstract CorrespondenceEstimationBase class
pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT, Scalar >CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud
pcl::registration::CorrespondenceEstimationOrganizedProjection< PointSource, PointTarget, Scalar >CorrespondenceEstimationOrganizedProjection computes correspondences by projecting the source point cloud onto the target point cloud using the camera intrinsic and extrinsic parameters
pcl::CorrespondenceGrouping< PointModelT, PointSceneT >Abstract base class for Correspondence Grouping algorithms
pcl::registration::CorrespondenceRejectionOrganizedBoundaryImplements a simple correspondence rejection measure
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::CorrespondenceRejectorMedianDistanceCorrespondenceRejectorMedianDistance implements a simple correspondence rejection method based on thresholding based on the median distance between the correspondences
pcl::registration::CorrespondenceRejectorOneToOneCorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences
pcl::registration::CorrespondenceRejectorPoly< SourceT, TargetT >CorrespondenceRejectorPoly implements a correspondence rejection method that exploits low-level and pose-invariant geometric constraints between two point sets by forming virtual polygons of a user-specifiable cardinality on each model using the input correspondences
pcl::registration::CorrespondenceRejectorSampleConsensus< PointT >CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
pcl::registration::CorrespondenceRejectorSampleConsensus2D< PointT >CorrespondenceRejectorSampleConsensus2D implements a pixel-based correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
pcl::registration::CorrespondenceRejectorSurfaceNormalCorrespondenceRejectorSurfaceNormal implements a simple correspondence rejection method based on the angle between the normals at correspondent points
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::registration::CorrespondenceRejectorVarTrimmedCorrespondenceRejectoVarTrimmed implements a simple correspondence rejection method by considering as inliers a certain percentage of correspondences with the least distances
pcl::CovarianceSampling< PointT, PointNT >Point Cloud sampling based on the 6D covariances
pcl::CrfNormalSegmentation< PointT >
pcl::CRHAlignment< PointT, nbins_ >CRHAlignment uses two Camera Roll Histograms (CRH) to find the roll rotation that aligns both views
pcl::CRHEstimation< PointInT, PointNT, PointOutT >CRHEstimation estimates the Camera Roll Histogram (CRH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in:
pcl::CropBox< PointT >CropBox is a filter that allows the user to filter all the data inside of a given box
pcl::CropBox< pcl::PCLPointCloud2 >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
ct_data_s
pcl::poisson::Cube
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:
pcl::recognition::ORROctree::Node::Data
pcl::registration::DataContainer< PointT, NormalT >DataContainer is a container for the input and target point clouds and implements the interface to compute correspondence scores between correspondent points in the input and target clouds
pcl::registration::DataContainerInterfaceDataContainerInterface provides a generic interface for computing correspondence scores between correspondent points in the input and target clouds
pcl::traits::datatype< PointT, Tag >
pcl::io::DeBayerVarious debayering methods
pcl::traits::decomposeArray< T >
pcl::ConstCloudIterator< PointT >::DefaultConstIterator
pcl::registration::DefaultConvergenceCriteria< Scalar >DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the following criteria for registration loop evaluation:
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::DefaultIterator< PointT >
pcl::geometry::DefaultMeshTraits< VertexDataT, HalfEdgeDataT, EdgeDataT, FaceDataT >The mesh traits are used to set up compile time settings for the mesh
pcl::DefaultPointRepresentation< PointDefault >DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types
pcl::DefaultPointRepresentation< FPFHSignature33 >
pcl::DefaultPointRepresentation< Narf36 >
pcl::DefaultPointRepresentation< NormalBasedSignature12 >
pcl::DefaultPointRepresentation< PFHRGBSignature250 >
pcl::DefaultPointRepresentation< PFHSignature125 >
pcl::DefaultPointRepresentation< PointNormal >
pcl::DefaultPointRepresentation< PointXYZ >
pcl::DefaultPointRepresentation< PointXYZI >
pcl::DefaultPointRepresentation< PPFSignature >
pcl::DefaultPointRepresentation< ShapeContext1980 >
pcl::DefaultPointRepresentation< SHOT1344 >
pcl::DefaultPointRepresentation< SHOT352 >
pcl::DefaultPointRepresentation< VFHSignature308 >
pcl::DenseQuantizedMultiModTemplate
pcl::DenseQuantizedSingleModTemplate
openni_wrapper::DepthImageThis class provides methods to fill a depth or disparity image
pcl::LineRGBD< PointXYZT, PointRGBT >::DetectionA LineRGBD detection
openni_wrapper::OpenNIDriver::DeviceContext
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::DifferenceOfNormalsEstimation< PointInT, PointNT, PointOutT >A Difference of Normals (DoN) scale filter implementation for point cloud data
pcl::DinastGrabberGrabber for DINAST devices (i.e., IPA-1002, IPA-1110, IPA-2001)
pcl::visualization::context_items::Disk
pcl::tracking::DistanceCoherence< PointInT >DistanceCoherence computes coherence between two points from the distance between them
pcl::DistanceMapRepresents a distance map obtained from a distance transformation
pcl::DOTMODTemplate matching using the DOTMOD approach
pcl::DOTModality
pcl::DOTMODDetection
pcl::EarClippingThe ear clipping triangulation algorithm
pcl::poisson::Edge
pcl::EdgeAwarePlaneComparator< PointT, PointNT >EdgeAwarePlaneComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
pcl::poisson::EdgeIndex
pcl::geometry::EdgeIndexIndex used to access elements in the half-edge mesh
pcl::poisson::SortedTreeNodes::EdgeIndices
pcl::registration::LUM< PointT >::EdgeProperties
pcl::poisson::SortedTreeNodes::EdgeTableData
boost::eigen_listS
boost::eigen_vecS
pcl::registration::ELCH< PointT >ELCH (Explicit Loop Closing Heuristic) class
pcl::EnergyMapsStores a set of energy maps
pcl::recognition::RotationSpaceCell::Entry
pcl::search::OrganizedNeighbor< PointT >::Entry
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctor
pcl::ESFEstimation< PointInT, PointOutT >ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points
pcl::ESFSignature640A point structure representing the Ensemble of Shape Functions (ESF)
pcl::EuclideanClusterComparator< PointT, PointNT, PointLT >EuclideanClusterComparator is a comparator used for finding clusters supported by planar surfaces
pcl::EuclideanClusterExtraction< PointT >EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
pcl::EuclideanPlaneCoefficientComparator< PointT, PointNT >EuclideanPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
pcl::visualization::ImageViewer::ExitCallback
pcl::visualization::Window::ExitCallback
pcl::visualization::ImageViewer::ExitMainLoopTimerCallback
pcl::visualization::Window::ExitMainLoopTimerCallback
pcl::ExtractIndices< PointT >ExtractIndices extracts a set of indices from a point cloud
