Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 12345]

►Cpcl::registration::CorrespondenceRejectorFeatures:: FeatureContainerInterface
Cpcl::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
C__pixAdd_CN< Tin, Tout, CN >
C__pixAdd_CN< Tin, Tout, 1 >
C__pixAdd_CN< Tin, Tout, 3 >
C__pixAdd_CN< Tin, Tout, 4 >
C__pixColorConv< CSin, CSout, Tin, Tout >
C__pixColorConv< NCVColorSpaceGray, NCVColorSpaceRGBA, Tin, Tout >
C__pixColorConv< NCVColorSpaceRGBA, NCVColorSpaceGray, Tin, Tout >
C__pixDemoteClampNN_CN< Tin, Tout, CN >
C__pixDemoteClampNN_CN< Tin, Tout, 1 >
C__pixDemoteClampNN_CN< Tin, Tout, 3 >
C__pixDemoteClampNN_CN< Tin, Tout, 4 >
C__pixDemoteClampZ_CN< Tin, Tout, CN >
C__pixDemoteClampZ_CN< Tin, Tout, 1 >
C__pixDemoteClampZ_CN< Tin, Tout, 3 >
C__pixDemoteClampZ_CN< Tin, Tout, 4 >
C__pixDist_CN< Tin, Tout, CN >
C__pixDist_CN< Tin, Tout, 1 >
C__pixDist_CN< Tin, Tout, 3 >
C__pixDist_CN< Tin, Tout, 4 >
C__pixScale_CN< Tin, Tout, Tw, CN >
C__pixScale_CN< Tin, Tout, Tw, 1 >
C__pixScale_CN< Tin, Tout, Tw, 3 >
C__pixScale_CN< Tin, Tout, Tw, 4 >
►Cpcl::_Axis
Cpcl::Axis	A point structure representing an Axis using its normal coordinates
►Cpcl::_Intensity
Cpcl::Intensity	A point structure representing the grayscale intensity in single-channel images
►Cpcl::_Intensity32u
Cpcl::Intensity32u	A point structure representing the grayscale intensity in single-channel images
►Cpcl::_Intensity8u
Cpcl::Intensity8u	A point structure representing the grayscale intensity in single-channel images
►Cpcl::_Normal
Cpcl::Normal	A point structure representing normal coordinates and the surface curvature estimate
►Cpcl::tracking::_ParticleXYR
Cpcl::tracking::ParticleXYR
►Cpcl::tracking::_ParticleXYRP
Cpcl::tracking::ParticleXYRP
►Cpcl::tracking::_ParticleXYRPY
Cpcl::tracking::ParticleXYRPY
►Cpcl::tracking::_ParticleXYZR
Cpcl::tracking::ParticleXYZR
►Cpcl::tracking::_ParticleXYZRPY
Cpcl::tracking::ParticleXYZRPY
►Cpcl::_PointDEM
Cpcl::PointDEM	A point structure representing Digital Elevation Map
►Cpcl::_PointNormal
Cpcl::PointNormal	A point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate
►Cpcl::_PointSurfel
Cpcl::PointSurfel	A 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
►Cpcl::_PointWithRange
Cpcl::PointWithRange	A point structure representing Euclidean xyz coordinates, padded with an extra range float
►Cpcl::_PointWithScale
Cpcl::PointWithScale	A point structure representing a 3-D position and scale
►Cpcl::_PointWithViewpoint
Cpcl::PointWithViewpoint	A point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen
►Cpcl::_PointXYZ
Cpcl::PointXYZ	A point structure representing Euclidean xyz coordinates
►Cpcl::_PointXYZHSV
Cpcl::PointXYZHSV
►Cpcl::_PointXYZI	A point structure representing Euclidean xyz coordinates, and the intensity value
Cpcl::PointXYZI
►Cpcl::_PointXYZINormal
Cpcl::PointXYZINormal	A point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate
►Cpcl::_PointXYZL
Cpcl::PointXYZL
►Cpcl::_PointXYZLAB
Cpcl::PointXYZLAB	A point structure representing Euclidean xyz coordinates, and the CIELAB color
►Cpcl::_PointXYZLNormal
Cpcl::PointXYZLNormal	A point structure representing Euclidean xyz coordinates, a label, together with normal coordinates and the surface curvature estimate
►Cpcl::_PointXYZRGB
Cpcl::PointXYZRGB	A point structure representing Euclidean xyz coordinates, and the RGB color
►Cpcl::_PointXYZRGBA
Cpcl::PointXYZRGBA	A point structure representing Euclidean xyz coordinates, and the RGBA color
►Cpcl::_PointXYZRGBL
Cpcl::PointXYZRGBL
►Cpcl::_PointXYZRGBNormal
Cpcl::PointXYZRGBNormal	A point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate
►Cpcl::_ReferenceFrame	A structure representing the Local Reference Frame of a point
Cpcl::ReferenceFrame
►Cpcl::_RGB
Cpcl::RGB	A structure representing RGB color information
►Cpcl::keypoints::agast::AbstractAgastDetector	Abstract detector class for AGAST corner point detectors
Cpcl::keypoints::agast::AgastDetector5_8	Detector class for AGAST corner point detector (5_8)
Cpcl::keypoints::agast::AgastDetector7_12s	Detector class for AGAST corner point detector (7_12s)
Cpcl::keypoints::agast::OastDetector9_16	Detector class for AGAST corner point detector (OAST 9_16)
CAbstractMetadata	Abstract interface for outofcore metadata file types
Cpcl::detail::AccumulatorCurvature
Cpcl::detail::AccumulatorIntensity
Cpcl::detail::AccumulatorLabel
Cpcl::detail::AccumulatorNormal
Cpcl::detail::AccumulatorRGBA
Cpcl::detail::Accumulators< PointT >
Cpcl::detail::Accumulators< pcl::PointXYZRGB >
Cpcl::detail::AccumulatorXYZ
Cpcl::AdaptiveRangeCoder	AdaptiveRangeCoder compression class
Cpcl::cuda::AddCovariances	Adds two matrices element-wise
Cpcl::detail::AddPoint< PointT >
Cpcl::cuda::AddPoints	Simple kernel to add two points
Cpcl::keypoints::internal::AgastApplyNonMaxSuppresion< Out >
Cpcl::keypoints::internal::AgastApplyNonMaxSuppresion< pcl::PointUV >
Cpcl::keypoints::internal::AgastDetector< Out >
Cpcl::keypoints::internal::AgastDetector< pcl::PointUV >
Cpcl::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
Cpcl::poisson::Allocator< pcl::poisson::MatrixEntry< T > >
Cpcl::poisson::Allocator< pcl::poisson::OctNode >
Cpcl::poisson::AllocatorState
►Cboost::mpl::and_
Cpcl::detail::IsAccumulatorCompatible< Point1T, Point2T >::apply< AccumulatorT >
Cpcl::visualization::AreaPickingEvent	/brief Class representing 3D area picking events
►Cboost::array
Cpcl::gpu::people::trees::Histogram
Cpcl::FastBilateralFilter< PointT >::Array3D
Cpcl::traits::asEnum< T >
Cpcl::traits::asEnum< bool >
Cpcl::traits::asEnum< double >
Cpcl::traits::asEnum< float >
Cpcl::traits::asEnum< std::int16_t >
Cpcl::traits::asEnum< std::int32_t >
Cpcl::traits::asEnum< std::int64_t >
Cpcl::traits::asEnum< std::int8_t >
Cpcl::traits::asEnum< std::uint16_t >
Cpcl::traits::asEnum< std::uint32_t >
Cpcl::traits::asEnum< std::uint64_t >
Cpcl::traits::asEnum< std::uint8_t >
CNcvCTprep::assertTest< x >
Cpcl::traits::asType< int >
Cpcl::traits::asType< detail::PointFieldTypes::BOOL >
Cpcl::traits::asType< detail::PointFieldTypes::FLOAT32 >
Cpcl::traits::asType< detail::PointFieldTypes::FLOAT64 >
Cpcl::traits::asType< detail::PointFieldTypes::INT16 >
Cpcl::traits::asType< detail::PointFieldTypes::INT32 >
Cpcl::traits::asType< detail::PointFieldTypes::INT64 >
Cpcl::traits::asType< detail::PointFieldTypes::INT8 >
Cpcl::traits::asType< detail::PointFieldTypes::UINT16 >
Cpcl::traits::asType< detail::PointFieldTypes::UINT32 >
Cpcl::traits::asType< detail::PointFieldTypes::UINT64 >
Cpcl::traits::asType< detail::PointFieldTypes::UINT8 >
Cpcl::gpu::AsyncCopy< T >
Cpcl::gpu::people::trees::AttribLocation
CBFGS< FunctorType >	BFGS stands for Broyden–Fletcher–Goldfarb–Shanno (BFGS) method for solving unconstrained nonlinear optimization problems
CBFGSDummyFunctor< _Scalar, NX >
►CBFGSDummyFunctor< double, 6 >
Cpcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >::OptimizationFunctorWithIndices	Optimization functor structure
Cpcl::poisson::BinaryNode< Real >
Cpcl::BivariatePolynomialT< real >	This represents a bivariate polynomial and provides some functionality for it
Cpcl::gpu::people::Blob2	This structure contains all parameters to describe blobs and their parent/child relations
Cpcl::device::Block
Cpcl::device::kinfuLS::Block
Cpcl::BorderDescription	A structure to store if a point in a range image lies on a border between an obstacle and the background
Cpcl::Boundary	A point structure representing a description of whether a point is lying on a surface boundary or not
Cpcl::recognition::BVH< UserData >::BoundedObject
Cpcl::BoundingBoxXYZ
Cpcl::segmentation::grabcut::BoykovKolmogorov	Boost implementation of Boykov and Kolmogorov's maxflow algorithm doesn't support negative flows which makes it inappropriate for this context
►Cpcl::BranchEstimator	Interface for branch estimators
Cpcl::BinaryTreeThresholdBasedBranchEstimator	Branch estimator for binary trees where the branch is computed only from the threshold
Cpcl::TernaryTreeMissingDataBranchEstimator	Branch estimator for ternary trees where one branch is used for missing data (indicated by flag != 0)
Cpcl::BRISK2DEstimation< PointInT, PointOutT, KeypointT, IntensityT >	Implementation of the BRISK-descriptor, based on the original code and paper reference by
Cpcl::BRISKSignature512	A point structure representing the Binary Robust Invariant Scalable Keypoints (BRISK)
Cpcl::poisson::BSplineData< Degree, Real >::BSplineComponents
Cpcl::poisson::BSplineData< Degree, Real >
Cpcl::poisson::BSplineData< Degree, BSplineDataReal >
Cpcl::poisson::BSplineElementCoefficients< Degree >
►Cpcl::io::Buffer< T >	An abstract base class for fixed-size data buffers
Cpcl::io::AverageBuffer< T >	A buffer that computes running window average of the data inserted
Cpcl::io::MedianBuffer< T >	A buffer that computes running window median of the data inserted
Cpcl::io::SingleBuffer< T >	A simple buffer that only stores data
Cbuffer_traits< T >
Cbuffer_traits< double >
Cbuffer_traits< float >
Cpcl::recognition::BVH< UserData >	This class is an implementation of bounding volume hierarchies
Cpcl::registration::by_score	Sorting of candidates based on fitness score value
Cpcl::device::CalcMorton
Cpcl::texture_mapping::Camera	Structure to store camera pose and focal length
Cpcl::visualization::Camera	Camera class holds a set of camera parameters together with the window pos/size
Cpcl::io::CameraParameters	Basic camera parameters placeholder
►CCameraPoseProcessor	Interface to extract camera pose data generated by the pcl_kinfu_app program
CCameraPoseWriter	CameraPoseWriter writes all camera poses computed by the KinfuTracker to a file on disk
Cpcl::ColorGradientDOTModality< PointInT >::Candidate
Cpcl::ColorGradientModality< PointInT >::Candidate	Candidate for a feature (used in feature extraction methods)
Cpcl::ColorModality< PointInT >::Candidate
Cpcl::SurfaceNormalModality< PointInT >::Candidate	Candidate for a feature (used in feature extraction methods)
Cpcl::gpu::CaptureOpenNI
Cpcl::gpu::kinfuLS::CaptureOpenNI
Cpcl::CentroidPoint< PointT >	A generic class that computes the centroid of points fed to it
Cpcl::cuda::ChangeColor
Cpcl::cuda::CheckPlanarInlier	Check if a certain tuple is a point inlier
Cpcl::cuda::CheckPlanarInlierIndices	Check if a certain tuple is a point inlier
Cpcl::cuda::CheckPlanarInlierKinectIndices	Check if a certain tuple is a point inlier
Cpcl::cuda::CheckPlanarInlierKinectNormalIndices	Check if a certain tuple is a point inlier
Cpcl::cuda::CheckPlanarInlierNormalIndices	Check if a certain tuple is a point inlier
CcJSON
CcJSON_Hooks
►Cpcl::Clipper3D< PointT >	Base class for 3D clipper objects
Cpcl::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 a cube centered at origin and with an extend of -1 to +1 in each dimension
Cpcl::PlaneClipper3D< PointT >	Implementation of a plane clipper in 3D
Cpcl::internal::cloud_point_index_idx	Used internally in voxel grid classes
Cpcl::visualization::CloudActor
Cpcl::common::CloudGenerator< PointT, GeneratorT >
Cpcl::common::CloudGenerator< pcl::PointXY, GeneratorT >
Cpcl::CloudIterator< PointT >	Iterator class for point clouds with or without given indices
Ccode
Cpcl::segmentation::grabcut::Color	Structure to save RGB colors into floats
Cpcl::octree::ColorCoding< PointT >	ColorCoding class
Cpcl::octree::ColorCoding< pcl::PointXYZRGB >
Cpcl::ColorLUT< T >
Cpcl::gpu::ColorVolume	ColorVolume class
Cpcl::gpu::kinfuLS::ColorVolume	ColorVolume class
Cpcl::keypoints::brisk::Layer::CommonParams
►Cpcl::Comparator< PointT >	Comparator is the base class for comparators that compare two points given some function
Cpcl::EuclideanClusterComparator< PointT, PointLT >	EuclideanClusterComparator is a comparator used for finding clusters based on euclidean distance
Cpcl::GroundPlaneComparator< PointT, PointNT >	GroundPlaneComparator is a Comparator for detecting smooth surfaces suitable for driving
►Cpcl::PlaneCoefficientComparator< PointT, PointNT >	PlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
Cpcl::EdgeAwarePlaneComparator< PointT, PointNT >	EdgeAwarePlaneComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
Cpcl::EuclideanPlaneCoefficientComparator< PointT, PointNT >	EuclideanPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
Cpcl::PlaneRefinementComparator< PointT, PointNT, PointLT >	PlaneRefinementComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
Cpcl::RGBPlaneCoefficientComparator< PointT, PointNT >	RGBPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation
Cpcl::device::CompareByLevelCode
Cpcl::SupervoxelClustering< PointT >::SupervoxelHelper::compareLeaves	Comparator for LeafContainerT pointers - used for sorting set of leaves
Cpcl::keypoints::agast::AbstractAgastDetector::CompareScoreIndex	Score index comparator
►Cpcl::ComparisonBase< PointT >	The (abstract) base class for the comparison object
Cpcl::FieldComparison< PointT >	The field-based specialization of the comparison object
Cpcl::PackedHSIComparison< PointT >	A packed HSI specialization of the comparison object
Cpcl::PackedRGBComparison< PointT >	A packed rgb specialization of the comparison object
Cpcl::TfQuadraticXYZComparison< PointT >	A comparison whether the (x,y,z) components of a given point satisfy (p'Ap + 2v'p + c [OP] 0)
Cpcl::io::CompressionPointTraits< PointT >
Cpcl::io::CompressionPointTraits< PointXYZRGB >
Cpcl::io::CompressionPointTraits< PointXYZRGBA >
Cpcl::cuda::ComputeCovarianceForPoint	Kernel to compute a `‘covariance matrix’' for a single point
Cpcl::cuda::ComputeXYZ	Compute the XYZ values for a point based on disparity information
Cpcl::cuda::ComputeXYZRGB	Compute the XYZ and RGB values for a point based on disparity information
►Cpcl::ConditionBase< PointT >	Base condition class
Cpcl::ConditionAnd< PointT >	AND condition
Cpcl::ConditionOr< PointT >	OR condition
Cpcl::io::configurationProfile_t
Cpcl::device::ConnectedComponents
Cpcl::ConstCloudIterator< PointT >	Iterator class for point clouds with or without given indices
Cpcl::ConstCloudIterator< PointT >::ConstIteratorIdx
Cpcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey3
Cpcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey5
Cpcl::poisson::OctNode< NodeData, Real >::ConstNeighbors3
Cpcl::poisson::OctNode< NodeData, Real >::ConstNeighbors5
Cboost::container_gen< eigen_listS, ValueType >
Cboost::container_gen< eigen_vecS, ValueType >
►Cpcl::registration::ConvergenceCriteria	ConvergenceCriteria represents an abstract base class for different convergence criteria used in registration loops
Cpcl::registration::DefaultConvergenceCriteria< float >
Cpcl::registration::DefaultConvergenceCriteria< Scalar >	DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the following criteria for registration loop evaluation:
Cpcl::cuda::convert_point_to_float3	Simple kernel to convert a PointXYZRGB to float3
Cpcl::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
►Cpcl::filters::ConvolvingKernel< PointInT, PointOutT >	Class ConvolvingKernel base class for all convolving kernels
►Cpcl::filters::GaussianKernel< PointInT, PointOutT >	Gaussian kernel implementation interface Use this as implementation reference
Cpcl::filters::GaussianKernelRGB< PointInT, PointOutT >	Gaussian kernel implementation interface with RGB channel handling Use this as implementation reference
Cpcl::filters::ConvolvingKernel< PointT, pcl::Normal >