pcl::ExtractIndices< pcl::PCLPointCloud2 >ExtractIndices extracts a set of indices from a point cloud
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::geometry::FaceA face is a closed loop of edges
pcl::geometry::FaceAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the face of the outer half-edge (the target)
pcl::geometry::FaceAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the face of the outgoing half-edge (the target)
pcl::geometry::FaceIndexIndex used to access elements in the half-edge mesh
pcl::FastBilateralFilter< PointT >Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and FrŽdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06)
pcl::FastBilateralFilterOMP< PointT >Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and FrÂŽdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06)
pcl::Feature< PointInT, PointOutT >Feature represents the base feature class
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainer< FeatureT >An inner class containing pointers to the source and target feature clouds and the parameters needed to perform the correspondence search
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainerInterface
pcl::FeatureFromLabels< PointInT, PointLT, PointOutT >
pcl::FeatureFromNormals< PointInT, PointNT, PointOutT >
pcl::Narf::FeaturePointRepresentation
pcl::FeatureWithLocalReferenceFrames< PointInT, PointRFT >FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint
pcl::visualization::FEllipticArc2DClass for storing EllipticArc; every ellipse , circle are covered by this
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::FieldMatches< PointT, Tag >
pcl::visualization::Figure2DAbstract class for storing figure information
pcl::FileGrabber< PointT >FileGrabber provides a container-style interface for grabbers which operate on fixed-size input
pcl::FileReaderPoint Cloud Data (FILE) file format reader interface
pcl::FileWriterPoint Cloud Data (FILE) file format writer
pcl::visualization::context_items::FilledRectangle
pcl::Filter< PointT >Filter represents the base filter class
pcl::Filter< pcl::PCLPointCloud2 >Filter represents the base filter class
pcl::FilterIndices< PointT >FilterIndices represents the base class for filters that are about binary point removal
pcl::FilterIndices< pcl::PCLPointCloud2 >FilterIndices represents the base class for filters that are about binary point removal
pcl::search::FlannSearch< PointT, FlannDistance >::FlannIndexCreatorHelper class that creates a FLANN index from a given FLANN matrix
pcl::search::FlannSearch< PointT, FlannDistance >search::FlannSearch is a generic FLANN wrapper class for the new search interface
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::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::FPoints2DClass for storing Points
pcl::visualization::FPolygon2DClass for Polygon
pcl::visualization::FPolyLine2DClass for PolyLine
pcl::visualization::FQuad2DClass for storing Quads
pcl::FrustumCulling< PointT >FrustumCulling filters points inside a frustum given by pose and field of view of the camera
pcl::poisson::FunctionData< Degree, Real >
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::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::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::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::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::segmentation::grabcut::GaussianGaussian structure
pcl::segmentation::grabcut::GaussianFitterHelper class that fits a single Gaussian to color samples
pcl::filters::GaussianKernel< PointInT, PointOutT >Gaussian kernel implementation interface Use this as implementation reference
pcl::GaussianKernelClass GaussianKernel assembles all the method for computing, convolving, smoothing, gradients computing an image using a gaussian kernel
pcl::filters::GaussianKernelRGB< PointInT, PointOutT >Gaussian kernel implementation interface with RGB channel handling Use this as implementation reference
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::GeometricConsistencyGrouping< PointModelT, PointSceneT >Class implementing a 3D correspondence grouping enforcing geometric consistency among feature correspondences
Geometry
pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >GFPFHEstimation estimates the Global Fast Point Feature Histogram (GFPFH) descriptor for a given point cloud dataset containing points and labels
pcl::GFPFHSignature16A point structure representing the GFPFH descriptor with 16 bins
pcl::GlobalHypothesesVerification< ModelT, SceneT >A hypothesis verification method proposed in "A Global Hypotheses Verification Method for 3D Object Recognition", A
pcl::segmentation::grabcut::GMM
pcl::GrabberGrabber interface for PCL 1.x device drivers
pcl::GrabCut< PointT >Implementation of the GrabCut segmentation in "GrabCut — Interactive Foreground Extraction using Iterated Graph Cuts" by Carsten Rother, Vladimir Kolmogorov and Andrew Blake
pcl::GradientXYA point structure representing Euclidean xyz coordinates, and the intensity value
pcl::registration::GraphHandler< GraphT >GraphHandler class is a wrapper for a general SLAM graph The actual graph class must fulfil the following boost::graph concepts:
pcl::registration::GraphOptimizer< GraphT >GraphOptimizer class; derive and specialize for each graph type
pcl::GraphRegistration< GraphT >GraphRegistration class is the base class for graph-based registration methods
pcl::GreedyProjectionTriangulation< PointInT >GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections
pcl::GreedyVerification< ModelT, SceneT >A greedy hypothesis verification method
Grid
pcl::GridProjection< PointNT >Grid projection surface reconstruction method
pcl::people::GroundBasedPeopleDetectionApp< PointT >
pcl::GroundPlaneComparator< PointT, PointNT >GroundPlaneComparator is a Comparator for detecting smooth surfaces suitable for driving
gz_header_s
pcl::geometry::HalfEdgeAn edge is a connection between two vertices
pcl::geometry::HalfEdgeIndexIndex used to access elements in the half-edge mesh
pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >HarrisKeypoint2D detects Harris corners family points
pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals
pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >Keypoint detector for detecting corners in 3D (XYZ), 2D (intensity) AND mixed versions of these
__gnu_cxx::hash< const long long >
__gnu_cxx::hash< const unsigned long long >
__gnu_cxx::hash< long long >
__gnu_cxx::hash< unsigned long long >
pcl::PPFHashMapSearch::HashKeyStructData structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class
pcl::HDLGrabber::HDLDataPacket
pcl::HDLGrabber::HDLFiringData
pcl::HDLGrabberGrabber for the Velodyne High-Definition-Laser (HDL)
pcl::HDLGrabber::HDLLaserCorrection
pcl::HDLGrabber::HDLLaserReturn
pcl::people::HeadBasedSubclustering< PointT >
pcl::people::HeightMap2D< PointT >
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::people::HOGHOG represents a class for computing the HOG descriptor described in Dalal, N
pcl::Hough3DGrouping< PointModelT, PointSceneT, PointModelRfT, PointSceneRfT >Class implementing a 3D correspondence grouping algorithm that can deal with multiple instances of a model template found into a given scene
pcl::recognition::HoughSpace3DHoughSpace3D is a 3D voting space
pcl::tracking::HSVColorCoherence< PointInT >HSVColorCoherence computes coherence between the two points from the color difference between them
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::HuberPenalty
pcl::recognition::Hypothesis
pcl::recognition::HypothesisBase
pcl::recognition::ObjRecRANSAC::HypothesisCreator
pcl::HypothesisVerification< ModelT, SceneT >Abstract class for hypotheses verification methods
openni_wrapper::ImageImage class containing just a reference to image meta data
openni_wrapper::ImageBayerGRBGThis class provides methods to fill a RGB or Grayscale image buffer from underlying Bayer pattern image
pcl::ImageGrabber< PointT >
pcl::ImageGrabberBaseBase class for Image file grabber
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
pcl::visualization::ImageViewerInteractorStyleAn image viewer interactor style, tailored for ImageViewer
openni_wrapper::ImageYUV422Concrete implementation of the interface Image for a YUV 422 image used by Primesense devices
pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >This class implements Implicit Shape Model algorithm described in "Hough Transforms and 3D SURF for robust three dimensional classication" by Jan Knopp1, Mukta Prasad, Geert Willems1, Radu Timofte, and Luc Van Gool
pcl::geometry::IncomingHalfEdgeAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the incoming half-edge (the target)
inflate_state
pcl::InitFailedExceptionAn exception thrown when init can not be performed should be used in all the PCLBase class inheritants
pcl::geometry::InnerHalfEdgeAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the inner half-edge (the target)
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 organized 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::IntensityA point structure representing the grayscale intensity in single-channel images
pcl::Intensity32uA point structure representing the grayscale intensity in single-channel images
pcl::Intensity8uA point structure representing the grayscale intensity in single-channel images
pcl::common::IntensityFieldAccessor< PointT >
pcl::common::IntensityFieldAccessor< pcl::PointNormal >
pcl::common::IntensityFieldAccessor< pcl::PointXYZ >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGB >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBA >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBL >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBNormal >
pcl::IntensityGradientA point structure representing the intensity gradient of an XYZI point cloud
pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT >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::InterestPointA point structure representing an interest point with Euclidean xyz coordinates, and an interest value
internal_state
pcl::intersect< Sequence1, Sequence2 >
pcl::InvalidConversionExceptionAn exception that is thrown when a PCLPointCloud2 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
boost::detail::is_random_access< eigen_listS >
boost::detail::is_random_access< eigen_vecS >
pcl::PosesFromMatches::PoseEstimate::IsBetter
pcl::features::ISMModelThe assignment of this structure is to store the statistical/learned weights and other information of the trained Implict Shape Model algorithm
pcl::ISMPeakThis struct is used for storing peak
pcl::features::ISMVoteList< PointT >This class is used for storing, analyzing and manipulating votes obtained from ISM algorithm
pcl::IsNotDenseExceptionAn exception that is thrown when a PointCloud is not dense but is attemped to be used as dense
pcl::ISSKeypoint3D< PointInT, PointOutT, NormalT >ISSKeypoint3D detects the Intrinsic Shape Signatures keypoints for a given point cloud
pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm
pcl::IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar >IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend
pcl::IterativeClosestPointWithNormals< PointSource, PointTarget, Scalar >IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformation estimated based on Point to Plane distances by default
pcl::IteratorIdx< PointT >
pcl::octree::IteratorState
pcl::search::KdTree< PointT >search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure
pcl::KdTree< PointT >KdTree represents the base spatial locator class for kd-tree implementations
pcl::KdTreeFLANN< PointT, Dist >KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures
pcl::search::FlannSearch< PointT, FlannDistance >::KdTreeIndexCreatorCreates a FLANN KdTreeSingleIndex from the given input data
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
kiss_fft_cpx
kiss_fft_state
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::search::FlannSearch< PointT, FlannDistance >::KMeansIndexCreatorCreates a FLANN KdTreeSingleIndex from the given input data
pcl::Label
pcl::LabeledEuclideanClusterExtraction< PointT >LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info
pcl::VoxelGridCovariance< PointT >::LeafSimple structure to hold a centroid, covarince and the number of points in a leaf
pcl::GridProjection< PointNT >::LeafData leaf
pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid::Leaf
pcl::UniformSampling< PointInT >::LeafSimple structure to hold an nD centroid and the number of points in a leaf
pcl::LeastMedianSquares< PointT >LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm
pcl::visualization::context_items::Line
pcl::LinearizedMapsStores a set of linearized maps
pcl::LinearLeastSquaresNormalEstimation< PointInT, PointOutT >Surface normal estimation on dense data using a least-squares estimation based on a first-order Taylor approximation
pcl::LineIteratorOrganized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm
pcl::LINEMODTemplate matching using the LINEMOD approach
pcl::LINEMOD_OrientationMapMap that stores orientations
pcl::LINEMODDetectionRepresents a detection of a template using the LINEMOD approach
pcl::LineRGBD< PointXYZT, PointRGBT >High-level class for template matching using the LINEMOD approach based on RGB and Depth data
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::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::LocationInfoThis structure stores the information about the keypoint
LRUCache< KeyT, CacheItemT >
LRUCacheItem< T >
pcl::registration::LUM< PointT >Globally Consistent Scan Matching based on an algorithm by Lu and Milios
pcl::io::LZFBayer8ImageReaderPCL-LZF 8-bit Bayer image format reader
pcl::io::LZFBayer8ImageWriterPCL-LZF 8-bit Bayer image format writer
pcl::io::LZFDepth16ImageReaderPCL-LZF 16-bit depth image format reader
pcl::io::LZFDepth16ImageWriterPCL-LZF 16-bit depth image format writer
pcl::io::LZFImageReaderPCL-LZF image format reader
pcl::io::LZFImageWriterPCL-LZF image format writer
pcl::io::LZFRGB24ImageReaderPCL-LZF 24-bit RGB image format reader
pcl::io::LZFRGB24ImageWriterPCL-LZF 24-bit RGB image format writer