Cpcl::filters::ConvolvingKernel< PointT, pcl::PointXY >
Cpcl::CopyIfFieldExists< PointInT, OutT >	A helper functor that can copy a specific value if the given field exists
Cpcl::detail::CopyPointHelper< PointInT, PointOutT, Enable >
Cpcl::detail::CopyPointHelper< PointInT, PointOutT, std::enable_if_t< boost::mpl::and_< boost::mpl::not_< std::is_same< PointInT, PointOutT > >, boost::mpl::or_< boost::mpl::and_< pcl::traits::has_field< PointInT, pcl::fields::rgb >, pcl::traits::has_field< PointOutT, pcl::fields::rgba > >, boost::mpl::and_< pcl::traits::has_field< PointInT, pcl::fields::rgba >, pcl::traits::has_field< PointOutT, pcl::fields::rgb > > > >::value > >
Cpcl::detail::CopyPointHelper< PointInT, PointOutT, std::enable_if_t< boost::mpl::and_< boost::mpl::not_< std::is_same< PointInT, PointOutT > >, boost::mpl::or_< boost::mpl::not_< pcl::traits::has_color< PointInT > >, boost::mpl::not_< pcl::traits::has_color< PointOutT > >, boost::mpl::and_< pcl::traits::has_field< PointInT, pcl::fields::rgb >, pcl::traits::has_field< PointOutT, pcl::fields::rgb > >, boost::mpl::and_< pcl::traits::has_field< PointInT, pcl::fields::rgba >, pcl::traits::has_field< PointOutT, pcl::fields::rgba > > > >::value > >
Cpcl::detail::CopyPointHelper< PointInT, PointOutT, std::enable_if_t< std::is_same< PointInT, PointOutT >::value > >
Cpcl::poisson::CoredEdgeIndex
►Cpcl::poisson::CoredMeshData
Cpcl::poisson::CoredFileMeshData
Cpcl::poisson::CoredVectorMeshData
►Cpcl::poisson::CoredMeshData2
Cpcl::poisson::CoredFileMeshData2
Cpcl::poisson::CoredVectorMeshData2
Cpcl::poisson::CoredPointIndex
Cpcl::poisson::CoredVertexIndex
Cpcl::poisson::SortedTreeNodes::CornerIndices
Cpcl::poisson::SortedTreeNodes::CornerTableData
►Cpcl::Correspondence	Correspondence represents a match between two entities (e.g., points, descriptors, etc)
►Cpcl::PointCorrespondence3D	Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g
Cpcl::PointCorrespondence6D	Representation of a (possible) correspondence between two points (e.g
►Cpcl::registration::CorrespondenceRejector	CorrespondenceRejector represents the base class for correspondence rejection methods
Cpcl::registration::CorrespondenceRejectionOrganizedBoundary	Implements a simple correspondence rejection measure
Cpcl::registration::CorrespondenceRejectorDistance	CorrespondenceRejectorDistance implements a simple correspondence rejection method based on thresholding the distances between the correspondences
Cpcl::registration::CorrespondenceRejectorFeatures	CorrespondenceRejectorFeatures implements a correspondence rejection method based on a set of feature descriptors
Cpcl::registration::CorrespondenceRejectorMedianDistance	CorrespondenceRejectorMedianDistance implements a simple correspondence rejection method based on thresholding based on the median distance between the correspondences
Cpcl::registration::CorrespondenceRejectorOneToOne	CorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences
Cpcl::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
►Cpcl::registration::CorrespondenceRejectorSampleConsensus< PointT >	CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
Cpcl::registration::CorrespondenceRejectorSampleConsensus2D< PointT >	CorrespondenceRejectorSampleConsensus2D implements a pixel-based correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
Cpcl::registration::CorrespondenceRejectorSurfaceNormal	CorrespondenceRejectorSurfaceNormal implements a simple correspondence rejection method based on the angle between the normals at correspondent points
Cpcl::registration::CorrespondenceRejectorTrimmed	CorrespondenceRejectorTrimmed 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
Cpcl::registration::CorrespondenceRejectorVarTrimmed	CorrespondenceRejectoVarTrimmed implements a simple correspondence rejection method by considering as inliers a certain percentage of correspondences with the least distances
Cpcl::cuda::CountPlanarInlier	Check if a certain tuple is a point inlier
Cpcl::cuda::CovarianceMatrix	Misnamed class holding a 3x3 matrix
Cpcl::CPPFSignature	A point structure for storing the Point Pair Feature (CPPF) values
Cpcl::cuda::Create1PointPlaneHypothesis< Storage >	Check if a certain tuple is a point inlier
Cpcl::cuda::Create1PointPlaneSampleHypothesis< Storage >	Check if a certain tuple is a point inlier
Cpcl::cuda::CreatePlaneHypothesis< Storage >	Check if a certain tuple is a point inlier
Cpcl::CrfNormalSegmentation< PointT >
Cpcl::CrfSegmentation< PointT >
Cpcl::CRHAlignment< PointT, nbins_ >	CRHAlignment uses two Camera Roll Histograms (CRH) to find the roll rotation that aligns both views
CNcvCTprep::CT_ASSERT_FAILURE< x >
CNcvCTprep::CT_ASSERT_FAILURE< true >
Cct_data_s
Cpcl::poisson::Cube
Cpcl::device::CUDATree	Struct that holds a single RDF tree in GPU
Cpcl::gpu::kinfuLS::CyclicalBuffer	CyclicalBuffer implements a cyclical TSDF buffer
Cpcl::recognition::ORROctree::Node::Data
►Cpcl::registration::DataContainerInterface	DataContainerInterface provides a generic interface for computing correspondence scores between correspondent points in the input and target clouds
Cpcl::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
Cpcl::gpu::DataSource
Cpcl::io::DeBayer	Various debayering methods
Cpcl::cuda::DebayerBilinear< Storage >
Cpcl::cuda::Debayering< Storage >
Cpcl::cuda::DebayeringDownsampling< Storage >
Cpcl::DecisionForestEvaluator< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >	Utility class for evaluating a decision forests
Cpcl::DecisionForestTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >	Trainer for decision trees
Cpcl::DecisionTree< NodeType >	Class representing a decision tree
Cpcl::DecisionTreeEvaluator< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >	Utility class for evaluating a decision tree
Cpcl::DecisionTreeTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >	Trainer for decision trees
►Cpcl::DecisionTreeTrainerDataProvider< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >
Cpcl::face_detection::FaceDetectorDataProvider< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >
Cpcl::ConstCloudIterator< PointT >::DefaultConstIterator
Cpcl::DefaultIterator< PointT >
Cpcl::geometry::DefaultMeshTraits< VertexDataT, HalfEdgeDataT, EdgeDataT, FaceDataT >	The mesh traits are used to set up compile time settings for the mesh
Cpcl::cuda::DeleteIndices	Check if a certain tuple is a point inlier
Cpcl::DenseCrf
Cpcl::DenseQuantizedMultiModTemplate
Cpcl::DenseQuantizedSingleModTemplate
CDeprecatedType	A dummy type to aid in template parameter deprecation
Copenni_wrapper::DepthImage	This class provides methods to fill a depth or disparity image
Cpcl::io::DepthImage	This class provides methods to fill a depth or disparity image
Cpcl::io::depth_sense::DepthSenseGrabberImpl
Cpcl::detail::traits::descriptorSize< FeaturePointT >
Cpcl::detail::traits::descriptorSize< BRISKSignature512 >
Cpcl::detail::traits::descriptorSize< ESFSignature640 >
Cpcl::detail::traits::descriptorSize< FPFHSignature33 >
Cpcl::detail::traits::descriptorSize< GASDSignature512 >
Cpcl::detail::traits::descriptorSize< GASDSignature7992 >
Cpcl::detail::traits::descriptorSize< GASDSignature984 >
Cpcl::detail::traits::descriptorSize< GFPFHSignature16 >
Cpcl::detail::traits::descriptorSize< GRSDSignature21 >
Cpcl::detail::traits::descriptorSize< Histogram< N > >
Cpcl::detail::traits::descriptorSize< Narf36 >
Cpcl::detail::traits::descriptorSize< PFHRGBSignature250 >
Cpcl::detail::traits::descriptorSize< PFHSignature125 >
Cpcl::detail::traits::descriptorSize< ShapeContext1980 >
Cpcl::detail::traits::descriptorSize< SHOT1344 >
Cpcl::detail::traits::descriptorSize< SHOT352 >
Cpcl::detail::traits::descriptorSize< UniqueShapeContext1960 >
Cpcl::detail::traits::descriptorSize< VFHSignature308 >
Cpcl::LineRGBD< PointXYZT, PointRGBT >::Detection	A LineRGBD detection
Cpcl::cuda::Device< T >	Device helper class
Copenni_wrapper::OpenNIDriver::DeviceContext
►Cpcl::gpu::DeviceMemory	DeviceMemory class
Cpcl::gpu::DeviceArray< float4 >
Cpcl::gpu::DeviceArray< double >
Cpcl::gpu::DeviceArray< Label >
Cpcl::gpu::DeviceArray< float >
Cpcl::gpu::DeviceArray< int >
Cpcl::gpu::DeviceArray< pcl::gpu::people::trees::Node >
Cpcl::gpu::DeviceArray< unsigned char >
Cpcl::gpu::DeviceArray< NormalType >
Cpcl::gpu::DeviceArray< std::uint64_t >
Cpcl::gpu::DeviceArray< pcl::PointXYZ >
Cpcl::gpu::DeviceArray< curandState >
Cpcl::gpu::DeviceArray< pcl::tracking::ParticleXYZRPY >
Cpcl::gpu::DeviceArray< PointType >
Cpcl::gpu::DeviceArray< T >	DeviceArray class
►Cpcl::gpu::DeviceMemory2D	DeviceMemory2D class
Cpcl::gpu::DeviceArray2D< prob_histogram >
Cpcl::gpu::DeviceArray2D< double >
Cpcl::gpu::DeviceArray2D< float >
Cpcl::gpu::DeviceArray2D< int >
Cpcl::gpu::DeviceArray2D< unsigned short >
Cpcl::gpu::DeviceArray2D< unsigned char >
Cpcl::gpu::DeviceArray2D< pcl::PointXYZ >
Cpcl::gpu::DeviceArray2D< char4 >
Cpcl::gpu::DeviceArray2D< pcl::FPFHSignature33 >
Cpcl::gpu::DeviceArray2D< pcl::RGB >
Cpcl::gpu::DeviceArray2D< T >	DeviceArray2D class
►Cpcl::gpu::DevPtr< T >
►Cpcl::gpu::PtrStep< T >
Cpcl::gpu::PtrStepSz< T >
Cpcl::gpu::PtrSz< T >
Cpcl::device::Dilatation
Cpcl::cuda::DisparityBoundSmoothing
Cpcl::cuda::DisparityClampedSmoothing
Cpcl::cuda::DisparityHelperMap
Cpcl::DisparityMapConverter< PointT >	Compute point cloud from the disparity map
►Cpcl::DisparityMapConverter< PointDEM >
Cpcl::DigitalElevationMapBuilder	Build a Digital Elevation Map in the column-disparity space from a disparity map and a color image of the scene
Cpcl::cuda::DisparityToCloud	Disparity to PointCloudAOS generator
Cpcl::DistanceMap	Represents a distance map obtained from a distance transformation
Cpcl::cuda::detail::DjSets
Cpcl::DOTMOD	Template matching using the DOTMOD approach
►Cpcl::DOTModality
Cpcl::ColorGradientDOTModality< PointInT >
Cpcl::DOTMODDetection
Cpcl::cuda::downsampleIndices
Cpcl::Edge< PointInT, PointOutT >
Cpcl::poisson::Edge
Cpcl::Edge< ImageType, ImageType >
Cpcl::poisson::EdgeIndex
Cpcl::poisson::SortedTreeNodes::EdgeIndices
Cpcl::registration::LUM< PointT >::EdgeProperties
Cpcl::poisson::SortedTreeNodes::EdgeTableData
Cpcl::device::Eigen33
Cpcl::device::kinfuLS::Eigen33
Cboost::eigen_listS
Cboost::eigen_vecS
Cpcl::detail::EigenVector< Vector, Scalar >
Cpcl::device::Emulation
Cpcl::device::kinfuLS::Emulation
Cpcl::EnergyMaps	Stores a set of energy maps
Cpcl::recognition::RotationSpaceCell::Entry
Cpcl::search::OrganizedNeighbor< PointT >::Entry
►Cboost::equality_comparable
Cpcl::geometry::FaceAroundFaceCirculator< MeshT >	Circulates clockwise around a face and returns an index to the face of the outer half-edge (the target)
Cpcl::geometry::FaceAroundVertexCirculator< MeshT >	Circulates counter-clockwise around a vertex and returns an index to the face of the outgoing half-edge (the target)
Cpcl::geometry::IncomingHalfEdgeAroundVertexCirculator< MeshT >	Circulates counter-clockwise around a vertex and returns an index to the incoming half-edge (the target)
Cpcl::geometry::InnerHalfEdgeAroundFaceCirculator< MeshT >	Circulates clockwise around a face and returns an index to the inner half-edge (the target)
Cpcl::geometry::OuterHalfEdgeAroundFaceCirculator< MeshT >	Circulates clockwise around a face and returns an index to the outer half-edge (the target)
Cpcl::geometry::OutgoingHalfEdgeAroundVertexCirculator< MeshT >	Circulates counter-clockwise around a vertex and returns an index to the outgoing half-edge (the target)
Cpcl::geometry::VertexAroundFaceCirculator< MeshT >	Circulates clockwise around a face and returns an index to the terminating vertex of the inner half-edge (the target)
Cpcl::geometry::VertexAroundVertexCirculator< MeshT >	Circulates counter-clockwise around a vertex and returns an index to the terminating vertex of the outgoing half-edge (the target)
►Cpcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctor
Cpcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::HuberPenalty
Cpcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::TruncatedError
Cpcl::ESFSignature640	A point structure representing the Ensemble of Shape Functions (ESF)
Cpcl::gpu::EuclideanClusterExtraction< PointT >	EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, depending on pcl::gpu::octree
Cpcl::gpu::EuclideanLabeledClusterExtraction< PointT >	EuclideanLabeledClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, depending on pcl::gpu::octree
CEvaluation	Class for RGB-D SLAM Dataset and Benchmark
Cpcl::EventFrequency	A helper class to measure frequency of a certain event
►Cstd::exception
Copenni_wrapper::OpenNIException	General exception class
Cpcl::io::IOException	General IO exception class
Cpcl::geometry::Face	A face is a closed loop of edges
Cpcl::gpu::people::FaceDetector
Cpcl::device::FacetStream
►Cstd::false_type
Cpcl::detail::compat_with_flann< IndexT >
Cpcl::cuda::FastNormalEstimationKernel< Storage >
►Cpcl::gpu::Feature	Feature represents the base feature class
►Cpcl::gpu::FeatureFromNormals	Feature represents the base feature class that takes normals as input also
Cpcl::gpu::FPFHEstimation	Class for FPFH estimation
Cpcl::gpu::PFHEstimation	Class for PFH estimation
Cpcl::gpu::PFHRGBEstimation	Class for PFHRGB estimation
Cpcl::gpu::PPFEstimation	**
Cpcl::gpu::PPFRGBEstimation	**
Cpcl::gpu::PPFRGBRegionEstimation	**
Cpcl::gpu::PrincipalCurvaturesEstimation	‍**
Cpcl::gpu::SpinImageEstimation	‍**
Cpcl::gpu::VFHEstimation	**
Cpcl::gpu::NormalEstimation	Class for normal estimation
Cpcl::registration::CorrespondenceRejectorFeatures::FeatureContainerInterface
Cpcl::FeatureHandler< FeatureType, DataSet, ExampleIndex >	Utility class interface which is used for creating and evaluating features
►Cpcl::FeatureHandler< FT, DataSet, ExampleIndex >
Cpcl::face_detection::FeatureHandlerDepthAverage< FT, DataSet, ExampleIndex >
►Cpcl::FeatureHandler< pcl::MultiChannel2DComparisonFeature< pcl::PointXY32f >, pcl::MultiChannel2DDataSet< DATA_TYPE, NUM_OF_CHANNELS >, pcl::MultipleData2DExampleIndex >
Cpcl::ScaledMultiChannel2DComparisonFeatureHandler< DATA_TYPE, NUM_OF_CHANNELS, SCALE_CHANNEL, INVERT_SCALE >	Feature utility class that handles the creation and evaluation of RGBD comparison features
►Cpcl::FeatureHandler< pcl::MultiChannel2DComparisonFeature< pcl::PointXY32i >, pcl::MultiChannel2DDataSet< DATA_TYPE, NUM_OF_CHANNELS >, pcl::MultipleData2DExampleIndex >
Cpcl::MultiChannel2DComparisonFeatureHandler< DATA_TYPE, NUM_OF_CHANNELS >	Feature utility class that handles the creation and evaluation of RGBD comparison features
►Cpcl::FeatureHandlerCodeGenerator
Cpcl::ScaledMultiChannel2DComparisonFeatureHandlerCCodeGenerator< DATA_TYPE, NUM_OF_CHANNELS, SCALE_CHANNEL, INVERT_SCALE >
Cpcl::FeatureHistogram	Type for histograms for computing mean and variance of some floats
Cpcl::face_detection::FeatureType
Cpcl::FeatureWithLocalReferenceFrames< PointInT, PointRFT >	FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint
►Cpcl::FeatureWithLocalReferenceFrames< PointInT, pcl::ReferenceFrame >
►Cpcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT352, pcl::ReferenceFrame >
►Cpcl::SHOTEstimation< PointInT, PointNT, pcl::SHOT352, pcl::ReferenceFrame >
Cpcl::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
Cpcl::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
►Cpcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT1344, pcl::ReferenceFrame >