pcl::io::LZFYUV422ImageReaderPCL-LZF 8-bit Bayer image format reader
pcl::io::LZFYUV422ImageWriterPCL-LZF 16-bit YUV422 image format writer
ON_SerialNumberMap::MAP_VALUE
pcl::poisson::MapReduceVector< T2 >
pcl::poisson::MarchingCubes
pcl::MarchingCubes< PointNT >The marching cubes surface reconstruction algorithm
pcl::MarchingCubesHoppe< PointNT >The marching cubes surface reconstruction algorithm, using a signed distance function based on the distance from tangent planes, proposed by Hoppe et
pcl::MarchingCubesRBF< PointNT >The marching cubes surface reconstruction algorithm, using a signed distance function based on radial basis functions
pcl::poisson::MarchingSquares
pcl::MaskMap
pcl::poisson::MatrixEntry< T >
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::MedianFilter< PointT >Implementation of the median filter
Mesh
pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >Base class for the half-edge mesh
pcl::MeshConstruction< PointInT >MeshConstruction represents a base surface reconstruction class
pcl::geometry::MeshIO< MeshT >Read / write the half-edge mesh from / to a file
pcl::MeshProcessingMeshProcessing represents the base class for mesh processing algorithms
pcl::MeshQuadricDecimationVTKPCL mesh decimation based on vtkQuadricDecimation from the VTK library
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::poisson::MinimalAreaTriangulation< Real >
pcl::MovingLeastSquares< PointInT, PointOutT >::MLSResultData structure used to store the results of the MLS fitting
pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGridA minimalistic implementation of a voxel grid, necessary for the point cloud upsampling
pcl::OpenNIGrabber::modeComp
pcl::recognition::ModelLibrary::ModelStores some information about the model
pcl::ModelCoefficients
pcl::recognition::ModelLibrary
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
MonitorQueue< DataT >
pcl::visualization::MouseEvent
pcl::MovingLeastSquares< PointInT, PointOutT >MovingLeastSquares represent an 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::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >::MyPointRepresentationInternal point representation uses only 3D coordinates for L2
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 See B
pcl::NarfKeypointNARF (Normal Aligned Radial Feature) keypoints
pcl::NdCentroidFunctor< PointT, Scalar >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::ndt2d::NDT2D< PointT >Build a Normal Distributions Transform of a 2D point cloud
pcl::ndt2d::NDTSingleGrid< PointT >Build a set of normal distributions modelling a 2D point cloud, and provide the value and derivatives of the model at any point via the test (...) function
pcl::tracking::NearestPairPointCloudCoherence< PointInT >NearestPairPointCloudCoherence computes coherence between two pointclouds using the nearest point pairs
pcl::poisson::OctNode< NodeData, Real >::NeighborKey3
pcl::poisson::OctNode< NodeData, Real >::NeighborKey5
pcl::poisson::OctNode< NodeData, Real >::Neighbors3
pcl::poisson::OctNode< NodeData, Real >::Neighbors5
pcl::GrabCut< PointT >::NLinks
pcl::geometry::NoDataNo data is associated with the vertices / half-edges / edges / faces
pcl::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar >::Node
pcl::recognition::BVH< UserData >::Node
pcl::recognition::ORRGraph< NodeData >::Node
pcl::recognition::ORROctree::Node
pcl::NormalA point structure representing normal coordinates and the surface curvature estimate
pcl::common::normal_distribution< T >Normal distribution
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::ndt2d::NormalDist< PointT >A normal distribution estimation class
pcl::NormalDistributionsTransform< PointSource, PointTarget >A 3D Normal Distribution Transform registration implementation for point cloud data
pcl::NormalDistributionsTransform2D< PointSource, PointTarget >NormalDistributionsTransform2D provides an implementation of the Normal Distributions Transform algorithm for scan matching
pcl::NormalEstimation< PointInT, PointOutT >NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point
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::common::NormalGenerator< T >NormalGenerator class generates a random number from a normal distribution specified by (mean, sigma)
pcl::NormalRefinement< NormalT >Normal vector refinement class
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::registration::NullEstimateNullEstimate struct
pcl::registration::NullMeasurementNullMeasurement struct
Eigen::NumTraits< pcl::ndt2d::NormalDist< PointT > >
pcl::poisson::NVector< T, Dim >
pcl::keypoints::agast::OastDetector9_16Detector class for AGAST corner point detector (OAST 9_16)
Object
ObjectFeatures
ObjectModel
ObjectRecognition
ObjectRecognitionParameters
pcl::recognition::ObjRecRANSACThis is a RANSAC-based 3D object recognition method
pcl::poisson::OctNode< NodeData, Real >
pcl::poisson::Octree< Degree >
pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure
pcl::octree::Octree2BufBase< LeafContainerT, BranchContainerT >Octree double buffer class
pcl::octree::OctreeBase< LeafContainerT, BranchContainerT >Octree class
pcl::octree::OctreeBranchNode< ContainerT >Abstract octree branch class
pcl::octree::OctreeBreadthFirstIterator< OctreeT >Octree iterator class
pcl::octree::OctreeContainerBaseOctree container class that can serve as a base to construct own leaf node container classes
pcl::octree::OctreeContainerEmptyOctree container class that does not store any information
pcl::octree::OctreeContainerPointIndexOctree container class that does store a single point index
pcl::octree::OctreeContainerPointIndicesOctree container class that does store a vector of point indices
pcl::octree::OctreeDepthFirstIterator< OctreeT >Octree iterator class
pcl::octree::OctreeIteratorBase< OctreeT >Abstract octree iterator class
pcl::octree::OctreeKeyOctree key class
pcl::octree::OctreeLeafNode< ContainerT >Abstract octree leaf class
pcl::octree::OctreeLeafNodeIterator< OctreeT >Octree leaf node iterator class
pcl::octree::OctreeNodeAbstract octree node class
pcl::octree::OctreeNodePool< NodeT >Octree node pool
pcl::octree::OctreePointCloud< PointT, LeafContainerT, BranchContainerT, OctreeT >Octree pointcloud class
pcl::octree::OctreePointCloudAdjacency< PointT, LeafContainerT, BranchContainerT >Octree pointcloud voxel class used for adjacency calculation
pcl::octree::OctreePointCloudAdjacencyContainer< PointInT, DataT >Octree adjacency leaf container class- stores set of pointers to neighbors, number of points added, and a DataT value
pcl::octree::OctreePointCloudChangeDetector< PointT, LeafContainerT, BranchContainerT >Octree pointcloud change detector class
pcl::io::OctreePointCloudCompression< PointT, LeafT, BranchT, OctreeT >Octree pointcloud compression class
pcl::octree::OctreePointCloudDensity< PointT, LeafContainerT, BranchContainerT >Octree pointcloud density class
pcl::octree::OctreePointCloudDensityContainerOctree pointcloud density leaf node class
pcl::octree::OctreePointCloudOccupancy< PointT, LeafContainerT, BranchContainerT >Octree pointcloud occupancy class
pcl::octree::OctreePointCloudPointVector< PointT, LeafContainerT, BranchContainerT, OctreeT >Octree pointcloud point vector class
pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >Octree pointcloud search class
pcl::octree::OctreePointCloudSinglePoint< PointT, LeafContainerT, BranchContainerT, OctreeT >Octree pointcloud single point class
pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafContainerT, BranchContainerT >Octree pointcloud voxel centroid class
pcl::octree::OctreePointCloudVoxelCentroidContainer< PointT >Octree pointcloud voxel centroid leaf node class
OctreeT
pcl::traits::offset< PointT, Tag >
ON_2dexMap
ON_2dPoint
ON_2dPointArray
ON_2dVector
ON_2dVectorArray
ON_2fPoint
ON_2fPointArray
ON_2fVector
ON_2fVectorArray
ON_3DM_BIG_CHUNK
ON_3DM_CHUNK
ON_3dmAnnotationSettings
ON_3dmApplication
ON_3dmConstructionPlane
ON_3dmConstructionPlaneGridDefaults
ON_3dmGoo
ON_3dmIOSettings
ON_3dmNotes
ON_3dmObjectAttributes
ON_3dmPageSettings
ON_3dmProperties
ON_3dmRenderSettings
ON_3dmRevisionHistory
ON_3dmSettings
ON_3dmUnitsAndTolerances
ON_3dmView
ON_3dmViewPosition
ON_3dmViewTraceImage
ON_3dmWallpaperImage
ON_3dPoint
ON_3dPointArray
ON_3dRay
ON_3dVector
ON_3dVectorArray
ON_3fPoint
ON_3fPointArray
ON_3fVector
ON_3fVectorArray
ON_4dPoint
ON_4dPointArray
ON_4fPoint
ON_4fPointArray
ON_AngularDimension
ON_AngularDimension2
ON_Annotation
ON_Annotation2
ON_Annotation2Text
ON_AnnotationArrow
ON_AnnotationTextDot
ON_Arc
ON_ArcCurve
ON_Base64EncodeStream
ON_BezierCage
ON_BezierCageMorph
ON_BezierCurve
ON_BezierSurface
ON_BinaryArchive
ON_BinaryArchiveBuffer
ON_BinaryFile
ON_Bitmap
ON_BoundingBox
ON_Box
ON_Brep
ON_BrepEdge
ON_BrepEdgeArray
ON_BrepFace
ON_BrepFaceArray
ON_BrepFaceSide
ON_BrepFaceSideArray
ON_BrepLoop
ON_BrepLoopArray
ON_BrepRegion
ON_BrepRegionArray
ON_BrepRegionTopology
ON_BrepTrim
ON_BrepTrimArray
ON_BrepTrimPoint
ON_BrepVertex
ON_BrepVertexArray
ON_Buffer
ON_BumpFunction
ON_CageMorph
ON_CheckSum
ON_Circle
ON_ClassArray< T >
ON_ClassId
ON_ClippingPlane
ON_ClippingPlaneInfo
ON_ClippingPlaneSurface
ON_ClippingRegion
ON_Color
ON_CompressedBuffer
ON_CompressStream
ON_Cone
ON_Curve
ON_CurveArray
ON_CurveOnSurface
ON_CurveProxy
ON_CurveProxyHistory
ON_Cylinder
ON_DecodeBase64
ON_DetailView
ON_DimensionExtra
ON_DimStyle
ON_DisplayMaterialRef
ON_DocumentUserStringList
ON_EarthAnchorPoint
ON_Ellipse
ON_EmbeddedBitmap
ON_EmbeddedFile
ON_Evaluator
ON_Extrusion
ON_FileIterator
ON_FileStream
ON_FixedSizePool
ON_FixedSizePoolIterator
ON_Font
ON_Geometry
ON_Group
ON_Hatch
ON_HatchLine
ON_HatchLoop
ON_HatchPattern
ON_HistoryRecord
ON_InstanceDefinition
ON_InstanceRef
ON_Interval
ON_Layer
ON_Leader
ON_Leader2
ON_Light
ON_Line
ON_LinearDimension
ON_LinearDimension2
ON_LineCurve
ON_Linetype
ON_LinetypeSegment
ON_Localizer
ON_LocalZero1
ON_MappingChannel
ON_MappingRef
ON_MappingTag
ON_Material
ON_MaterialRef
ON_Matrix
ON_Mesh
ON_MeshCurvatureStats
ON_MeshCurveParameters
ON_MeshEdgeRef
ON_MeshFace
ON_MeshFaceRef
ON_MeshFaceSide
ON_MeshNgon
ON_MeshNgonList
ON_MeshParameters
ON_MeshPart
ON_MeshPartition
ON_MeshTopology
ON_MeshTopologyEdge
ON_MeshTopologyFace
ON_MeshTopologyVertex
ON_MeshVertexRef
ON_MorphControl
ON_NurbsCage
ON_NurbsCurve
ON_NurbsSurface
ON_Object
ON_ObjectArray< T >
ON_ObjectRenderingAttributes
ON_ObjRef
ON_ObjRef_IRefID
ON_ObjRefEvaluationParameter
ON_OffsetSurface
ON_OffsetSurfaceFunction
ON_OffsetSurfaceValue
ON_OrdinateDimension2
ON_Plane
ON_PlaneEquation
ON_PlaneSurface
ON_PlugInRef
ON_Point
ON_PointCloud
ON_PointGrid
ON_PolyCurve
ON_PolyEdgeCurve
ON_PolyEdgeHistory
ON_PolyEdgeSegment
ON_Polyline
ON_PolylineCurve
ON_PolynomialCurve
ON_PolynomialSurface
ON_RadialDimension
ON_RadialDimension2
ON_RANDOM_NUMBER_CONTEXT
ON_Read3dmBufferArchive
ON_RenderingAttributes
ON_RevSurface
ON_RTree
ON_RTreeBBox
ON_RTreeBranch
ON_RTreeCapsule
ON_RTreeIterator
ON_RTreeLeaf
ON_RTreeMemPool
ON_RTreeNode
ON_RTreeSearchResult
ON_RTreeSphere
ON_SerialNumberMap
ON_SimpleArray< T >
ON_SimpleFixedSizePool< T >
ON_SpaceMorph
ON_Sphere
ON_String
ON_Sum
ON_SumSurface
ON_Surface
ON_SurfaceArray
ON_SurfaceCurvature
ON_SurfaceProperties
ON_SurfaceProxy
ON_TensorProduct
ON_TextDot
ON_TextEntity
ON_TextEntity2
ON_TextExtra
ON_TextLog
ON_Texture
ON_TextureCoordinates
ON_TextureMapping
ON_Torus
ON_U
ON_UncompressStream
ON_UnicodeErrorParameters
ON_UnitSystem
ON_UnknownUserData
ON_UserData
ON_UserDataHolder
ON_UserString
ON_UserStringList
ON_UUID
ON_UuidIndexList
ON_UuidList
ON_UuidPair
ON_UuidPairList
ON_Viewport
ON_WindowsBitmap
ON_WindowsBitmapEx
ON_WindowsBITMAPINFO
ON_WindowsBITMAPINFOHEADER
ON_WindowsRGBQUAD
ON_Workspace
ON_Write3dmBufferArchive
ON_wString
ON_Xform
pcl::ONIGrabberA simple ONI grabber
ONX_Model
ONX_Model_Object
ONX_Model_RenderLight
ONX_Model_UserData
OpenNICapture
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::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctor
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctor
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices
pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices
pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndicesOptimization functor structure
pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >OrganizedConnectedComponentSegmentation allows connected components to be found within organized point cloud data, given a comparison function
pcl::io::OrganizedConversion< PointT, false >
pcl::io::OrganizedConversion< PointT, true >
pcl::OrganizedFastMesh< PointInT >Simple triangulation/surface reconstruction for organized point clouds
pcl::OrganizedIndexIteratorBase class for iterators on 2-dimensional maps like images/organized clouds etc
pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT >OrganizedMultiPlaneSegmentation finds all planes present in the input cloud, and outputs a vector of plane equations, as well as a vector of point clouds corresponding to the inliers of each detected plane
pcl::search::OrganizedNeighbor< PointT >OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds
pcl::io::OrganizedPointCloudCompression< PointT >
pcl::recognition::ObjRecRANSAC::OrientedPointPair
pcl::recognition::ORRGraph< NodeData >
pcl::recognition::ORROctreeThat's a very specialized and simple octree class
pcl::recognition::ORROctreeZProjection
pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >OURCVFHEstimation estimates the Oriented, Unique and Repetable Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset given XYZ data and normals, as presented in:
pcl::geometry::OuterHalfEdgeAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the outer half-edge (the target)
pcl::geometry::OutgoingHalfEdgeAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the outgoing half-edge (the target)
pcl::outofcore::OutofcoreAbstractMetadata
pcl::outofcore::OutofcoreAbstractNodeContainer< PointT >
pcl::outofcore::OutofcoreBreadthFirstIterator< PointT, ContainerT >
OutofcoreCloud
pcl::outofcore::OutofcoreDepthFirstIterator< PointT, ContainerT >
pcl::outofcore::OutofcoreIteratorBase< PointT, ContainerT >Abstract octree iterator class
pcl::outofcore::OutofcoreOctreeBase< ContainerT, PointT >This code defines the octree used for point storage at Urban Robotics