►Cpcl::SHOTColorEstimation< PointInT, PointNT, pcl::SHOT1344, pcl::ReferenceFrame >
Cpcl::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
Cpcl::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
Cpcl::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
Cpcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >	UniqueShapeContext implements the Unique Shape Context Descriptor described here:
Cpcl::Fern< FeatureType, NodeType >	Class representing a Fern
Cpcl::FernEvaluator< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >	Utility class for evaluating a fern
Cpcl::FernTrainer< FeatureType, DataSet, LabelType, ExampleIndex, NodeType >	Trainer for a Fern
Cpcl::detail::FieldAdder< PointT >
Cpcl::detail::FieldAdderAdvanced< PointT >	Used together with pcl::for_each_type, creates list of all fields, and list of size of each field
Cpcl::detail::FieldCaster< PointT >
Cpcl::detail::FieldCopier< PointT >	Used together with pcl::for_each_type, copies all point fields from cloud_data (respecting each field offset) to msg_data (tightly packed)
Cpcl::detail::FieldMapper< PointT >
Cpcl::detail::FieldMapping
Cpcl::FieldMatches< PointT, Tag >
►Cpcl::visualization::Figure2D	Abstract class for storing figure information
Cpcl::visualization::FEllipticArc2D	Class for storing EllipticArc; every ellipse , circle are covered by this
Cpcl::visualization::FPoints2D	Class for storing Points
Cpcl::visualization::FPolyLine2D	Class for PolyLine
Cpcl::visualization::FPolygon2D	Class for Polygon
Cpcl::visualization::FQuad2D	Class for storing Quads
►Cpcl::FileGrabber< PointT >	FileGrabber provides a container-style interface for grabbers which operate on fixed-size input
Cpcl::ImageGrabber< PointT >
Cpcl::PCDGrabber< PointT >
►Cpcl::FileReader	Point Cloud Data (FILE) file format reader interface
Cpcl::ASCIIReader	Ascii Point Cloud Reader
Cpcl::OBJReader
Cpcl::PCDReader	Point Cloud Data (PCD) file format reader
Cpcl::PLYReader	Point Cloud Data (PLY) file format reader
►Cpcl::FileWriter	Point Cloud Data (FILE) file format writer
Cpcl::PCDWriter	Point Cloud Data (PCD) file format writer
Cpcl::PLYWriter	Point Cloud Data (PLY) file format writer
►Cpcl::search::FlannSearch< PointT, FlannDistance >::FlannIndexCreator	Helper class that creates a FLANN index from a given FLANN matrix
Cpcl::search::FlannSearch< PointT, FlannDistance >::KMeansIndexCreator	Creates a FLANN KdTreeSingleIndex from the given input data
Cpcl::search::FlannSearch< PointT, FlannDistance >::KdTreeIndexCreator	Creates a FLANN KdTreeSingleIndex from the given input data
Cpcl::search::FlannSearch< PointT, FlannDistance >::KdTreeMultiIndexCreator	Creates a FLANN KdTreeIndex of multiple randomized trees from the given input data, suitable for feature matching
Cpcl::device::float12
Cpcl::device::kinfuLS::float12
Cpcl::device::float8
Cpcl::device::kinfuLS::float8
Cpcl::visualization::FloatImageUtils	Provide some general functionalities regarding 2d float arrays, e.g., for visualization purposes
Cpcl::for_each_type_impl< done >
Cpcl::for_each_type_impl< false >
Cpcl::FPFHSignature33	A point structure representing the Fast Point Feature Histogram (FPFH)
Cpcl::io::FrameWrapper	Pure abstract interface to wrap native frame data types
Cpcl::poisson::FunctionData< Degree, Real >
Cpcl::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) depending on the chosen _Scalar
Cpcl::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) depending on the chosen _Scalar
►Cpcl::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) depending on the chosen _Scalar
Cpcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctor
Cpcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices
Cpcl::Functor< double >
Cpcl::Functor< float >
►Cpcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::Functor< MatScalar >
Cpcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctor
Cpcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices
CfunctorAddValues< T >
CfunctorMaxValues< T >
CfunctorMinValues< T >
Cpcl::GASDSignature512	A point structure representing the Globally Aligned Spatial Distribution (GASD) shape descriptor
Cpcl::GASDSignature7992	A point structure representing the Globally Aligned Spatial Distribution (GASD) shape and color descriptor
Cpcl::GASDSignature984	A point structure representing the Globally Aligned Spatial Distribution (GASD) shape and color descriptor
Cpcl::segmentation::grabcut::Gaussian	Gaussian structure
Cpcl::segmentation::grabcut::GaussianFitter	Helper class that fits a single Gaussian to color samples
Cpcl::GaussianKernel	Class GaussianKernel assembles all the method for computing, convolving, smoothing, gradients computing an image using a gaussian kernel
Cpcl::detail::GetPoint< PointT >
Cpcl::GFPFHSignature16	A point structure representing the GFPFH descriptor with 16 bins
Cpcl::segmentation::grabcut::GMM
►Cpcl::Grabber	Grabber interface for PCL 1.x device drivers
Cpcl::DavidSDKGrabber	Grabber for davidSDK structured light compliant devices
Cpcl::DepthSenseGrabber	Grabber for DepthSense devices (e.g
Cpcl::DinastGrabber	Grabber for DINAST devices (i.e., IPA-1002, IPA-1110, IPA-2001)
Cpcl::EnsensoGrabber	Grabber for IDS-Imaging Ensenso's devices
►Cpcl::HDLGrabber	Grabber for the Velodyne High-Definition-Laser (HDL)
Cpcl::VLPGrabber	Grabber for the Velodyne LiDAR (VLP), based on the Velodyne High Definition Laser (HDL)
►Cpcl::ImageGrabberBase	Base class for Image file grabber
Cpcl::ImageGrabber< PointT >
Cpcl::ONIGrabber	A simple ONI grabber
Cpcl::OpenNIGrabber	Grabber for OpenNI devices (i.e., Primesense PSDK, Microsoft Kinect, Asus XTion Pro/Live)
►Cpcl::PCDGrabberBase	Base class for PCD file grabber
Cpcl::PCDGrabber< PointT >
Cpcl::RealSense2Grabber	Grabber for Intel Realsense 2 SDK devices (D400 series)
Cpcl::RealSenseGrabber
Cpcl::RobotEyeGrabber	Grabber for the Ocular Robotics RobotEye sensor
►Cpcl::StereoGrabberBase	Base class for Stereo file grabber
Cpcl::StereoGrabber< PointT >
Cpcl::TimGrabber
Cpcl::GradientXY	A point structure representing Euclidean xyz coordinates, and the intensity value
Cpcl::cuda::detail::Graph< T >
Cpcl::cuda::detail::GraphEdge< T >
Cpcl::registration::GraphHandler< GraphT >	GraphHandler class is a wrapper for a general SLAM graph The actual graph class must fulfill the following boost::graph concepts:
Cpcl::registration::GraphOptimizer< GraphT >	GraphOptimizer class; derive and specialize for each graph type
►Cpcl::GraphRegistration< GraphT >	GraphRegistration class is the base class for graph-based registration methods
Cpcl::PairwiseGraphRegistration< GraphT, PointT >	PairwiseGraphRegistration class aligns the clouds two by two
Cpcl::people::GroundBasedPeopleDetectionApp< PointT >	GroundBasedPeopleDetectionApp performs people detection on RGB-D data having as input the ground plane coefficients
Cpcl::GRSDSignature21	A point structure representing the Global Radius-based Surface Descriptor (GRSD)
Cgz_header_s
CHaarClassifierCascadeDescriptor	Classifier cascade descriptor
CHaarClassifierNode128
CHaarClassifierNodeDescriptor32
CHaarFeature64
CHaarFeatureDescriptor32
CHaarStage64
Cpcl::geometry::HalfEdge	An edge is a connection between two vertices
Cpcl::has_custom_allocator< T >	Tests at compile time if type T has a custom allocator
Cpcl::HashTableOLD
Cpcl::HDLGrabber::HDLDataPacket
Cpcl::HDLGrabber::HDLFiringData
Cpcl::HDLGrabber::HDLLaserCorrection
Cpcl::HDLGrabber::HDLLaserReturn
Cpcl::people::HeadBasedSubclustering< PointT >	HeadBasedSubclustering represents a class for searching for people inside a HeightMap2D based on a 3D head detection algorithm
Cpcl::gpu::kinfuLS::TsdfVolume::Header	Structure storing voxel grid resolution, volume size (in mm) and element_size of data stored on host
Cpcl::TSDFVolume< VoxelT, WeightT >::Header	Structure storing voxel grid resolution, volume size (in mm) and element_size of stored data
Cpcl::people::HeightMap2D< PointT >	HeightMap2D represents a class for creating a 2D height map from a point cloud and searching for its local maxima
Cpcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT, NrDims >
Cpcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT[NrDims], NrDims >
Cpcl::device::Histogram< N >
Cpcl::Histogram< N >	A point structure representing an N-D histogram
Cpcl::gpu::people::trees::HistogramPair
Cpcl::people::HOG	HOG represents a class for computing the HOG descriptor described in Dalal, N
Cpcl::cuda::Host< T >	Host helper class
Cpcl::recognition::HoughSpace3D	HoughSpace3D is a 3D voting space
►Cpcl::recognition::HypothesisBase
Cpcl::recognition::Hypothesis
Cpcl::recognition::ObjRecRANSAC::HypothesisCreator
►Cpcl::HypothesisVerification< ModelT, SceneT >	Abstract class for hypotheses verification methods
Cpcl::GlobalHypothesesVerification< ModelT, SceneT >	A hypothesis verification method proposed in "A Global Hypotheses Verification Method for 3D Object Recognition", A
Cpcl::GreedyVerification< ModelT, SceneT >	A greedy hypothesis verification method
Cpcl::PapazovHV< ModelT, SceneT >	A hypothesis verification method proposed in "An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes", C
Cpcl::IFSReader	Indexed Face set (IFS) file format reader
Cpcl::IFSWriter	Point Cloud Data (IFS) file format writer
►Copenni_wrapper::Image	Image class containing just a reference to image meta data
Copenni_wrapper::ImageBayerGRBG	This class provides methods to fill a RGB or Grayscale image buffer from underlying Bayer pattern image
Copenni_wrapper::ImageRGB24	This class provides methods to fill a RGB or Grayscale image buffer from underlying RGB24 image
Copenni_wrapper::ImageYUV422	Concrete implementation of the interface Image for a YUV 422 image used by Primesense devices
►Cpcl::io::Image	Image interface class providing an interface to fill a RGB or Grayscale image buffer
Cpcl::io::ImageRGB24	This class provides methods to fill a RGB or Grayscale image buffer from underlying RGB24 image
Cpcl::io::ImageYUV422	Concrete implementation of the interface Image for a YUV 422 image used by Primesense devices
►Cpcl::visualization::ImageViewer	ImageViewer is a class for 2D image visualization
Cpcl::visualization::RangeImageVisualizer	Range image visualizer class
Cpcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >	This class implements Implicit Shape Model algorithm described in "Hough Transforms and 3D SURF for robust three dimensional classification" by Jan Knopp, Mukta Prasad, Geert Willems, Radu Timofte, and Luc Van Gool
Cpcl::registration::IncrementalRegistration< PointT, Scalar >	Incremental IterativeClosestPoint class
►CINCVMemAllocator	INCVMemAllocator (Interface)
CNCVMemNativeAllocator	NCVMemNativeAllocator
CNCVMemStackAllocator	NCVMemStackAllocator
Cflann::Index< T >
Cinflate_state
Cpcl::device::InitalSimplex
CLoki::Int2Type< v >
Cpcl::detail::int_type< Bits, Signed >	Int_type::type refers to an integral type that satisfies template parameters
Cpcl::detail::int_type< 16, false >
Cpcl::detail::int_type< 16, true >
Cpcl::detail::int_type< 32, false >
Cpcl::detail::int_type< 32, true >
Cpcl::detail::int_type< 64, false >
Cpcl::detail::int_type< 64, true >
Cpcl::detail::int_type< 8, false >
Cpcl::detail::int_type< 8, true >
Cpcl::IntegralImage2D< DataType, Dimension >	Determines an integral image representation for a given organized data array
Cpcl::IntegralImage2D< DataType, 1 >	Partial template specialization for integral images with just one channel
Cpcl::IntegralImage2D< float, 1 >
Cpcl::IntegralImage2D< float, 3 >
Cpcl::IntegralImageTypeTraits< DataType >
Cpcl::IntegralImageTypeTraits< char >
Cpcl::IntegralImageTypeTraits< float >
Cpcl::IntegralImageTypeTraits< int >
Cpcl::IntegralImageTypeTraits< short >
Cpcl::IntegralImageTypeTraits< unsigned char >
Cpcl::IntegralImageTypeTraits< unsigned int >
Cpcl::IntegralImageTypeTraits< unsigned short >
Cpcl::common::IntensityFieldAccessor< PointT >
Cpcl::common::IntensityFieldAccessor< pcl::InterestPoint >
Cpcl::common::IntensityFieldAccessor< pcl::PointNormal >
Cpcl::common::IntensityFieldAccessor< pcl::PointSurfel >
Cpcl::common::IntensityFieldAccessor< pcl::PointWithRange >
Cpcl::common::IntensityFieldAccessor< pcl::PointWithScale >
Cpcl::common::IntensityFieldAccessor< pcl::PointWithViewpoint >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZ >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZHSV >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZL >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZLAB >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZLNormal >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZRGB >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZRGBA >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZRGBL >
Cpcl::common::IntensityFieldAccessor< pcl::PointXYZRGBNormal >
Cpcl::common::IntensityFieldAccessor< PointInT >
Cpcl::common::IntensityFieldAccessor< PointOutT >
Cpcl::IntensityGradient	A point structure representing the intensity gradient of an XYZI point cloud
Cpcl::InterestPoint	A point structure representing an interest point with Euclidean xyz coordinates, and an interest value
Cinternal_state
Cpcl::intersect< Sequence1, Sequence2 >
Cpcl::device::Intr	Camera intrinsics structure
Cpcl::device::kinfuLS::Intr	Camera intrinsics structure
Cpcl::TSDFVolume< VoxelT, WeightT >::Intr	Camera intrinsics structure
Copenni_wrapper::IRImage	Class containing just a reference to IR meta data
Cpcl::io::IRImage	Class containing just a reference to IR meta data
Cboost::detail::is_random_access< eigen_listS >
Cboost::detail::is_random_access< eigen_vecS >
Cpcl::detail::IsAccumulatorCompatible< Point1T, Point2T >
Cpcl::PosesFromMatches::PoseEstimate::IsBetter
Cpcl_cuda::isFiniteAOS	Check if a specific point is valid or not
Cpcl_cuda::isFiniteSOA	Check if a specific point is valid or not
Cpcl_cuda::isFiniteZIPSOA	Check if a specific point is valid or not
Cpcl::cuda::isInlier	Check if a certain tuple is a point inlier
Cpcl::features::ISMModel	The assignment of this structure is to store the statistical/learned weights and other information of the trained Implicit Shape Model algorithm
Cpcl::ISMPeak	This struct is used for storing peak
Cpcl::features::ISMVoteList< PointT >	This class is used for storing, analyzing and manipulating votes obtained from ISM algorithm
Cpcl::cuda::isNaNPoint
Cpcl::cuda::isNotInlier
Cpcl::cuda::isNotZero< T >
Cpcl::IteratorIdx< PointT >
Cpcl::octree::IteratorState
►Cpcl::KdTree< PointT >	KdTree represents the base spatial locator class for kd-tree implementations
Cpcl::KdTreeFLANN< pcl::PointXYZRGB >
Cpcl::KdTreeFLANN< pcl::VFHSignature308 >
Cpcl::KdTreeFLANN< PointTarget >
Cpcl::KdTreeFLANN< pcl::PointXYZLAB >
Cpcl::KdTreeFLANN< pcl::InterestPoint >
Cpcl::KdTreeFLANN< FeatureT >
Cpcl::KdTreeFLANN< PointT, Dist >	KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures
Cpcl::kernel< PointT >
Cpcl::kernel< PointInT >
CNCVRuntimeTemplateBool::KernelCaller< TList, NumArguments, Func >
CNCVRuntimeTemplateBool::KernelCaller< TList, 0, Func >
Cpcl::visualization::KeyboardEvent	/brief Class representing key hit/release events
Cpcl::gpu::kinfuLS::KinfuTracker	KinfuTracker class encapsulates implementation of Microsoft Kinect Fusion algorithm
Cpcl::gpu::KinfuTracker	KinfuTracker class encapsulates implementation of Microsoft Kinect Fusion algorithm
Ckiss_fft_cpx
Ckiss_fft_state