pcl::outofcore::OutofcoreOctreeBaseMetadataEncapsulated class to read JSON metadata into memory, and write the JSON metadata associated with the octree root node
pcl::outofcore::OutofcoreOctreeBaseNode< ContainerT, PointT >OutofcoreOctreeBaseNode Class internally representing nodes of an outofcore octree, with accessors to its data via the octree_disk_container class or octree_ram_container class, whichever it is templated against
pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >Class responsible for serialization and deserialization of out of core point data
pcl::outofcore::OutofcoreOctreeNodeMetadataEncapsulated class to read JSON metadata into memory, and write the JSON metadata for each node
pcl::outofcore::OutofcoreOctreeRamContainer< PointT >Storage container class which the outofcore octree base is templated against
pcl::outofcore::OutofcoreParams
pcl::recognition::ObjRecRANSAC::OutputThis is an output item of the ObjRecRANSAC::recognize() method
pcl::PackedHSIComparison< PointT >A packed HSI specialization of the comparison object
pcl::PackedRGBComparison< PointT >A packed rgb specialization of the comparison object
pcl::PairwiseGraphRegistration< GraphT, PointT >PairwiseGraphRegistration class aligns the clouds two by two
pcl::PapazovHV< ModelT, SceneT >A hypothesis verification method proposed in "An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes", C
boost::parallel_edge_traits< eigen_listS >
boost::parallel_edge_traits< eigen_vecS >
pcl::RangeImageBorderExtractor::ParametersParameters used in this class
pcl::PolynomialCalculationsT< real >::ParametersParameters used in this class
pcl::common::UniformGenerator< T >::Parameters
pcl::NarfDescriptor::Parameters
pcl::NarfKeypoint::ParametersParameters used in this class
BFGS< FunctorType >::Parameters
pcl::PosesFromMatches::ParametersParameters used in this class
pcl::common::NormalGenerator< T >::Parameters
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::ParticleXYR
pcl::tracking::ParticleXYRP
pcl::tracking::ParticleXYRPY
pcl::tracking::ParticleXYZR
pcl::tracking::ParticleXYZRPY
pcl::PassThrough< PointT >PassThrough passes points in a cloud based on constraints for one particular field of the point type
pcl::PassThrough< pcl::PCLPointCloud2 >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
OutofcoreCloud::PcdQueueItem
pcl::PCDReaderPoint Cloud Data (PCD) file format reader
pcl::PCDWriterPoint Cloud Data (PCD) file format writer
pcl::PCLBase< PointT >PCL base class
pcl::PCLBase< pcl::PCLPointCloud2 >
pcl::visualization::PCLContextImageItemStruct PCLContextImageItem a specification of vtkContextItem, used to add an image to the scene in the ImageViewer class
pcl::visualization::PCLContextItemStruct PCLContextItem represents our own custom version of vtkContextItem, used by the ImageViewer class
pcl::PCLExceptionA base class for all pcl exceptions which inherits from std::runtime_error
pcl::PCLHeader
pcl::visualization::PCLHistogramVisualizerPCL histogram visualizer main class
pcl::visualization::PCLHistogramVisualizerInteractorStylePCL histogram visualizer interactory style class
pcl::PCLImage
pcl::visualization::PCLImageCanvasSource2DPCLImageCanvasSource2D represents our own custom version of vtkImageCanvasSource2D, used by the ImageViewer class
pcl::visualization::PCLPainter2DPCL Painter2D main class
pcl::visualization::PCLPlotterPCL Plotter main class
pcl::PCLPointCloud2
pcl::PCLPointField
pcl::visualization::PCLSimpleBufferVisualizerPCL simple buffer visualizer main class
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::people::PersonClassifier< PointT >
pcl::people::PersonCluster< PointT >
pcl::PFHEstimation< PointInT, PointNT, PointOutT >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::recognition::ORROctreeZProjection::Pixel
pcl::PlanarPolygon< PointT >PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space
pcl::PlanarPolygonFusion< PointT >PlanarPolygonFusion takes a list of 2D planar polygons and attempts to reduce them to a minimum set that best represents the scene, based on various given comparators
pcl::PlanarRegion< PointT >PlanarRegion represents a set of points that lie in a plane
pcl::PlaneClipper3D< PointT >Implementation of a plane clipper in 3D
pcl::PlaneCoefficientComparator< PointT, PointNT >PlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
pcl::PlaneRefinementComparator< PointT, PointNT, PointLT >PlaneRefinementComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
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::visualization::context_items::Point
pcl::poisson::Point3D< Real >
pcl::PointCloud< PointT >PointCloud represents the base class in PCL for storing collections of 3D points
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< pcl::PCLPointCloud2 >Base Handler class for PointCloud colors
pcl::visualization::PointCloudColorHandlerCustom< PointT >Handler for predefined user colors
pcl::visualization::PointCloudColorHandlerCustom< pcl::PCLPointCloud2 >Handler for predefined user colors
pcl::visualization::PointCloudColorHandlerGenericField< PointT >Generic field handler class for colors
pcl::visualization::PointCloudColorHandlerGenericField< pcl::PCLPointCloud2 >Generic field handler class for colors
pcl::visualization::PointCloudColorHandlerHSVField< PointT >HSV handler class for colors
pcl::visualization::PointCloudColorHandlerHSVField< pcl::PCLPointCloud2 >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< pcl::PCLPointCloud2 >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< pcl::PCLPointCloud2 >RGB handler class for colors
pcl::visualization::PointCloudGeometryHandler< PointT >Base handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandler< pcl::PCLPointCloud2 >Base handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerCustom< PointT >Custom handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerCustom< pcl::PCLPointCloud2 >Custom handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT >Surface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< pcl::PCLPointCloud2 >Surface normal handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZ< PointT >XYZ handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerXYZ< pcl::PCLPointCloud2 >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
pcl::PointIndices
pcl::PointNormalA point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate
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::PointRGBA point structure for representing RGB color
pcl::visualization::context_items::Points
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::PointUVA 2D point structure representing pixel image coordinates
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::PointXYZL
pcl::PointXYZRGBA point structure representing Euclidean xyz coordinates, and the RGB color
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::Poisson< PointNT >The Poisson surface reconstruction algorithm
pcl::visualization::context_items::Polygon
pcl::geometry::PolygonMesh< MeshTraitsT >General half-edge mesh that can store any polygon with a minimum number of vertices of 3