Cpcl::Kmeans	K-means clustering
Cflann::L2< T >
Cflann::L2_Simple< T >
Cpcl::Label
Cpcl::gpu::people::trees::LabeledAttrib
Cpcl::gpu::people::trees::LabeledFeature
Cpcl::keypoints::brisk::Layer	A layer in the BRISK detector pyramid
►Cpcl::LCCPSegmentation< PointT >	A simple segmentation algorithm partitioning a supervoxel graph into groups of locally convex connected supervoxels separated by concave borders
Cpcl::CPCSegmentation< PointT >	A segmentation algorithm partitioning a supervoxel graph
Cpcl::GridProjection< PointNT >::Leaf	Data leaf
Cpcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid::Leaf
Cpcl::UniformSampling< PointT >::Leaf	Simple structure to hold an nD centroid and the number of points in a leaf
Cpcl::VoxelGridCovariance< PointT >::Leaf	Simple structure to hold a centroid, covarince and the number of points in a leaf
Cpcl::device::LessThanByFacet
Cpcl::device::kinfuLS::LightSource	Light source collection
Cpcl::device::LightSource	Light source collection
Cpcl::LinearizedMaps	Stores a set of linearized maps
Cpcl::LINEMOD	Template matching using the LINEMOD approach
Cpcl::LINEMOD_OrientationMap	Map that stores orientations
Cpcl::LINEMODDetection	Represents a detection of a template using the LINEMOD approach
Cpcl::LineRGBD< PointXYZT, PointRGBT >	High-level class for template matching using the LINEMOD approach based on RGB and Depth data
Cpcl::io::ply::ply_parser::list_property_begin_callback_type< SizeType, ScalarType >
Cpcl::io::ply::ply_parser::list_property_begin_callback_type< size_type, scalar_type >
Cpcl::io::ply::ply_parser::list_property_definition_callback_type< SizeType, ScalarType >
Cpcl::io::ply::ply_parser::list_property_definition_callback_type< size_type, scalar_type >
Cpcl::io::ply::ply_parser::list_property_definition_callbacks_type
Cpcl::io::ply::ply_parser::list_property_element_callback_type< SizeType, ScalarType >
Cpcl::io::ply::ply_parser::list_property_element_callback_type< size_type, scalar_type >
Cpcl::io::ply::ply_parser::list_property_end_callback_type< SizeType, ScalarType >
Cpcl::io::ply::ply_parser::list_property_end_callback_type< size_type, scalar_type >
Cpcl::RangeImageBorderExtractor::LocalSurface	Stores some information extracted from the neighborhood of a point
Cpcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::LocationInfo	This structure stores the information about the keypoint
CLRUCache< KeyT, CacheItemT >
CLRUCacheItem< T >
►CLRUCacheItem< vtkSmartPointer< vtkPolyData > >
COutofcoreCloud::CloudDataCacheItem
Cpcl::registration::LUM< PointT >	Globally Consistent Scan Matching based on an algorithm by Lu and Milios
►Cpcl::io::LZFImageReader	PCL-LZF image format reader
Cpcl::io::LZFDepth16ImageReader	PCL-LZF 16-bit depth image format reader
►Cpcl::io::LZFRGB24ImageReader	PCL-LZF 24-bit RGB image format reader
Cpcl::io::LZFBayer8ImageReader	PCL-LZF 8-bit Bayer image format reader
Cpcl::io::LZFYUV422ImageReader	PCL-LZF 8-bit Bayer image format reader
►Cpcl::io::LZFImageWriter	PCL-LZF image format writer
Cpcl::io::LZFDepth16ImageWriter	PCL-LZF 16-bit depth image format writer
►Cpcl::io::LZFRGB24ImageWriter	PCL-LZF 24-bit RGB image format writer
Cpcl::io::LZFBayer8ImageWriter	PCL-LZF 8-bit Bayer image format writer
Cpcl::io::LZFYUV422ImageWriter	PCL-LZF 16-bit YUV422 image format writer
CON_SerialNumberMap::MAP_VALUE
Cpcl::poisson::MapReduceVector< T2 >
Cpcl::gpu::kinfuLS::MarchingCubes	MarchingCubes implements MarchingCubes functionality for TSDF volume on GPU
Cpcl::gpu::MarchingCubes	MarchingCubes implements MarchingCubes functionality for TSDF volume on GPU
Cpcl::poisson::MarchingCubes
Cpcl::poisson::MarchingSquares
Cpcl::MaskMap
Cpcl::device::kinfuLS::Mat33	3x3 Matrix for device code
Cpcl::device::Mat33	3x3 Matrix for device code
Cpcl::registration::MatchingCandidate	Container for matching candidate consisting of
Cflann::Matrix< T >
Cpcl::poisson::MatrixEntry< T >
CMesh
Cpcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >	Base class for the half-edge mesh
►Cpcl::geometry::MeshBase< PolygonMesh< MeshTraitsT >, MeshTraitsT, PolygonMeshTag >
Cpcl::geometry::PolygonMesh< MeshTraitsT >	General half-edge mesh that can store any polygon with a minimum number of vertices of 3
►Cpcl::geometry::MeshBase< QuadMesh< MeshTraitsT >, MeshTraitsT, QuadMeshTag >
Cpcl::geometry::QuadMesh< MeshTraitsT >	Half-edge mesh that can only store quads
►Cpcl::geometry::MeshBase< TriangleMesh< MeshTraitsT >, MeshTraitsT, TriangleMeshTag >
Cpcl::geometry::TriangleMesh< MeshTraitsT >	Half-edge mesh that can only store triangles
Cpcl::geometry::MeshIO< MeshT >	Read / write the half-edge mesh from / to a file
►Cpcl::MeshProcessing	MeshProcessing represents the base class for mesh processing algorithms
Cpcl::EarClipping	The ear clipping triangulation algorithm
Cpcl::MeshQuadricDecimationVTK	PCL mesh decimation based on vtkQuadricDecimation from the VTK library
Cpcl::MeshSmoothingLaplacianVTK	PCL mesh smoothing based on the vtkSmoothPolyDataFilter algorithm from the VTK library
Cpcl::MeshSmoothingWindowedSincVTK	PCL mesh smoothing based on the vtkWindowedSincPolyDataFilter algorithm from the VTK library
Cpcl::MeshSubdivisionVTK	PCL mesh smoothing based on the vtkLinearSubdivisionFilter, vtkLoopSubdivisionFilter, vtkButterflySubdivisionFilter depending on the selected MeshSubdivisionVTKFilterType algorithm from the VTK library
Cpcl::registration::MetaRegistration< PointT, Scalar >	Meta Registration class
Cpcl::poisson::MinimalAreaTriangulation< Real >
Cpcl::device::Eigen33::MiniMat< Rows >
Cpcl::device::kinfuLS::Eigen33::MiniMat< Rows >
Cpcl::MLSResult::MLSProjectionResults	Data structure used to store the MLS projection results
Cpcl::MLSResult	Data structure used to store the results of the MLS fitting
Cpcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid	A minimalistic implementation of a voxel grid, necessary for the point cloud upsampling
Cpcl::RealSenseGrabber::Mode	A descriptor for capturing mode
Cpcl::OpenNIGrabber::modeComp
Cpcl::recognition::ModelLibrary::Model	Stores some information about the model
Cpcl::ModelCoefficients
Cpcl::recognition::ModelLibrary
Cpcl::MomentInvariants	A point structure representing the three moment invariants
Cpcl::device::Morton
Cpcl::visualization::MouseEvent
Cpcl::MTLReader
Cpcl::MultiChannel2DComparisonFeature< PointT >	Feature for comparing two sample points in 2D multi-channel data
Cpcl::MultiChannel2DData< DATA_TYPE, NUM_OF_CHANNELS >	Holds two-dimensional multi-channel data
Cpcl::MultiChannel2DDataSet< DATA_TYPE, NUM_OF_CHANNELS >	Holds a set of two-dimensional multi-channel data
Cpcl::MultipleData2DExampleIndex	Example index for a set of 2D data blocks
Cpcl::device::MultiTreeLiveProc	Processor using multiple trees
Cpcl::Narf	NARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data
Cpcl::Narf36	A point structure representing the Narf descriptor
►CNCVMatrix< T >	NCVMatrix (2D)
CNCVMatrixAlloc< T >	NCVMatrixAlloc
CNCVMatrixReuse< T >	NCVMatrixReuse
CNCVMemPtr	NCVMemPtr
CNCVMemSegment	NCVMemSegment
CNcvPoint2D32s
CNcvPoint2D32u
CNcvRect32s
CNcvRect32u
CNcvRect8u
CNcvSize32s
CNcvSize32u
►CNCVVector< T >	NCVVector (1D)
CNCVVectorAlloc< HaarStage64 >
CNCVVectorAlloc< HaarFeature64 >
CNCVVectorAlloc< HaarClassifierNode128 >
CNCVVectorAlloc< T >	NCVVectorAlloc
CNCVVectorReuse< T >	NCVVectorReuse
Cpcl::NdCentroidFunctor< PointT, Scalar >	Helper functor structure for n-D centroid estimation
Cpcl::NdConcatenateFunctor< PointInT, PointOutT >	Helper functor structure for concatenate
Cpcl::NdCopyEigenPointFunctor< PointOutT >	Helper functor structure for copying data between an Eigen type and a PointT
Cpcl::NdCopyPointEigenFunctor< PointInT >	Helper functor structure for copying data between an Eigen type and a PointT
Cpcl::OrganizedNeighborSearch< PointT >::nearestNeighborCandidate	nearestNeighborCandidate entry for the nearest neighbor candidate queue
Cpcl::OrganizedEdgeBase< PointT, PointLT >::Neighbor
Cpcl::gpu::NeighborIndices
Cpcl::poisson::OctNode< NodeData, Real >::NeighborKey3
Cpcl::poisson::OctNode< NodeData, Real >::NeighborKey5
Cpcl::Permutohedral::Neighbors
Cpcl::poisson::OctNode< NodeData, Real >::Neighbors3
Cpcl::poisson::OctNode< NodeData, Real >::Neighbors5
Cpcl::cuda::NewCheckPlanarInlier< Storage >	Check if a certain tuple is a point inlier
►Copenni::VideoStream::NewFrameListener
Cpcl::io::openni2::OpenNI2FrameListener
Cpcl::GrabCut< PointT >::NLinks
Cflann::NNIndex< T >
Cpcl::geometry::NoData	No data is associated with the vertices / half-edges / edges / faces
Cpcl::gpu::people::trees::Node
Cpcl::recognition::BVH< UserData >::Node
Cpcl::recognition::ORRGraph< NodeData >::Node
Cpcl::recognition::ORROctree::Node
Cpcl::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar >::Node
Cpcl::device::NonCachedLoad< T >
►Cboost::noncopyable
CMonitorQueue< DataT >
Cpcl::io::depth_sense::DepthSenseDeviceManager	A helper class for enumerating and managing access to DepthSense devices
Cpcl::io::real_sense::RealSenseDevice
Cpcl::io::real_sense::RealSenseDeviceManager
Cpcl::ndt2d::NDT2D< PointT >	Build a Normal Distributions Transform of a 2D point cloud
Cpcl::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
Cpcl::visualization::CloudViewer	Simple point cloud visualization class
Cpcl::gpu::DataSource::Normal2PointXYZ
Cpcl::common::normal_distribution< T >	Normal distribution
Cpcl::NormalBasedSignature12	A point structure representing the Normal Based Signature for a feature matrix of 4-by-3
Cpcl::cuda::NormalDeviationKernel< Storage >
Cpcl::ndt2d::NormalDist< PointT >	A normal distribution estimation class
Cpcl::cuda::NormalEstimationKernel< Storage >
Cpcl::common::NormalGenerator< T >	NormalGenerator class generates a random number from a normal distribution specified by (mean, sigma)
CNppStInterpolationState	Frame interpolation state
Cpcl::registration::NullEstimate	NullEstimate struct
Cpcl::registration::NullMeasurement	NullMeasurement struct
CLoki::NullType
CEigen::NumTraits< pcl::ndt2d::NormalDist< PointT > >
Cpcl::poisson::NVector< T, Dim >
►CObject
CAxes
CCamera
CGeometry
CGrid
COutofcoreCloud
CObjectFeatures
CObjectModel
CObjectRecognition
CObjectRecognitionParameters
Cpcl::recognition::ObjRecRANSAC	This is a RANSAC-based 3D object recognition method
Cpcl::poisson::OctNode< NodeData, Real >
Cpcl::poisson::OctNode< class TreeNodeData, Real >
Cpcl::gpu::Octree	Octree implementation on GPU
Cpcl::poisson::Octree< Degree >
►Cpcl::octree::Octree2BufBase< LeafContainerT, BranchContainerT >	Octree double buffer class
►Cpcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndices, OctreeContainerEmpty, Octree2BufBase< OctreeContainerPointIndices, OctreeContainerEmpty > >
Cpcl::io::OctreePointCloudCompression< PointT, LeafT, BranchT, OctreeT >	Octree pointcloud compression class
Cpcl::octree::OctreePointCloudChangeDetector< PointT, LeafContainerT, BranchContainerT >	Octree pointcloud change detector class
►Cpcl::octree::OctreeBase< LeafContainerT, BranchContainerT >	Octree class
►Cpcl::octree::OctreePointCloud< PointT, OctreePointCloudAdjacencyContainer< PointT >, OctreeContainerEmpty >
Cpcl::octree::OctreePointCloudAdjacency< PointT, LeafContainerT, BranchContainerT >	Octree pointcloud voxel class which maintains adjacency information for its voxels
►Cpcl::octree::OctreePointCloud< PointT, pcl::octree::OctreeContainerPointIndices, pcl::octree::OctreeContainerEmpty >
Cpcl::octree::OctreePointCloudSearch< PointT, pcl::octree::OctreeContainerPointIndices, pcl::octree::OctreeContainerEmpty >
►Cpcl::octree::OctreePointCloud< PointT, OctreePointCloudDensityContainer, OctreeContainerEmpty >
Cpcl::octree::OctreePointCloudDensity< PointT, LeafContainerT, BranchContainerT >	Octree pointcloud density class
►Cpcl::octree::OctreePointCloud< PointT, OctreePointCloudVoxelCentroidContainer< PointT >, OctreeContainerEmpty >
Cpcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafContainerT, BranchContainerT >	Octree pointcloud voxel centroid class
►Cpcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndex, OctreeContainerEmpty, OctreeBase< OctreeContainerPointIndex, OctreeContainerEmpty > >
Cpcl::octree::OctreePointCloudSinglePoint< PointT, LeafContainerT, BranchContainerT, OctreeT >	Octree pointcloud single point class
►Cpcl::octree::OctreePointCloud< PointT, OctreeContainerEmpty, OctreeContainerEmpty, OctreeBase< OctreeContainerEmpty, OctreeContainerEmpty > >
Cpcl::octree::OctreePointCloudOccupancy< PointT, LeafContainerT, BranchContainerT >	Octree pointcloud occupancy class
►Cpcl::octree::OctreeBase< OctreeContainerPointIndices, OctreeContainerEmpty >
►Cpcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndices, OctreeContainerEmpty, OctreeBase< OctreeContainerPointIndices, OctreeContainerEmpty > >
Cpcl::octree::OctreePointCloudPointVector< PointT, LeafContainerT, BranchContainerT, OctreeT >	Octree pointcloud point vector class
►Cpcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndices, OctreeContainerEmpty >
Cpcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >	Octree pointcloud search class
►Cpcl::octree::OctreePointCloud< PointT, LeafContainerT, BranchContainerT, OctreeT >	Octree pointcloud class
Cpcl::octree::OctreePointCloudChangeDetector< PointInT >
►Cpcl::octree::OctreeContainerBase	Octree container class that can serve as a base to construct own leaf node container classes
Cpcl::octree::OctreeContainerEmpty	Octree container class that does not store any information
Cpcl::octree::OctreeContainerPointIndex	Octree container class that does store a single point index
Cpcl::octree::OctreeContainerPointIndices	Octree container class that does store a vector of point indices
Cpcl::octree::OctreePointCloudAdjacencyContainer< PointInT, DataT >	Octree adjacency leaf container class- stores a list of pointers to neighbors, number of points added, and a DataT value
Cpcl::octree::OctreePointCloudDensityContainer	Octree pointcloud density leaf node class
Cpcl::octree::OctreePointCloudVoxelCentroidContainer< PointT >	Octree pointcloud voxel centroid leaf node class
Cpcl::device::OctreeImpl::OctreeDataHost
►Cpcl::device::OctreeGlobal
Cpcl::device::OctreeGlobalWithBox
Cpcl::device::OctreeImpl
►Cpcl::octree::OctreeIteratorBase< OctreeT >	Abstract octree iterator class
►Cpcl::octree::OctreeBreadthFirstIterator< OctreeT >	Octree iterator class
Cpcl::octree::OctreeFixedDepthIterator< OctreeT >	Octree iterator class
Cpcl::octree::OctreeLeafNodeBreadthFirstIterator< OctreeT >	Octree leaf node iterator class
►Cpcl::octree::OctreeDepthFirstIterator< OctreeT >	Octree iterator class
Cpcl::octree::OctreeLeafNodeDepthFirstIterator< OctreeT >	Octree leaf node iterator class
Cpcl::device::OctreeIteratorDevice< CTA_SIZE, STACK_DEPTH >
Cpcl::device::OctreeIteratorDeviceNS
Cpcl::octree::OctreeKey	Octree key class
►Cpcl::octree::OctreeNode	Abstract octree node class
Cpcl::octree::BufferedBranchNode< ContainerT >
Cpcl::octree::OctreeBranchNode< ContainerT >	Abstract octree branch class
Cpcl::octree::OctreeLeafNode< ContainerT >	Abstract octree leaf class
Cpcl::outofcore::OutofcoreOctreeBaseNode< ContainerT, PointT >	OutofcoreOctreeBaseNode Class internally representing nodes of an outofcore octree, with accessors to its data via the pcl::outofcore::OutofcoreOctreeDiskContainer class or pcl::outofcore::OutofcoreOctreeRamContainer class, whichever it is templated against.