pcl::PolygonMesh
pcl::geometry::PolygonMeshTagTag describing the type of the mesh
pcl::poisson::Polynomial< Degree >
pcl::PolynomialCalculationsT< real >This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials
Eigen::PolynomialSolver< _Scalar, 2 >
pcl::PosesFromMatches::PoseEstimateA result of the pose estimation process
pcl::registration::PoseEstimate< PointT >PoseEstimate struct
pcl::registration::PoseMeasurement< VertexT, InformationT >PoseMeasurement struct
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::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::poisson::PPolynomial< Degree >
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::PrincipalRadiiRSDA point structure representing the minimum and maximum surface radii (in meters) computed using RSD
pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioBranchQueueEntryPriority queue entry for branch nodes
pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioPointQueueEntryPriority queue entry for point candidates
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< pcl::PCLPointCloud2 >ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
pcl::filters::Pyramid< PointT >Pyramid constructs a multi-scale representation of an organised point cloud
pcl::PyramidFeatureHistogram< PointFeature >Class that compares two sets of features by using a multiscale representation of the features inside a pyramid
pcl::geometry::QuadMesh< MeshTraitsT >Half-edge mesh that can only store quads
pcl::geometry::QuadMeshTagTag describing the type of the mesh
pcl::QuantizableModalityInterface for a quantizable modality
pcl::QuantizedMap
pcl::QuantizedMultiModFeatureFeature that defines a position and quantized value in a specific modality
pcl::QuantizedNormalLookUpTableLook-up-table for fast surface normal quantization
pcl::RadiusOutlierRemoval< PointT >RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have
pcl::RadiusOutlierRemoval< pcl::PCLPointCloud2 >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< pcl::PCLPointCloud2 >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::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::RangeImageSphericalRangeImageSpherical is derived from the original range image and uses a slightly different spherical projection
pcl::visualization::RangeImageVisualizerRange image visualizer class
pcl::visualization::context_items::Rectangle
pcl::ReferenceFrame
pcl::Region3D< PointT >Region3D represents summary statistics of a 3D collection of points
pcl::RegionGrowing< PointT, NormalT >Implements the well known Region Growing algorithm used for segmentation
pcl::RegionGrowingRGB< PointT, NormalT >Implements the well known Region Growing algorithm used for segmentation based on color of points
pcl::RegionXYDefines a region in XY-space
pcl::Registration< PointSource, PointTarget, Scalar >Registration represents the base registration class for general purpose, ICP-like methods
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::TexMaterial::RGB
pcl::RGBA structure representing RGB color information
pcl::RGBPlaneCoefficientComparator< PointT, PointNT >RGBPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
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::recognition::RigidTransformSpace
pcl::RobotEyeGrabberGrabber for the Ocular Robotics RobotEye sensor
pcl::poisson::RootInfo
pcl::recognition::RotationSpaceThis is a class for a discrete representation of the rotation space based on the axis-angle representation
pcl::recognition::RotationSpaceCell
pcl::recognition::RotationSpaceCellCreator
pcl::recognition::RotationSpaceCreator
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::SampleConsensusModelCircle3D< PointT >SampleConsensusModelCircle3D defines a model for 3D circle segmentation
pcl::SampleConsensusModelCone< PointT, PointNT >SampleConsensusModelCone defines a model for 3D cone segmentation
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::SampleConsensusModelNormalSphere< PointT, PointNT >SampleConsensusModelNormalSphere defines a model for 3D sphere 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::SampleConsensusModelRegistration2D< PointT >SampleConsensusModelRegistration2D defines a model for Point-To-Point registration outlier rejection using distances between 2D pixels
pcl::SampleConsensusModelSphere< PointT >SampleConsensusModelSphere defines a model for 3D sphere segmentation
pcl::SampleConsensusModelStick< PointT >SampleConsensusModelStick defines a model for 3D stick segmentation
pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >Pose estimation and alignment class using a prerejective RANSAC routine
pcl::SamplingSurfaceNormal< PointT >SamplingSurfaceNormal divides the input space into grids until each grid contains a maximum of N points, and samples points randomly within each grid
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
Scene
pcl::ScopeTimeClass to measure the time spent in a scope
pcl::keypoints::agast::AbstractAgastDetector::ScoreIndexStructure holding an index and the associated keypoint score
pcl::search::Search< PointT >Generic search class
pcl::SeededHueSegmentationSeededHueSegmentation
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::recognition::ORROctreeZProjection::Set
pcl::SetIfFieldExists< PointOutT, InT >A helper functor that can set a specific value in a field if the field exists
pcl::RangeImageBorderExtractor::ShadowBorderIndicesStores the indices of the shadow border corresponding to obstacle borders
pcl::ShadowPoints< PointT, NormalT >ShadowPoints removes the ghost points appearing on edge discontinuties
pcl::ShapeContext1980A point structure representing a Shape Context
pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT >ShapeContext3DEstimation implements the 3D shape context descriptor as described in:
openni_wrapper::OpenNIDevice::ShiftConversion
openni_wrapper::ShiftToDepthConverterThis class provides conversion of the openni 11-bit shift data to depth;
pcl::SHOT1344A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color
pcl::SHOT352A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only
pcl::SHOTColorEstimation< PointInT, PointNT, PointOutT, PointRFT >SHOTColorEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors
pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >SHOTColorEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors, in parallel, using the OpenMP standard
pcl::SHOTEstimation< PointInT, PointNT, PointOutT, PointRFT >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, PointRFT >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, PointRFT >SHOTEstimationOMP 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::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT >SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor
pcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT >SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor
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::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar >
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
ON_SerialNumberMap::SN_ELEMENT