Cpcl::octree::OctreeNodePool< NodeT >	Octree node pool
Cpcl::device::OctreePriorityIteratorDevice
CON_2dPoint
CON_2dVector
CON_2fPoint
CON_2fVector
CON_3DM_BIG_CHUNK
CON_3DM_CHUNK
CON_3dmAnnotationSettings
CON_3dmApplication
CON_3dmConstructionPlane
CON_3dmConstructionPlaneGridDefaults
CON_3dmGoo
CON_3dmIOSettings
CON_3dmNotes
CON_3dmPageSettings
CON_3dmProperties
CON_3dmRenderSettings
CON_3dmRevisionHistory
CON_3dmSettings
CON_3dmUnitsAndTolerances
CON_3dmView
CON_3dmViewPosition
CON_3dmViewTraceImage
CON_3dmWallpaperImage
CON_3dPoint
CON_3dRay
►CON_3dVector
CON_PlaneEquation
CON_3fPoint
CON_3fVector
CON_4dPoint
CON_4fPoint
CON_Base64EncodeStream
CON_BezierCage
CON_BezierCurve
CON_BezierSurface
►CON_BinaryArchive
CON_BinaryArchiveBuffer
CON_BinaryFile
CON_Read3dmBufferArchive
CON_Write3dmBufferArchive
CON_BoundingBox
CON_Box
CON_BrepRegionTopology
CON_BrepTrimPoint
CON_Buffer
CON_BumpFunction
CON_CheckSum
►CON_Circle
CON_Arc
►CON_ClassArray< T >
CON_ObjectArray< ON_Texture >
CON_ObjectArray< ON_HatchPattern >
CON_ObjectArray< ON_Group >
CON_ObjectArray< ON_Layer >
CON_ObjectArray< ON_InstanceDefinition >
►CON_ObjectArray< ON_BrepTrim >
CON_BrepTrimArray
CON_ObjectArray< ON_Linetype >
CON_ObjectArray< ON_Font >
►CON_ObjectArray< ON_BrepFaceSide >
CON_BrepFaceSideArray
►CON_ObjectArray< ON_BrepEdge >
CON_BrepEdgeArray
CON_ObjectArray< ON_Material >
CON_ObjectArray< ON_TextureMapping >
►CON_ObjectArray< ON_BrepLoop >
CON_BrepLoopArray
►CON_ObjectArray< ON_BrepRegion >
CON_BrepRegionArray
►CON_ObjectArray< ON_BrepFace >
CON_BrepFaceArray
CON_ObjectArray< ON_DimStyle >
►CON_ObjectArray< ON_BrepVertex >
CON_BrepVertexArray
CON_ObjectArray< T >
CON_ClassArray< ON_3dmConstructionPlane >
CON_ClassArray< ON_3dmView >
CON_ClassArray< ON_CurveProxyHistory >
CON_ClassArray< ON_HatchLine >
CON_ClassArray< ON_Localizer >
CON_ClassArray< ON_MappingRef >
CON_ClassArray< ON_MaterialRef >
CON_ClassArray< ON_PlugInRef >
CON_ClassArray< ON_TextureCoordinates >
CON_ClassArray< ON_UserString >
CON_ClassArray< ONX_Model_Object >
CON_ClassArray< ONX_Model_RenderLight >
CON_ClassArray< ONX_Model_UserData >
CON_ClassId
CON_ClippingPlane
CON_ClippingPlaneInfo
CON_ClippingRegion
CON_Color
CON_CompressedBuffer
CON_CompressStream
CON_Cone
CON_CurveProxyHistory
CON_Cylinder
CON_DecodeBase64
CON_DisplayMaterialRef
CON_EarthAnchorPoint
CON_Ellipse
CON_Evaluator
CON_FileIterator
CON_FileStream
►CON_FixedSizePool
CON_SimpleFixedSizePool< T >
CON_FixedSizePoolIterator
CON_HatchLine
CON_HatchLoop
CON_Interval
CON_Line
CON_LinetypeSegment
CON_Localizer
CON_LocalZero1
CON_MappingChannel
CON_MappingRef
CON_MappingTag
CON_MaterialRef
CON_Matrix
CON_MeshCurvatureStats
CON_MeshCurveParameters
CON_MeshFace
CON_MeshFaceSide
CON_MeshNgon
CON_MeshNgonList
CON_MeshParameters
CON_MeshPart
CON_MeshPartition
CON_MeshTopology
CON_MeshTopologyEdge
CON_MeshTopologyFace
CON_MeshTopologyVertex
►CON_Object
CON_3dmObjectAttributes
►CON_Bitmap
CON_EmbeddedBitmap
►CON_WindowsBitmap
CON_WindowsBitmapEx
CON_BrepFaceSide
CON_BrepRegion
CON_DimStyle
CON_DocumentUserStringList
CON_EmbeddedFile
CON_Font
►CON_Geometry
►CON_Annotation
CON_AngularDimension
CON_Leader
CON_LinearDimension
CON_RadialDimension
CON_TextEntity
►CON_Annotation2
CON_AngularDimension2
CON_Leader2
CON_LinearDimension2
CON_OrdinateDimension2
CON_RadialDimension2
CON_TextEntity2
CON_AnnotationArrow
CON_Brep
CON_BrepLoop
►CON_Curve
CON_ArcCurve
CON_CurveOnSurface
►CON_CurveProxy
CON_BrepEdge
CON_BrepTrim
CON_PolyEdgeSegment
CON_LineCurve
CON_NurbsCurve
►CON_PolyCurve
CON_PolyEdgeCurve
CON_PolylineCurve
CON_DetailView
CON_Hatch
CON_InstanceDefinition
CON_InstanceRef
CON_Light
CON_Mesh
CON_MeshEdgeRef
CON_MeshFaceRef
CON_MeshVertexRef
CON_MorphControl
CON_NurbsCage
►CON_Point
CON_AnnotationTextDot
CON_BrepVertex
CON_PointCloud
CON_PointGrid
►CON_Surface
CON_Extrusion
CON_NurbsSurface
►CON_PlaneSurface
CON_ClippingPlaneSurface
CON_RevSurface
CON_SumSurface
►CON_SurfaceProxy
CON_BrepFace
CON_OffsetSurface
CON_TextDot
CON_Viewport
CON_Group
CON_HatchPattern
CON_HistoryRecord
CON_Layer
CON_Linetype
CON_Material
CON_Texture
CON_TextureMapping
►CON_UserData
CON_DimensionExtra
CON_TextExtra
CON_UnknownUserData
CON_UserStringList
CON_UserDataHolder
CON_ObjRef
CON_ObjRef_IRefID
CON_ObjRefEvaluationParameter
CON_OffsetSurfaceFunction
CON_OffsetSurfaceValue
CON_Plane
CON_PlugInRef
CON_PolyEdgeHistory
CON_PolynomialCurve
CON_PolynomialSurface
CON_RANDOM_NUMBER_CONTEXT
►CON_RenderingAttributes
CON_ObjectRenderingAttributes
CON_RTree
CON_RTreeBBox
CON_RTreeBranch
CON_RTreeCapsule
CON_RTreeIterator
CON_RTreeLeaf
CON_RTreeMemPool
CON_RTreeNode
CON_RTreeSearchResult
CON_RTreeSphere
CON_SerialNumberMap
CON_SimpleArray< T >
CON_SimpleArray< bool >
CON_SimpleArray< class ON_BumpFunction >
CON_SimpleArray< class ON_Value * >
CON_SimpleArray< double * >
CON_SimpleArray< double >
CON_SimpleArray< int >
►CON_SimpleArray< ON_2dex >
CON_2dexMap
►CON_SimpleArray< ON_2dPoint >
CON_2dPointArray
►CON_SimpleArray< ON_2dVector >
CON_2dVectorArray
►CON_SimpleArray< ON_2fPoint >
CON_2fPointArray
►CON_SimpleArray< ON_2fVector >
CON_2fVectorArray
CON_SimpleArray< ON_3DM_BIG_CHUNK >
►CON_SimpleArray< ON_3dPoint >
►CON_3dPointArray
CON_Polyline
►CON_SimpleArray< ON_3dVector >
CON_3dVectorArray
►CON_SimpleArray< ON_3fPoint >
CON_3fPointArray
►CON_SimpleArray< ON_3fVector >
CON_3fVectorArray
►CON_SimpleArray< ON_4dPoint >
CON_4dPointArray
►CON_SimpleArray< ON_4fPoint >
CON_4fPointArray
CON_SimpleArray< ON_Bitmap * >
CON_SimpleArray< ON_BrepTrimPoint >
CON_SimpleArray< ON_ClippingPlaneInfo >
CON_SimpleArray< ON_Color >
►CON_SimpleArray< ON_Curve * >
CON_CurveArray
CON_SimpleArray< ON_DisplayMaterialRef >
CON_SimpleArray< ON_HatchLoop * >
CON_SimpleArray< ON_HistoryRecord * >
CON_SimpleArray< ON_Interval >
CON_SimpleArray< ON_LinetypeSegment >
CON_SimpleArray< ON_MappingChannel >
CON_SimpleArray< ON_MeshFace >
CON_SimpleArray< ON_MeshTopologyEdge >
CON_SimpleArray< ON_MeshTopologyFace >
CON_SimpleArray< ON_MeshTopologyVertex >
CON_SimpleArray< ON_ObjRef_IRefID >
CON_SimpleArray< ON_OffsetSurfaceValue >
►CON_SimpleArray< ON_Surface * >
CON_SurfaceArray
CON_SimpleArray< ON_SurfaceCurvature >
►CON_SimpleArray< ON_UUID >
CON_UuidList
►CON_SimpleArray< ON_UuidIndex >
CON_UuidIndexList
►CON_SimpleArray< ON_UuidPair >
CON_UuidPairList
CON_SimpleArray< struct ON_MeshPart >
►CON_SpaceMorph
CON_BezierCageMorph
CON_CageMorph
CON_Sphere
CON_String
CON_Sum
CON_SurfaceCurvature
CON_SurfaceProperties
CON_TensorProduct
CON_TextLog
CON_TextureCoordinates
CON_Torus
CON_U
CON_UncompressStream
CON_UnicodeErrorParameters
CON_UnitSystem
CON_UserString
CON_UUID
CON_UuidPair
CON_WindowsBITMAPINFO
CON_WindowsBITMAPINFOHEADER
CON_WindowsRGBQUAD
CON_Workspace
►CON_wString
CON_Annotation2Text
CON_Xform
CONX_Model
CONX_Model_Object
CONX_Model_RenderLight
CONX_Model_UserData
Cpcl::io::openni2::OpenNI2Device
Cpcl::io::openni2::OpenNI2DeviceInfo
Cpcl::io::openni2::OpenNI2DeviceManager
Cpcl::io::openni2::OpenNI2TimerFilter
Cpcl::io::openni2::OpenNI2VideoMode
COpenNICapture
►Copenni_wrapper::OpenNIDevice	Class representing an astract device for OpenNI devices: Primesense PSDK, Microsoft Kinect, Asus Xtion Pro/Live
Copenni_wrapper::DeviceKinect	Concrete implementation of the interface OpenNIDevice for a MS Kinect device
Copenni_wrapper::DeviceONI	Concrete implementation of the interface OpenNIDevice for a virtual device playing back an ONI file
Copenni_wrapper::DevicePrimesense	Concrete implementation of the interface OpenNIDevice for a Primesense device
Copenni_wrapper::DeviceXtionPro	Concrete implementation of the interface OpenNIDevice for a Asus Xtion Pro device
Copenni_wrapper::OpenNIDriver	Driver class implemented as Singleton
Cpcl::cuda::OpenNIRGB	Simple structure holding RGB data
Cpcl::io::OrganizedConversion< PointT, enableColor >
Cpcl::io::OrganizedConversion< PointT, false >
Cpcl::io::OrganizedConversion< PointT, true >
►Cpcl::OrganizedIndexIterator	Base class for iterators on 2-dimensional maps like images/organized clouds etc
Cpcl::LineIterator	Organized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm
Cpcl::OrganizedNeighborSearch< PointT >	OrganizedNeighborSearch class
Cpcl::gpu::people::OrganizedPlaneDetector
Cpcl::io::OrganizedPointCloudCompression< PointT >
Cpcl::cuda::OrganizedRadiusSearch< CloudPtr >	Kernel to compute a radius neighborhood given a organized point cloud (aka range image cloud)
Cpcl::cuda::OrganizedRadiusSearch< CloudConstPtr >
Cpcl::recognition::ObjRecRANSAC::OrientedPointPair
Cpcl::recognition::ORRGraph< NodeData >
Cpcl::recognition::ORROctree	That's a very specialized and simple octree class
Cpcl::recognition::ORROctreeZProjection
►Cpcl::outofcore::OutofcoreAbstractMetadata
Cpcl::outofcore::OutofcoreOctreeBaseMetadata	Encapsulated class to read JSON metadata into memory, and write the JSON metadata associated with the octree root node
►Cpcl::outofcore::OutofcoreAbstractNodeContainer< PointT >
Cpcl::outofcore::OutofcoreOctreeRamContainer< PointT >	Storage container class which the outofcore octree base is templated against
►Cpcl::outofcore::OutofcoreAbstractNodeContainer< pcl::PointXYZ >
Cpcl::outofcore::OutofcoreOctreeDiskContainer< PointT >	Class responsible for serialization and deserialization of out of core point data
Cpcl::outofcore::OutofcoreIteratorBase< PointT, ContainerT >	Abstract octree iterator class
►Cpcl::outofcore::OutofcoreIteratorBase< pcl::PointXYZ, OutofcoreOctreeDiskContainer< pcl::PointXYZ > >
Cpcl::outofcore::OutofcoreBreadthFirstIterator< PointT, ContainerT >
Cpcl::outofcore::OutofcoreDepthFirstIterator< PointT, ContainerT >
Cpcl::outofcore::OutofcoreOctreeBase< ContainerT, PointT >	This code defines the octree used for point storage at Urban Robotics
Cpcl::outofcore::OutofcoreOctreeBase< OutofcoreOctreeDiskContainer< pcl::PointXYZ >, pcl::PointXYZRGB >
Cpcl::outofcore::OutofcoreOctreeNodeMetadata	Encapsulated class to read JSON metadata into memory, and write the JSON metadata for each node
Cpcl::outofcore::OutofcoreParams
Cpcl::recognition::ObjRecRANSAC::Output	This is an output item of the ObjRecRANSAC::recognize() method
►Cstd::pair
Cpcl::PPFHashMapSearch::HashKeyStruct	Data structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class
Cpcl::PairwisePotential
Cboost::parallel_edge_traits< eigen_listS >
Cboost::parallel_edge_traits< eigen_vecS >
Cpcl::cuda::parallel_random_generator
CBFGS< FunctorType >::Parameters
Cpcl::common::NormalGenerator< T >::Parameters
Cpcl::common::UniformGenerator< T >::Parameters
Cpcl::NarfDescriptor::Parameters
Cpcl::NarfKeypoint::Parameters	Parameters used in this class
Cpcl::PolynomialCalculationsT< real >::Parameters	Parameters used in this class
Cpcl::PosesFromMatches::Parameters	Parameters used in this class
Cpcl::RangeImageBorderExtractor::Parameters	Parameters used in this class
Cpcl::gpu::ParticleFilterGPUTracker
COutofcoreCloud::PcdQueueItem
►Cpcl::PCLBase< PointT >	PCL base class
►Cpcl::Feature< PointWithRange, BorderDescription >
Cpcl::RangeImageBorderExtractor	Extract obstacle borders from range images, meaning positions where there is a transition from foreground to background
►Cpcl::Feature< PointWithRange, Narf36 >
Cpcl::NarfDescriptor	Computes NARF feature descriptors for points in a range image See B
►Cpcl::Filter< NormalT >
Cpcl::NormalRefinement< NormalT >	Normal vector refinement class
Cpcl::OrganizedMultiPlaneSegmentation< pcl::PointXYZRGBA, pcl::Normal, pcl::Label >
►Cpcl::RegionGrowing< PointT, pcl::Normal >
Cpcl::RegionGrowingRGB< PointT, NormalT >	Implements the well known Region Growing algorithm used for segmentation based on color of points
Cpcl::Registration< PointT, PointT >
Cpcl::Registration< PointT, PointT, float >
Cpcl::ApproximateProgressiveMorphologicalFilter< PointT >	Implements the Progressive Morphological Filter for segmentation of ground points
Cpcl::ConditionalEuclideanClustering< PointT >	ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition
Cpcl::EuclideanClusterExtraction< PointT >	EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
Cpcl::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
►Cpcl::Filter< PointT >	Filter represents the base filter class
Cpcl::Convolution< PointInT >
Cpcl::Convolution< ImageType >
Cpcl::VoxelGrid< pcl::PointXYZRGB >
►Cpcl::VoxelGrid< PointTarget >
Cpcl::VoxelGridCovariance< PointTarget >
►Cpcl::VoxelGrid< pcl::PointXYZRGBL >
Cpcl::VoxelGridLabel
Cpcl::ApproximateVoxelGrid< PointT >	ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
Cpcl::BilateralFilter< PointT >	A bilateral filter implementation for point cloud data
Cpcl::ConditionalRemoval< PointT >	ConditionalRemoval filters data that satisfies certain conditions
Cpcl::Convolution< PointT >	A 2D convolution class
►Cpcl::FastBilateralFilter< PointT >	Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper:
Cpcl::FastBilateralFilterOMP< PointT >	Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper:
►Cpcl::FilterIndices< PointT >	FilterIndices represents the base class for filters that are about binary point removal
Cpcl::PassThrough< PointInT >
Cpcl::CovarianceSampling< PointT, PointNT >	Point Cloud sampling based on the 6D covariances
Cpcl::CropBox< PointT >	CropBox is a filter that allows the user to filter all the data inside of a given box
Cpcl::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
Cpcl::ExtractIndices< PointT >	ExtractIndices extracts a set of indices from a point cloud
Cpcl::FarthestPointSampling< PointT >	FarthestPointSampling applies farthest point sampling using euclidean distance, starting with a random point, utilizing a naive method
Cpcl::FrustumCulling< PointT >	FrustumCulling filters points inside a frustum given by pose and field of view of the camera
Cpcl::GridMinimum< PointT >	GridMinimum assembles a local 2D grid over a given PointCloud, and downsamples the data
Cpcl::LocalMaximum< PointT >	LocalMaximum downsamples the cloud, by eliminating points that are locally maximal
Cpcl::ModelOutlierRemoval< PointT >	ModelOutlierRemoval filters points in a cloud based on the distance between model and point
Cpcl::NormalSpaceSampling< PointT, NormalT >	NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point
Cpcl::PassThrough< PointT >	PassThrough passes points in a cloud based on constraints for one particular field of the point type
Cpcl::RadiusOutlierRemoval< PointT >	RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have
Cpcl::RandomSample< PointT >	RandomSample applies a random sampling with uniform probability
Cpcl::ShadowPoints< PointT, NormalT >	ShadowPoints removes the ghost points appearing on edge discontinuties
Cpcl::StatisticalOutlierRemoval< PointT >	StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
Cpcl::UniformSampling< PointT >	UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
Cpcl::experimental::advanced::FunctorFilter< PointT, FunctionObject >	Filter point clouds and indices based on a function object passed in the ctor
Cpcl::MedianFilter< PointT >	Implementation of the median filter
Cpcl::ProjectInliers< PointT >	ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
Cpcl::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
►Cpcl::VoxelGrid< PointT >	VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
Cpcl::VoxelGridCovariance< PointT >	A searchable voxel structure containing the mean and covariance of the data
Cpcl::VoxelGridOcclusionEstimation< PointT >	VoxelGrid to estimate occluded space in the scene
Cpcl::GrabCut< PointT >	Implementation of the GrabCut segmentation in "GrabCut — Interactive Foreground Extraction using Iterated Graph Cuts" by Carsten Rother, Vladimir Kolmogorov and Andrew Blake
Cpcl::LabeledEuclideanClusterExtraction< PointT >	LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info
Cpcl::MinCutSegmentation< PointT >	This class implements the segmentation algorithm based on minimal cut of the graph
Cpcl::MomentOfInertiaEstimation< PointT >	Implements the method for extracting features based on moment of inertia
Cpcl::Morphology< PointT >
Cpcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >	OrganizedConnectedComponentSegmentation allows connected components to be found within organized point cloud data, given a comparison function
►Cpcl::OrganizedEdgeBase< PointT, PointLT >	OrganizedEdgeBase, OrganizedEdgeFromRGB, OrganizedEdgeFromNormals, and OrganizedEdgeFromRGBNormals find 3D edges from an organized point cloud data
►Cpcl::OrganizedEdgeFromNormals< PointT, PointNT, PointLT >
Cpcl::OrganizedEdgeFromRGBNormals< PointT, PointNT, PointLT >
►Cpcl::OrganizedEdgeFromRGB< PointT, PointLT >
Cpcl::OrganizedEdgeFromRGBNormals< PointT, PointNT, PointLT >
Cpcl::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
Cpcl::PCA< PointT >	Principal Component analysis (PCA) class
Cpcl::ProgressiveMorphologicalFilter< PointT >	Implements the Progressive Morphological Filter for segmentation of ground points
Cpcl::RegionGrowing< PointT, NormalT >	Implements the well known Region Growing algorithm used for segmentation
►Cpcl::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
Cpcl::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
Cpcl::SegmentDifferences< PointT >	SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold
Cpcl::StatisticalMultiscaleInterestRegionExtraction< PointT >	Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach
Cpcl::SupervoxelClustering< PointT >	Implements a supervoxel algorithm based on voxel structure, normals, and rgb values
Cpcl::SurfelSmoothing< PointT, PointNT >
Cpcl::registration::ELCH< PointT >	ELCH (Explicit Loop Closing Heuristic) class
►Cpcl::PCLBase< pcl::PCLPointCloud2 >
►Cpcl::Filter< pcl::PCLPointCloud2 >	Filter represents the base filter class
►Cpcl::FilterIndices< pcl::PCLPointCloud2 >	FilterIndices represents the base class for filters that are about binary point removal
Cpcl::CropBox< pcl::PCLPointCloud2 >	CropBox is a filter that allows the user to filter all the data inside of a given box
Cpcl::ExtractIndices< pcl::PCLPointCloud2 >	ExtractIndices extracts a set of indices from a point cloud
Cpcl::PassThrough< pcl::PCLPointCloud2 >	PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
Cpcl::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
Cpcl::RandomSample< pcl::PCLPointCloud2 >	RandomSample applies a random sampling with uniform probability
Cpcl::StatisticalOutlierRemoval< pcl::PCLPointCloud2 >	StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
Cpcl::ProjectInliers< pcl::PCLPointCloud2 >	ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
Cpcl::VoxelGrid< pcl::PCLPointCloud2 >	VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
►Cpcl::PCLBase< PointFeature >
Cpcl::PyramidFeatureHistogram< PointFeature >	Class that compares two sets of features by using a multiscale representation of the features inside a pyramid
►Cpcl::PCLBase< PointIn >
Cpcl::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
►Cpcl::PCLBase< PointInT >
►Cpcl::Feature< PointInT, ReferenceFrame >
►Cpcl::FeatureFromNormals< PointInT, PointNT, ReferenceFrame >
Cpcl::BOARDLocalReferenceFrameEstimation< PointInT, PointNT, PointOutT >	BOARDLocalReferenceFrameEstimation implements the BOrder Aware Repeatable Directions algorithm for local reference frame estimation as described here:
Cpcl::FLARELocalReferenceFrameEstimation< PointInT, PointNT, PointOutT, SignedDistanceT >	FLARELocalReferenceFrameEstimation implements the Fast LocAl Reference framE algorithm for local reference frame estimation as described here:
►Cpcl::SHOTLocalReferenceFrameEstimation< PointInT, ReferenceFrame >
Cpcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT >	SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor
Cpcl::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT >	SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor
►Cpcl::Feature< PointInT, pcl::ESFSignature640 >
Cpcl::ESFEstimation< PointInT, PointOutT >	ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points
►Cpcl::Feature< PointInT, pcl::UniqueShapeContext1960 >
Cpcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >	UniqueShapeContext implements the Unique Shape Context Descriptor described here:
►Cpcl::Feature< PointInT, GASDSignature512 >
Cpcl::GASDEstimation< PointInT, PointOutT >	GASDEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given point cloud dataset given XYZ data
►Cpcl::Keypoint< PointInT, pcl::PointWithScale >
Cpcl::BriskKeypoint2D< PointInT, PointOutT, IntensityT >	Detects BRISK interest points based on the original code and paper reference by
►Cpcl::Keypoint< PointWithRange, int >
Cpcl::NarfKeypoint	NARF (Normal Aligned Radial Feature) keypoints
►Cpcl::Keypoint< PointInT, PointOutT >
Cpcl::AgastKeypoint2DBase< PointInT, PointOutT, IntensityT >	Detects 2D AGAST corner points
Cpcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >	HarrisKeypoint2D detects Harris corners family points
Cpcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >	HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals
Cpcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >	Keypoint detector for detecting corners in 3D (XYZ), 2D (intensity) AND mixed versions of these
Cpcl::ISSKeypoint3D< PointInT, PointOutT, NormalT >	ISSKeypoint3D detects the Intrinsic Shape Signatures keypoints for a given point cloud
Cpcl::SIFTKeypoint< PointInT, PointOutT >	SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity
Cpcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT >	SUSANKeypoint implements a RGB-D extension of the SUSAN detector including normal directions variation in top of intensity variation
Cpcl::TrajkovicKeypoint2D< PointInT, PointOutT, IntensityT >	TrajkovicKeypoint2D implements Trajkovic and Hedley corner detector on organized point cloud using intensity information
Cpcl::TrajkovicKeypoint3D< PointInT, PointOutT, NormalT >	TrajkovicKeypoint3D implements Trajkovic and Hedley corner detector on point cloud using geometric information
►Cpcl::Keypoint< PointT, PointT >
Cpcl::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
►Cpcl::tracking::Tracker< PointInT, Eigen::Affine3f >
Cpcl::tracking::PyramidalKLTTracker< PointInT, IntensityT >	Pyramidal Kanade Lucas Tomasi tracker
►Cpcl::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
Cpcl::BilateralUpsampling< PointInT, PointOutT >	Bilateral filtering implementation, based on the following paper:
Cpcl::MovingLeastSquares< PointInT, PointOutT >	MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation
Cpcl::ColorGradientDOTModality< PointInT >
Cpcl::ColorGradientModality< PointInT >	Modality based on max-RGB gradients
Cpcl::ColorModality< PointInT >
►Cpcl::Feature< PointInT, PointOutT >	Feature represents the base feature class
►Cpcl::FeatureFromNormals< PointInT, PointNT, pcl::PFHRGBSignature250 >
Cpcl::PFHRGBEstimation< PointInT, PointNT, PointOutT >	Similar to the Point Feature Histogram descriptor, but also takes color into account
►Cpcl::FeatureFromNormals< PointInT, PointNT, pcl::FPFHSignature33 >
Cpcl::FPFHEstimation< PointInT, PointNT, PointOutT >	FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
►Cpcl::FeatureFromNormals< PointInT, PointNT, pcl::ShapeContext1980 >
Cpcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT >	ShapeContext3DEstimation implements the 3D shape context descriptor as described in:
►Cpcl::FeatureFromNormals< PointInT, PointNT, pcl::VFHSignature308 >
Cpcl::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:
Cpcl::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:
Cpcl::VFHEstimation< PointInT, PointNT, PointOutT >	VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals
►Cpcl::FeatureFromNormals< PointInT, PointNT, pcl::Histogram< 90 > >
Cpcl::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:
►Cpcl::FeatureFromNormals< PointT, PointNT, PointFeature >
Cpcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature >	Normal-based feature signature estimation class
►Cpcl::FeatureFromNormals< PointInT, PointNT, pcl::PrincipalCurvatures >
Cpcl::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
►Cpcl::FeatureFromNormals< PointInT, PointNT, pcl::PFHSignature125 >
Cpcl::PFHEstimation< PointInT, PointNT, PointOutT >	PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
►Cpcl::GASDEstimation< PointInT, GASDSignature984 >
Cpcl::GASDColorEstimation< PointInT, PointOutT >	GASDColorEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given point cloud dataset given XYZ and RGB data
Cpcl::IntegralImageNormalEstimation< pcl::PointXYZRGBA, pcl::Normal >
Cpcl::NormalEstimation< PointInT, PointNT >
Cpcl::DifferenceOfNormalsEstimation< PointInT, PointNT, PointOutT >	A Difference of Normals (DoN) scale filter implementation for point cloud data
►Cpcl::FeatureFromLabels< PointInT, PointLT, PointOutT >
Cpcl::GFPFHEstimation< PointInT, PointLT, PointOutT >	GFPFHEstimation estimates the Global Fast Point Feature Histogram (GFPFH) descriptor for a given point cloud dataset containing points and labels
►Cpcl::FeatureFromNormals< PointInT, PointNT, PointOutT >
►Cpcl::FPFHEstimation< PointInT, PointNT, PointOutT >
Cpcl::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
Cpcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT352, pcl::ReferenceFrame >
Cpcl::SHOTEstimationBase< PointInT, PointNT, pcl::SHOT1344, pcl::ReferenceFrame >
Cpcl::BoundaryEstimation< PointInT, PointNT, PointOutT >	BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion
Cpcl::CPPFEstimation< PointInT, PointNT, PointOutT >	Class that calculates the "surflet" features for each pair in the given pointcloud
Cpcl::GRSDEstimation< PointInT, PointNT, PointOutT >	GRSDEstimation estimates the Global Radius-based Surface Descriptor (GRSD) for a given point cloud dataset containing points and normals
Cpcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT >	IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values
Cpcl::PPFEstimation< PointInT, PointNT, PointOutT >	Class that calculates the "surflet" features for each pair in the given pointcloud
Cpcl::PPFRGBEstimation< PointInT, PointNT, PointOutT >
Cpcl::PPFRGBRegionEstimation< PointInT, PointNT, PointOutT >
Cpcl::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
Cpcl::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
Cpcl::IntegralImageNormalEstimation< PointInT, PointOutT >	Surface normal estimation on organized data using integral images
Cpcl::IntensitySpinEstimation< PointInT, PointOutT >	IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity
Cpcl::LinearLeastSquaresNormalEstimation< PointInT, PointOutT >	Surface normal estimation on dense data using a least-squares estimation based on a first-order Taylor approximation
Cpcl::MomentInvariantsEstimation< PointInT, PointOutT >	MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
►Cpcl::NormalEstimation< PointInT, PointOutT >	NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point
Cpcl::NormalEstimationOMP< PointInT, PointOutT >	NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
Cpcl::RIFTEstimation< PointInT, GradientT, PointOutT >	RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity
Cpcl::ROPSEstimation< PointInT, PointOutT >	This class implements the method for extracting RoPS features presented in the article "Rotational Projection Statistics for 3D Local Surface Description and Object Recognition" by Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu and Jianwei Wan
Cpcl::SpinImageEstimation< PointInT, PointNT, PointOutT >	Estimates spin-image descriptors in the given input points
►Cpcl::Keypoint< ImageType >	Keypoint represents the base class for key points
►Cpcl::AgastKeypoint2DBase< PointInT, pcl::PointUV, pcl::common::IntensityFieldAccessor< PointInT > >
Cpcl::AgastKeypoint2D< PointInT, PointOutT >	Detects 2D AGAST corner points
►Cpcl::AgastKeypoint2DBase< pcl::PointXYZ, pcl::PointUV, pcl::common::IntensityFieldAccessor< pcl::PointXYZ > >
Cpcl::AgastKeypoint2D< pcl::PointXYZ, pcl::PointUV >	Detects 2D AGAST corner points
►Cpcl::PCLSurfaceBase< PointInT >	Pure abstract class
►Cpcl::SurfaceReconstruction< PointNT >
Cpcl::GridProjection< PointNT >	Grid projection surface reconstruction method
►Cpcl::MarchingCubes< PointNT >	The marching cubes surface reconstruction algorithm
Cpcl::MarchingCubesHoppe< PointNT >	The marching cubes surface reconstruction algorithm, using a signed distance function based on the distance from tangent planes, proposed by Hoppe et
Cpcl::MarchingCubesRBF< PointNT >	The marching cubes surface reconstruction algorithm, using a signed distance function based on radial basis functions
Cpcl::Poisson< PointNT >	The Poisson surface reconstruction algorithm
►Cpcl::MeshConstruction< PointInT >	MeshConstruction represents a base surface reconstruction class
Cpcl::ConcaveHull< PointInT >	ConcaveHull (alpha shapes) using libqhull library
Cpcl::ConvexHull< PointInT >	ConvexHull using libqhull library
Cpcl::GreedyProjectionTriangulation< PointInT >	GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections
Cpcl::OrganizedFastMesh< PointInT >	Simple triangulation/surface reconstruction for organized point clouds
Cpcl::SurfaceReconstruction< PointInT >	SurfaceReconstruction represents a base surface reconstruction class
Cpcl::SurfaceNormalModality< PointInT >	Modality based on surface normals
►Cpcl::tracking::Tracker< PointInT, StateT >	Tracker represents the base tracker class
►Cpcl::tracking::ParticleFilterTracker< PointInT, StateT >	ParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method
►Cpcl::tracking::KLDAdaptiveParticleFilterTracker< PointInT, StateT >	KLDAdaptiveParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method
Cpcl::tracking::KLDAdaptiveParticleFilterOMPTracker< PointInT, StateT >	KLDAdaptiveParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method
Cpcl::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
►Cpcl::PCLBase< PointModelT >
►Cpcl::CorrespondenceGrouping< PointModelT, PointSceneT >	Abstract base class for Correspondence Grouping algorithms
Cpcl::GeometricConsistencyGrouping< PointModelT, PointSceneT >	Class implementing a 3D correspondence grouping enforcing geometric consistency among feature correspondences
Cpcl::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
►Cpcl::PCLBase< PointSource >
Cpcl::Feature< PointSource, PointFeature >
►Cpcl::Registration< PointSource, PointTarget, float >
►Cpcl::IterativeClosestPoint< PointSource, PointTarget, float >
Cpcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget, Scalar >	GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al
Cpcl::IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar >	IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend
Cpcl::IterativeClosestPointWithNormals< PointSource, PointTarget, Scalar >	IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformation estimated based on Point to Plane distances by default
Cpcl::JointIterativeClosestPoint< PointSource, PointTarget, Scalar >	JointIterativeClosestPoint extends ICP to multiple frames which share the same transform
►Cpcl::registration::FPCSInitialAlignment< PointSource, PointTarget, pcl::Normal, float >
Cpcl::registration::KFPCSInitialAlignment< PointSource, PointTarget, NormalT, Scalar >	KFPCSInitialAlignment computes corresponding four point congruent sets based on keypoints as described in: "Markerless point cloud registration with keypoint-based 4-points congruent sets", Pascal Theiler, Jan Dirk Wegner, Konrad Schindler
Cpcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >	IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm
Cpcl::NormalDistributionsTransform< PointSource, PointTarget, Scalar >	A 3D Normal Distribution Transform registration implementation for point cloud data