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::poisson::SortedTreeNodes
pcl::poisson::SparseMatrix< T >
pcl::SparseQuantizedMultiModTemplateA multi-modality template constructed from a set of quantized multi-modality features
pcl::poisson::SparseSymmetricMatrix< T >
pcl::SpinImageEstimation< PointInT, PointNT, PointOutT >Estimates spin-image descriptors in the given input points
pcl::poisson::Square
pcl::poisson::StartingPolynomial< Degree >
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< pcl::PCLPointCloud2 >StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
pcl::StopWatchSimple stopwatch
pcl::Supervoxel< PointT >Supervoxel container class - stores a cluster extracted using supervoxel clustering
pcl::SupervoxelClustering< PointT >Implements a supervoxel algorithm based on voxel structure, normals, and rgb values
pcl::SurfaceNormalModality< PointInT >Modality based on surface normals
pcl::SurfaceReconstruction< PointInT >SurfaceReconstruction represents a base surface reconstruction class
pcl::SurfelSmoothing< PointT, PointNT >
pcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT >SUSANKeypoint implements a RGB-D extension of the SUSAN detector inluding normal directions variation in top of intensity variation
pcl::SynchronizedQueue< T >
pcl::Synchronizer< T1, T2 >/brief This template class synchronizes two data streams of different types
tagON_2dex
tagON_3dex
tagON_4dex
tagON_RECT
pcl::io::TARHeaderA TAR file's header, as described on http://en.wikipedia.org/wiki/Tar_%28file_format%29
pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TCThis structure is used for determining the end of the k-means clustering process
pcl::TexMaterial
pcl::TextureMapping< PointInT >The texture mapping algorithm
pcl::TextureMesh
pcl::TfQuadraticXYZComparison< PointT >A comparison whether the (x,y,z) components of a given point satisfy (p'Ap + 2v'p + c [OP] 0)
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, Scalar >TransformationEstimation represents the base class for methods for transformation estimation based on:
pcl::registration::TransformationEstimation2D< PointSource, PointTarget, Scalar >TransformationEstimation2D implements a simple 2D rigid transformation estimation (x, y, theta) for a given pair of datasets
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences
pcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget, Scalar >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, Scalar >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::TransformationEstimationPointToPlaneLLSWeighted< PointSource, PointTarget, Scalar >TransformationEstimationPointToPlaneLLSWeighted implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals, with the possibility of assigning weights to the correspondences
pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >TransformationEstimationPointToPlaneWeighted uses Levenberg Marquardt optimization to find the transformation that minimizes the point-to-plane distance between the given correspondences
pcl::registration::TransformationEstimationSVD< PointSource, PointTarget, Scalar >TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences
pcl::registration::TransformationEstimationSVDScale< PointSource, PointTarget, Scalar >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, Scalar >TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation
pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset
tree_desc_s
pcl::poisson::TreeNodeData
pcl::poisson::Triangle
pcl::poisson::TriangleIndex
pcl::geometry::TriangleMesh< MeshTraitsT >Half-edge mesh that can only store triangles
pcl::geometry::TriangleMeshTagTag describing the type of the mesh
pcl::poisson::Triangulation< Real >
pcl::poisson::TriangulationEdge
pcl::poisson::TriangulationTriangle
pcl::recognition::TrimmedICP< PointT, Scalar >
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::TruncatedError
pcl::UnhandledPointTypeException
pcl::common::uniform_distribution< float >Uniform distribution float specialized
pcl::common::uniform_distribution< int >Uniform distribution int specialized
pcl::common::UniformGenerator< T >UniformGenerator class generates a random number from range [min, max] at each run picked according to a uniform distribution i.e eaach number within [min, max] has almost the same probability of being drawn
pcl::UniformSampling< PointInT >UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >UniqueShapeContext implements the Unique Shape Context Descriptor described here:
pcl::UnorganizedPointCloudExceptionAn exception that is thrown when an organized point cloud is needed but not provided
pcl::poisson::UpSampleData
pcl::texture_mapping::UvIndexStructure that links a uv coordinate to its 3D point and face
pcl::ndt2d::ValueAndDerivatives< N, T >Class to store vector value and first and second derivatives (grad vector and hessian matrix), so they can be returned easily from functions
pcl::poisson::Vector< T >
pcl::VectorAverage< real, dimension >Calculates the weighted average and the covariance matrix
pcl::geometry::VertexA vertex is a node in the mesh
pcl::poisson::CoredMeshData2::Vertex
pcl::registration::ELCH< PointT >::Vertex
pcl::geometry::VertexAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the terminating vertex of the inner half-edge (the target)
pcl::geometry::VertexAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the terminating vertex of the outgoing half-edge (the target)
pcl::poisson::VertexData
pcl::geometry::VertexIndexIndex used to access elements in the half-edge mesh
pcl::registration::LUM< PointT >::VertexProperties
pcl::VerticesDescribes a set of vertices in a polygon mesh, by basically storing an array of indices
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)
Viewport
pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::VisualWordStatStructure for storing the visual word
pcl::SupervoxelClustering< PointT >::VoxelDataVoxelData is a structure used for storing data within a pcl::octree::OctreePointCloudAdjacencyContainer
pcl::VoxelGrid< PointT >VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::VoxelGrid< pcl::PCLPointCloud2 >VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::VoxelGridCovariance< PointT >A searchable voxel strucure containing the mean and covariance of the data
pcl::VoxelGridLabel
pcl::VoxelGridOcclusionEstimation< PointT >VoxelGrid to estimate occluded space in the scene
pcl::recognition::VoxelStructure< T, REAL >This class is a box in R3 built of voxels ordered in a regular rectangular grid
pcl::VTKUtils
vtkVertexBufferObject
vtkVertexBufferObjectMapper
pcl::registration::WarpPointRigid< PointSourceT, PointTargetT, Scalar >Base warp point class
pcl::registration::WarpPointRigid3D< PointSourceT, PointTargetT, Scalar >WarpPointRigid3D enables 3D (1D rotation + 2D translation) transformations for points
pcl::registration::WarpPointRigid6D< PointSourceT, PointTargetT, Scalar >WarpPointRigid3D enables 6D (3D rotation + 3D translation) transformations for points
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
z_stream_s
pcl::occlusion_reasoning::ZBuffering< ModelT, SceneT >Class to reason about occlusions