Cpcl::registration::FPCSInitialAlignment< PointSource, PointTarget, NormalT, Scalar >	FPCSInitialAlignment computes corresponding four point congruent sets as described in: "4-points congruent sets for robust pairwise surface registration", Dror Aiger, Niloy Mitra, Daniel Cohen-Or
►Cpcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, float >
Cpcl::registration::CorrespondenceEstimation< PointSource, PointTarget, Scalar >	CorrespondenceEstimation represents a simple class for determining correspondences between target and query point sets/features, using nearest neighbor search
Cpcl::registration::CorrespondenceEstimationBackProjection< PointSource, PointTarget, NormalT, Scalar >	CorrespondenceEstimationBackprojection computes correspondences as points in the target cloud which have minimum
Cpcl::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
Cpcl::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
Cpcl::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
►Cpcl::Registration< PointSource, PointTarget, Scalar >	Registration represents the base registration class for general purpose, ICP-like methods
►Cpcl::IterativeClosestPoint< PointXYZRGBA, PointXYZRGBA, float >
►Cpcl::GeneralizedIterativeClosestPoint< PointXYZRGBA, PointXYZRGBA >
Cpcl::GeneralizedIterativeClosestPoint6D	GeneralizedIterativeClosestPoint6D integrates L*a*b* color space information into the Generalized Iterative Closest Point (GICP) algorithm
Cpcl::NormalDistributionsTransform2D< PointSource, PointTarget >	NormalDistributionsTransform2D provides an implementation of the Normal Distributions Transform algorithm for scan matching
Cpcl::PPFRegistration< PointSource, PointTarget >	Class that registers two point clouds based on their sets of PPFSignatures
Cpcl::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
Cpcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >	Pose estimation and alignment class using a prerejective RANSAC routine
Cpcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, Scalar >	Abstract CorrespondenceEstimationBase class
►Cpcl::PCLBase< PointXYZRGB >
Cpcl::SeededHueSegmentation	SeededHueSegmentation
►Cpcl::PCLBase< PointXYZT >
Cpcl::ColorGradientModality< PointXYZT >
Cpcl::SurfaceNormalModality< PointXYZT >
Cpcl::cuda::PCLCUDABase< CloudT >	PCL base class
►CPCLCUDABase
►Cpcl_cuda::Filter< PointCloudSOA< Device > >
Cpcl_cuda::PassThrough< PointCloudSOA< Device > >
Cpcl_cuda::VoxelGrid< PointCloudSOA< Device > >
►Cpcl_cuda::Filter< PointCloudAOS< Device > >
Cpcl_cuda::PassThrough< PointCloudAOS< Device > >
Cpcl_cuda::VoxelGrid< PointCloudAOS< Device > >
►Cpcl_cuda::Filter< CloudT >	Removes points with x, y, or z equal to NaN
Cpcl_cuda::PassThrough< CloudT >	PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
Cpcl_cuda::VoxelGrid< CloudT >	VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
Cpcl::PCLHeader
Cpcl::visualization::PCLHistogramVisualizer	PCL histogram visualizer main class
Cpcl::PCLImage
Cpcl::visualization::PCLPlotter	PCL Plotter main class
Cpcl::PCLPointCloud2
Cpcl::PCLPointField
Cpcl::visualization::PCLSimpleBufferVisualizer	PCL simple buffer visualizer main class
Cpcl::visualization::PCLVisualizer	PCL Visualizer main class
Cpcl::gpu::people::PeopleDetector
Cpcl::Permutohedral	Implementation of a high-dimensional gaussian filtering using the permutohedral lattice
Cpcl::gpu::people::PersonAttribs
Cpcl::people::PersonClassifier< PointT >
Cpcl::people::PersonClassifier< pcl::RGB >
Cpcl::people::PersonCluster< PointT >	PersonCluster represents a class for representing information about a cluster containing a person
Cpcl::PFHRGBSignature250	A point structure representing the Point Feature Histogram with colors (PFHRGB)
Cpcl::PFHSignature125	A point structure representing the Point Feature Histogram (PFH)
Cpcl::PiecewiseLinearFunction	This provides functionalities to efficiently return values for piecewise linear function
Cpcl::recognition::ORROctreeZProjection::Pixel
Cpcl::gpu::kinfuLS::PixelRGB	Input/output pixel format for KinfuTracker
Cpcl::gpu::PixelRGB	Input/output pixel format for KinfuTracker
►Cpcl::PlanarPolygon< PointT >	PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space
Cpcl::PlanarRegion< PointT >	PlanarRegion represents a set of points that lie in a plane
Cpcl::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
Cpcl::io::ply::ply_parser	Class ply_parser parses a PLY file and generates appropriate atomic parsers for the body
Cpcl::poisson::Point3D< Real >
Cpcl::poisson::Point3D< float >
Cpoint_index_idx
Cpcl::PointCloud< PointT >	PointCloud represents the base class in PCL for storing collections of 3D points
Cpcl::PointCloud< EdgeData >
Cpcl::PointCloud< FaceData >
Cpcl::PointCloud< FeatureT >
Cpcl::PointCloud< float >
Cpcl::PointCloud< GlobalDescriptorT >
Cpcl::PointCloud< HalfEdgeData >
Cpcl::PointCloud< LocalDescriptorT >
Cpcl::PointCloud< ModelT >
Cpcl::PointCloud< NormalT >
Cpcl::PointCloud< pcl::device::prob_histogram >
Cpcl::PointCloud< pcl::GradientXY >
Cpcl::PointCloud< pcl::IntensityGradient >
Cpcl::PointCloud< pcl::InterestPoint >
Cpcl::PointCloud< pcl::Normal >
Cpcl::PointCloud< pcl::PointNormal >
Cpcl::PointCloud< pcl::PointUV >
Cpcl::PointCloud< pcl::PointXYZ >
Cpcl::PointCloud< pcl::PointXYZI >
Cpcl::PointCloud< pcl::PointXYZINormal >
Cpcl::PointCloud< pcl::PointXYZLAB >
Cpcl::PointCloud< pcl::PointXYZRGB >
Cpcl::PointCloud< pcl::PointXYZRGBA >
Cpcl::PointCloud< pcl::PointXYZRGBL >
Cpcl::PointCloud< pcl::RGB >
Cpcl::PointCloud< pcl::VFHSignature308 >
Cpcl::PointCloud< PointInT >
Cpcl::PointCloud< PointNT >
Cpcl::PointCloud< PointOutT >
Cpcl::PointCloud< PointSource >
Cpcl::PointCloud< PointTarget >
►Cpcl::PointCloud< PointWithRange >
►Cpcl::RangeImage	RangeImage is derived from pcl/PointCloud and provides functionalities with focus on situations where a 3D scene was captured from a specific view point
Cpcl::RangeImagePlanar	RangeImagePlanar 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
Cpcl::RangeImageSpherical	RangeImageSpherical is derived from the original range image and uses a slightly different spherical projection
►Cpcl::PointCloud< PointXYZRGBA >
Cpcl::BearingAngleImage	Class BearingAngleImage is used as an interface to generate Bearing Angle(BA) image
Cpcl::PointCloud< PointXYZT >
Cpcl::PointCloud< RGB >
Cpcl::PointCloud< SceneT >
Cpcl::PointCloud< unsigned char >
Cpcl::PointCloud< unsigned short >
Cpcl::PointCloud< VertexData >
Cpcl::cuda::PointCloudAOS< Storage >	PointCloudAOS represents an AOS (Array of Structs) PointCloud implementation for CUDA processing
►Cpcl::tracking::PointCloudCoherence< PointInT >	PointCloudCoherence is a base class to compute coherence between the two PointClouds
►Cpcl::tracking::NearestPairPointCloudCoherence< PointInT >	NearestPairPointCloudCoherence computes coherence between two pointclouds using the nearest point pairs
Cpcl::tracking::ApproxNearestPairPointCloudCoherence< PointInT >	ApproxNearestPairPointCloudCoherence computes coherence between two pointclouds using the approximate nearest point pairs
►Cpcl::visualization::PointCloudColorHandler< PointT >	Base Handler class for PointCloud colors
Cpcl::visualization::PointCloudColorHandlerCustom< PointT >	Handler for predefined user colors
Cpcl::visualization::PointCloudColorHandlerGenericField< PointT >	Generic field handler class for colors
Cpcl::visualization::PointCloudColorHandlerHSVField< PointT >	HSV handler class for colors
Cpcl::visualization::PointCloudColorHandlerLabelField< PointT >	Label field handler class for colors
Cpcl::visualization::PointCloudColorHandlerRGBAField< PointT >	RGBA handler class for colors
Cpcl::visualization::PointCloudColorHandlerRGBField< PointT >	RGB handler class for colors
Cpcl::visualization::PointCloudColorHandlerRGBHack< PointT >
Cpcl::visualization::PointCloudColorHandlerRandom< PointT >	Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen)
►Cpcl::visualization::PointCloudColorHandler< pcl::PCLPointCloud2 >	Base Handler class for PointCloud colors
Cpcl::visualization::PointCloudColorHandlerCustom< pcl::PCLPointCloud2 >	Handler for predefined user colors
Cpcl::visualization::PointCloudColorHandlerGenericField< pcl::PCLPointCloud2 >	Generic field handler class for colors
Cpcl::visualization::PointCloudColorHandlerHSVField< pcl::PCLPointCloud2 >	HSV handler class for colors
Cpcl::visualization::PointCloudColorHandlerLabelField< pcl::PCLPointCloud2 >	Label field handler class for colors
Cpcl::visualization::PointCloudColorHandlerRGBAField< pcl::PCLPointCloud2 >	RGBA handler class for colors
Cpcl::visualization::PointCloudColorHandlerRGBField< pcl::PCLPointCloud2 >	RGB handler class for colors
Cpcl::visualization::PointCloudColorHandlerRandom< pcl::PCLPointCloud2 >	Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen)
►Cpcl::visualization::PointCloudGeometryHandler< PointT >	Base handler class for PointCloud geometry
Cpcl::visualization::PointCloudGeometryHandlerCustom< PointT >	Custom handler class for PointCloud geometry
Cpcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT >	Surface normal handler class for PointCloud geometry
Cpcl::visualization::PointCloudGeometryHandlerXYZ< PointT >	XYZ handler class for PointCloud geometry
►Cpcl::visualization::PointCloudGeometryHandler< pcl::PCLPointCloud2 >	Base handler class for PointCloud geometry
Cpcl::visualization::PointCloudGeometryHandlerCustom< pcl::PCLPointCloud2 >	Custom handler class for PointCloud geometry
Cpcl::visualization::PointCloudGeometryHandlerSurfaceNormal< pcl::PCLPointCloud2 >	Surface normal handler class for PointCloud geometry
Cpcl::visualization::PointCloudGeometryHandlerXYZ< pcl::PCLPointCloud2 >	XYZ handler class for PointCloud geometry
►Cpcl::io::PointCloudImageExtractor< PointT >	Base Image Extractor class for organized point clouds
Cpcl::io::PointCloudImageExtractorFromLabelField< PointT >	Image Extractor which uses the data present in the "label" field to produce either monochrome or RGB image where different labels correspond to different colors
Cpcl::io::PointCloudImageExtractorFromNormalField< PointT >	Image Extractor which uses the data present in the "normal" field
Cpcl::io::PointCloudImageExtractorFromRGBField< PointT >	Image Extractor which uses the data present in the "rgb" or "rgba" fields to produce a color image with rgb8 encoding
►Cpcl::io::PointCloudImageExtractorWithScaling< PointT >	Image Extractor extension which provides functionality to apply scaling to the values extracted from a field
Cpcl::io::PointCloudImageExtractorFromCurvatureField< PointT >	Image Extractor which uses the data present in the "curvature" field to produce a curvature map (as a monochrome image with mono16 encoding)
Cpcl::io::PointCloudImageExtractorFromIntensityField< PointT >	Image Extractor which uses the data present in the "intensity" field to produce a monochrome intensity image (with mono16 encoding)
Cpcl::io::PointCloudImageExtractorFromZField< PointT >	Image Extractor which uses the data present in the "z" field to produce a depth map (as a monochrome image with mono16 encoding)
Cpcl::cuda::PointCloudSOA< Storage >	PointCloudSOA represents a SOA (Struct of Arrays) PointCloud implementation for CUDA processing
Cpcl::octree::PointCoding< PointT >	PointCoding class
Cpcl::octree::PointCoding< pcl::PointXYZRGB >
►Cpcl::tracking::PointCoherence< PointInT >	PointCoherence is a base class to compute coherence between the two points
Cpcl::tracking::DistanceCoherence< PointInT >	DistanceCoherence computes coherence between two points from the distance between them
Cpcl::tracking::HSVColorCoherence< PointInT >	HSVColorCoherence computes coherence between the two points from the color difference between them
Cpcl::tracking::NormalCoherence< PointInT >	NormalCoherence computes coherence between two points from the angle between their normals
Cpcl::PointDataAtOffset< PointT >	A datatype that enables type-correct comparisons
Cpcl::PointDataAtOffset< pcl::PointXYZRGB >
Cpcl::traits::detail::PointFieldTypes	Enumeration for different numerical types
Cpcl::PointIndices
CPointIntensity
Cpcl::cuda::PointIterator< Storage, T >
Cpcl::cuda::PointIterator< Device, T >
Cpcl::cuda::PointIterator< Host, T >
Cpcl::visualization::PointPickingEvent	/brief Class representing 3D point picking events
Cpcl::PointRepresentation< PointT >	PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector
Cpcl::PointRepresentation< FeatureT >
►Cpcl::PointRepresentation< FPFHSignature33 >
►Cpcl::DefaultFeatureRepresentation< FPFHSignature33 >
Cpcl::DefaultPointRepresentation< FPFHSignature33 >
►Cpcl::PointRepresentation< GASDSignature512 >
►Cpcl::DefaultFeatureRepresentation< GASDSignature512 >
Cpcl::DefaultPointRepresentation< GASDSignature512 >
►Cpcl::PointRepresentation< GASDSignature7992 >
►Cpcl::DefaultFeatureRepresentation< GASDSignature7992 >
Cpcl::DefaultPointRepresentation< GASDSignature7992 >
►Cpcl::PointRepresentation< GASDSignature984 >
►Cpcl::DefaultFeatureRepresentation< GASDSignature984 >
Cpcl::DefaultPointRepresentation< GASDSignature984 >
►Cpcl::PointRepresentation< Narf * >
Cpcl::Narf::FeaturePointRepresentation
►Cpcl::PointRepresentation< Narf36 >
Cpcl::DefaultPointRepresentation< Narf36 >
►Cpcl::PointRepresentation< NormalBasedSignature12 >
►Cpcl::DefaultFeatureRepresentation< NormalBasedSignature12 >
Cpcl::DefaultPointRepresentation< NormalBasedSignature12 >
►Cpcl::PointRepresentation< PFHRGBSignature250 >
►Cpcl::DefaultFeatureRepresentation< PFHRGBSignature250 >
Cpcl::DefaultPointRepresentation< PFHRGBSignature250 >
►Cpcl::PointRepresentation< PFHSignature125 >
►Cpcl::DefaultFeatureRepresentation< PFHSignature125 >
Cpcl::DefaultPointRepresentation< PFHSignature125 >
►Cpcl::PointRepresentation< PointDefault >
Cpcl::CustomPointRepresentation< PointDefault >	CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point
Cpcl::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)
Cpcl::DefaultPointRepresentation< PointDefault >	DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types
Cpcl::PointRepresentation< PointFeature >
►Cpcl::PointRepresentation< PointNormal >
Cpcl::DefaultPointRepresentation< PointNormal >
►Cpcl::PointRepresentation< PointTarget >
Cpcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >::MyPointRepresentation	Internal point representation uses only 3D coordinates for L2
►Cpcl::PointRepresentation< PointXYZ >
Cpcl::DefaultPointRepresentation< PointXYZ >
►Cpcl::PointRepresentation< PointXYZI >
Cpcl::DefaultPointRepresentation< PointXYZI >
►Cpcl::PointRepresentation< PointXYZLAB >
Cpcl::GeneralizedIterativeClosestPoint6D::MyPointRepresentation	Custom point representation to perform kdtree searches in more than 3 (i.e
►Cpcl::PointRepresentation< PPFSignature >
►Cpcl::DefaultFeatureRepresentation< PPFSignature >
Cpcl::DefaultPointRepresentation< PPFSignature >
►Cpcl::PointRepresentation< ShapeContext1980 >
Cpcl::DefaultPointRepresentation< ShapeContext1980 >
►Cpcl::PointRepresentation< SHOT1344 >
Cpcl::DefaultPointRepresentation< SHOT1344 >
►Cpcl::PointRepresentation< SHOT352 >
Cpcl::DefaultPointRepresentation< SHOT352 >
►Cpcl::PointRepresentation< UniqueShapeContext1960 >
Cpcl::DefaultPointRepresentation< UniqueShapeContext1960 >
►Cpcl::PointRepresentation< VFHSignature308 >
►Cpcl::DefaultFeatureRepresentation< VFHSignature308 >
Cpcl::DefaultPointRepresentation< VFHSignature308 >
Cpcl::device::PointStream
Cpcl::PointUV	A 2D point structure representing pixel image coordinates
Cpcl::PointXY	A 2D point structure representing Euclidean xy coordinates
Cpcl::PointXY32f	2D point with float x- and y-coordinates
Cpcl::PointXY32i	2D point with integer x- and y-coordinates
Cpcl::PointXYZIEdge	Point cloud containing edge information
Cpcl::cuda::PointXYZRGB	Default point xyz-rgb structure
Cpcl::PolygonMesh
Cpcl::geometry::PolygonMeshTag	Tag describing the type of the mesh
Cpcl::poisson::Polynomial< Degree >
Cpcl::PolynomialCalculationsT< real >	This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials
Cpcl::MLSResult::PolynomialPartialDerivative	Data structure used to store the MLS polynomial partial derivatives
►CPolynomialSolverBase
CEigen::PolynomialSolver< _Scalar, 2 >
Cpcl::PosesFromMatches::PoseEstimate	A result of the pose estimation process
Cpcl::registration::PoseEstimate< PointT >	PoseEstimate struct
Cpcl::registration::PoseMeasurement< VertexT, InformationT >	PoseMeasurement struct
Cpcl::PosesFromMatches	Calculate 3D transformation based on point correspondences
Cpcl::PPFRegistration< PointSource, PointTarget >::PoseWithVotes	Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes
Cpcl::PPFHashMapSearch
Cpcl::device::PPFRGBSignature
Cpcl::PPFRGBSignature	A point structure for storing the Point Pair Color Feature (PPFRGB) values
Cpcl::device::PPFSignature
Cpcl::PPFSignature	A point structure for storing the Point Pair Feature (PPF) values
Cpcl::poisson::PPolynomial< Degree >
Cpcl::poisson::PPolynomial< Degree+1 >
Cpcl::poisson::PPolynomial< Degree-1 >
Cpcl::device::PrincipalCurvatures
Cpcl::PrincipalCurvatures	A point structure representing the principal curvatures and their magnitudes
Cpcl::PrincipalRadiiRSD	A point structure representing the minimum and maximum surface radii (in meters) computed using RSD
Cpcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioBranchQueueEntry	Priority queue entry for branch nodes
Cpcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioPointQueueEntry	Priority queue entry for point candidates
Cpcl::device::prob_histogram
Cpcl::device::ProbabilityProc	Implementation Class to process probability histograms on GPU
Cpcl::gpu::people::ProbabilityProcessor
Cpcl::gpu::PseudoConvexHull3D
Cpcl::filters::Pyramid< PointT >	Pyramid constructs a multi-scale representation of an organised point cloud
►CQMainWindow
CPCLViewer
CPCLViewer
Cpcl::geometry::QuadMeshTag	Tag describing the type of the mesh
►Cpcl::QuantizableModality	Interface for a quantizable modality
Cpcl::ColorGradientModality< PointXYZT >
Cpcl::SurfaceNormalModality< PointXYZT >
Cpcl::ColorGradientModality< PointInT >	Modality based on max-RGB gradients
Cpcl::ColorModality< PointInT >
Cpcl::SurfaceNormalModality< PointInT >	Modality based on surface normals
Cpcl::QuantizedMap
Cpcl::QuantizedMultiModFeature	Feature that defines a position and quantized value in a specific modality
Cpcl::QuantizedNormalLookUpTable	Look-up-table for fast surface normal quantization
Cpcl::OrganizedNeighborSearch< PointT >::radiusSearchLoopkupEntry	radiusSearchLoopkupEntry entry for radius search lookup vector
Cpcl::segmentation::detail::RandomWalker< Graph, EdgeWeightMap, VertexColorMap >	Multilabel graph segmentation using random walks
Cpcl::gpu::kinfuLS::RayCaster	Class that performs raycasting for TSDF volume
Cpcl::gpu::RayCaster	Class that performs raycasting for TSDF volume
Cpcl::gpu::people::RDFBodyPartsDetector
►Cpcl::Region3D< PointT >	Region3D represents summary statistics of a 3D collection of points
Cpcl::PlanarRegion< PointT >	PlanarRegion represents a set of points that lie in a plane
Cpcl::RegionXY	Defines a region in XY-space
Cpcl::RegistrationVisualizer< PointSource, PointTarget, Scalar >	RegistrationVisualizer represents the base class for rendering the intermediate positions occupied by the source point cloud during it's registration to the target point cloud
Cpcl::RegressionVarianceNode< FeatureType, LabelType >	Node for a regression trees which optimizes variance
Cpcl::visualization::RenWinInteract
Cpcl::RFFaceDetectorTrainer
Cpcl::face_detection::RFTreeNode< FeatureType >
Cpcl::cuda::RGB	Default RGB structure, defined as a union over 4 chars
Cpcl::TexMaterial::RGB
Cpcl::tracking::RGBValue
Cpcl::recognition::RigidTransformSpace
Cpcl::poisson::RootInfo
Cpcl::recognition::RotationSpace	This is a class for a discrete representation of the rotation space based on the axis-angle representation
Cpcl::recognition::RotationSpaceCell
Cpcl::recognition::RotationSpaceCellCreator
Cpcl::recognition::RotationSpaceCreator
►Cstd::runtime_error
►Cpcl::PCLException	A base class for all pcl exceptions which inherits from std::runtime_error
Cpcl::BadArgumentException	An exception that is thrown when the arguments number or type is wrong/unhandled
Cpcl::ComputeFailedException
Cpcl::IOException	An exception that is thrown during an IO error (typical read/write errors)
Cpcl::InitFailedException	An exception thrown when init can not be performed should be used in all the PCLBase class inheritants
Cpcl::InvalidConversionException	An exception that is thrown when a PCLPointCloud2 message cannot be converted into a PCL type
Cpcl::InvalidSACModelTypeException	An exception that is thrown when a sample consensus model doesn't have the correct number of samples defined in model_types.h
Cpcl::IsNotDenseException	An exception that is thrown when a PointCloud is not dense but is attempted to be used as dense
Cpcl::KernelWidthTooSmallException	An exception that is thrown when the kernel size is too small
Cpcl::NotEnoughPointsException	An exception that is thrown when the number of correspondents is not equal to the minimum required
Cpcl::SolverDidntConvergeException	An exception that is thrown when the non linear solver didn't converge
Cpcl::UnhandledPointTypeException
Cpcl::UnorganizedPointCloudException	An exception that is thrown when an organized point cloud is needed but not provided
►Cpcl::poisson::PoissonException	A base class for all poisson exceptions which inherits from std::runtime_error
Cpcl::poisson::PoissonBadArgumentException	An exception that is thrown when the arguments number or type is wrong/unhandled
Cpcl::poisson::PoissonBadInitException	An exception that is thrown when initialization fails
Cpcl::poisson::PoissonOpenMPException	An exception that is thrown when something goes wrong inside an openMP for loop
►Cpcl::cuda::SampleConsensus< Storage >
Cpcl::cuda::MultiRandomSampleConsensus< Storage >	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
Cpcl::cuda::RandomSampleConsensus< Storage >	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
Cpcl::SampleConsensus< T >	SampleConsensus represents the base class
►CSampleConsensus
Cpcl_cuda::MEstimatorSampleConsensus< Storage >
►Cpcl::SampleConsensus< PointT >
Cpcl::LeastMedianSquares< PointT >	LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm
Cpcl::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
Cpcl::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
Cpcl::ProgressiveSampleConsensus< PointT >	ProgressiveSampleConsensus represents an implementation of the PROSAC (PROgressive SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O
Cpcl::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
Cpcl::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)
Cpcl::RandomizedRandomSampleConsensus< PointT >	RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RANdom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O
Cpcl::SampleConsensus< WeightSACPointType >
►Cpcl::cuda::SampleConsensusModel< Storage >	SampleConsensusModel represents the base model class
Cpcl::cuda::SampleConsensusModel1PointPlane< Storage >	SampleConsensusModel1PointPlane defines a model for 3D plane segmentation
Cpcl::cuda::SampleConsensusModelPlane< Storage >	SampleConsensusModelPlane defines a model for 3D plane segmentation
►Cpcl::SampleConsensusModel< PointT >	SampleConsensusModel represents the base model class
Cpcl::SampleConsensusModelCircle2D< PointT >	SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane
Cpcl::SampleConsensusModelCircle3D< PointT >	SampleConsensusModelCircle3D defines a model for 3D circle segmentation
Cpcl::SampleConsensusModelCone< PointT, PointNT >	SampleConsensusModelCone defines a model for 3D cone segmentation
Cpcl::SampleConsensusModelCylinder< PointT, PointNT >	SampleConsensusModelCylinder defines a model for 3D cylinder segmentation
Cpcl::SampleConsensusModelEllipse3D< PointT >	SampleConsensusModelEllipse3D defines a model for 3D ellipse segmentation
►Cpcl::SampleConsensusModelLine< PointT >	SampleConsensusModelLine defines a model for 3D line segmentation
Cpcl::SampleConsensusModelParallelLine< PointT >	SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints
►Cpcl::SampleConsensusModelPlane< PointT >	SampleConsensusModelPlane defines a model for 3D plane segmentation
►Cpcl::SampleConsensusModelNormalPlane< PointT, PointNT >	SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints
Cpcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT >	SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints
Cpcl::SampleConsensusModelParallelPlane< PointT >	SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints
Cpcl::SampleConsensusModelPerpendicularPlane< PointT >	SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints
►Cpcl::SampleConsensusModelRegistration< PointT >	SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection
Cpcl::SampleConsensusModelRegistration2D< PointT >	SampleConsensusModelRegistration2D defines a model for Point-To-Point registration outlier rejection using distances between 2D pixels
►Cpcl::SampleConsensusModelSphere< PointT >	SampleConsensusModelSphere defines a model for 3D sphere segmentation
Cpcl::SampleConsensusModelNormalSphere< PointT, PointNT >	SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints
Cpcl::SampleConsensusModelStick< PointT >	SampleConsensusModelStick defines a model for 3D stick segmentation
Cpcl::SampleConsensusModelTorus< PointT, PointNT >	SampleConsensusModelTorus defines a model for 3D torus segmentation
►Cpcl::SampleConsensusModel< pcl::PointXYZRGB >
Cpcl::SampleConsensusModelCircle2D< pcl::PointXYZRGB >
Cpcl::SampleConsensusModelCircle3D< pcl::PointXYZRGB >
Cpcl::SampleConsensusModelEllipse3D< pcl::PointXYZRGB >
Cpcl::SampleConsensusModelTorus< pcl::PointXYZRGB, PointNT >
Cpcl::SampleConsensusModel< PointXYZ >
Cpcl::SampleConsensusModel< T >
►Cpcl::SampleConsensusModelFromNormals< PointT, PointNT >	SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation
Cpcl::SampleConsensusModelCone< PointT, PointNT >	SampleConsensusModelCone defines a model for 3D cone segmentation
Cpcl::SampleConsensusModelCylinder< PointT, PointNT >	SampleConsensusModelCylinder defines a model for 3D cylinder segmentation
Cpcl::SampleConsensusModelNormalPlane< PointT, PointNT >	SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints
Cpcl::SampleConsensusModelNormalSphere< PointT, PointNT >	SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints
Cpcl::SampleConsensusModelTorus< PointT, PointNT >	SampleConsensusModelTorus defines a model for 3D torus segmentation
►Cpcl::SampleConsensusModelFromNormals< pcl::PointXYZRGB, PointNT >
Cpcl::SampleConsensusModelTorus< pcl::PointXYZRGB, PointNT >
Cpcl::io::ply::ply_parser::scalar_property_callback_type< ScalarType >
Cpcl::io::ply::ply_parser::scalar_property_callback_type< scalar_type >
Cpcl::io::ply::ply_parser::scalar_property_definition_callback_type< ScalarType >
Cpcl::io::ply::ply_parser::scalar_property_definition_callback_type< scalar_type >
Cpcl::io::ply::ply_parser::scalar_property_definition_callbacks_type
Cpcl::keypoints::brisk::ScaleSpace	BRISK Scale Space helper
CScene
Cpcl::cuda::ScopeTimeCPU	Class to measure the time spent in a scope
Cpcl::cuda::ScopeTimeGPU	Class to measure the time spent in a scope
Cpcl::gpu::ScopeTimer
Cpcl::keypoints::agast::AbstractAgastDetector::ScoreIndex	Structure holding an index and the associated keypoint score
Cpcl::kinfuLS::ScreenshotManager	Screenshot Manager saves a screenshot with the corresponding camera pose from Kinfu
►Cpcl::search::Search< PointT >	Generic search class
Cpcl::search::KdTree< pcl::PointXYZRGB >
Cpcl::search::KdTree< SceneT >
Cpcl::search::BruteForce< PointT >	Implementation of a simple brute force search algorithm
Cpcl::search::FlannSearch< PointT, FlannDistance >	search::FlannSearch is a generic FLANN wrapper class for the new search interface
Cpcl::search::KdTree< PointT, Tree >	search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure
Cpcl::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
Cpcl::search::OrganizedNeighbor< PointT >	OrganizedNeighbor is a class for optimized nearest neighbor search in organized projectable point clouds, for instance from Time-Of-Flight cameras or stereo cameras
Cpcl::search::Search< pcl::PointXYZ >
►Cpcl::search::Search< PointInT >
Cpcl::search::Octree< PointInT >
Cpcl::search::Search< PointXYZRGB >
Cpcl::gpu::SeededHueSegmentation
Cpcl::cuda::detail::SegmLink
Cpcl::cuda::detail::SegmLinkVal
Cpcl::recognition::ORROctreeZProjection::Set
Cpcl::cuda::SetColor
Cpcl::SetIfFieldExists< PointOutT, InT >	A helper functor that can set a specific value in a field if the field exists
Cpcl::RangeImageBorderExtractor::ShadowBorderIndices	Stores the indices of the shadow border corresponding to obstacle borders
Cpcl::ShapeContext1980	A point structure representing a Shape Context
Copenni_wrapper::OpenNIDevice::ShiftConversion
Copenni_wrapper::ShiftToDepthConverter	This class provides conversion of the openni 11-bit shift data to depth;
Cpcl::SHOT1344	A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color
Cpcl::SHOT352	A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only
Cpcl::SIFTKeypointFieldSelector< PointT >
Cpcl::SIFTKeypointFieldSelector< PointInT >
Cpcl::SIFTKeypointFieldSelector< PointNormal >
Cpcl::SIFTKeypointFieldSelector< PointXYZRGB >
Cpcl::SIFTKeypointFieldSelector< PointXYZRGBA >
Cpcl::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar >
Cpcl::recognition::SimpleOctree< RotationSpace, RotationSpaceCreator, float >
Cpcl::recognition::SimpleOctree< RotationSpaceCell, RotationSpaceCellCreator, float >
Cpcl::surface::SimplificationRemoveUnusedVertices
CON_SerialNumberMap::SN_ELEMENT
Cpcl::registration::sortCorrespondencesByDistance	sortCorrespondencesByDistance : a functor for sorting correspondences by distance
Cpcl::registration::sortCorrespondencesByMatchIndex	sortCorrespondencesByMatchIndex : a functor for sorting correspondences by match index
Cpcl::registration::sortCorrespondencesByMatchIndexAndDistance	sortCorrespondencesByMatchIndexAndDistance : a functor for sorting correspondences by match index and distance
Cpcl::registration::sortCorrespondencesByQueryIndex	sortCorrespondencesByQueryIndex : a functor for sorting correspondences by query index
Cpcl::registration::sortCorrespondencesByQueryIndexAndDistance	sortCorrespondencesByQueryIndexAndDistance : a functor for sorting correspondences by query index and distance
Cpcl::poisson::SortedTreeNodes
►Cpcl::poisson::SparseMatrix< T >
Cpcl::poisson::SparseSymmetricMatrix< T >
Cpcl::SparseQuantizedMultiModTemplate	A multi-modality template constructed from a set of quantized multi-modality features
Cpcl::gpu::people::trees::SplitPoint
Cpcl::poisson::Square
Cpcl::gpu::kinfuLS::StandaloneMarchingCubes< PointT >	The Standalone Marching Cubes Class provides encapsulated functionality for the Marching Cubes implementation originally by Anatoly Baksheev
Cpcl::poisson::StartingPolynomial< Degree >
Cpcl::StaticRangeCoder	StaticRangeCoder compression class
►Cpcl::StatsEstimator< LabelDataType, NodeType, DataSet, ExampleIndex >	Class interface for gathering statistics for decision tree learning
Cpcl::RegressionVarianceStatsEstimator< LabelDataType, NodeType, DataSet, ExampleIndex >	Statistics estimator for regression trees which optimizes variance
Cpcl::face_detection::PoseClassRegressionVarianceStatsEstimator< LabelDataType, NodeType, DataSet, ExampleIndex >	Statistics estimator for regression trees which optimizes information gain and pose parameters error
Cpcl::StatsEstimator< LabelType, NodeType, DataSet, ExampleIndex >
►Cpcl::StereoMatching	Stereo Matching abstract class
►Cpcl::GrayStereoMatching	Stereo Matching abstract class for Grayscale images
Cpcl::AdaptiveCostSOStereoMatching	Adaptive Cost 2-pass Scanline Optimization Stereo Matching class
Cpcl::BlockBasedStereoMatching	Block based (or fixed window) Stereo Matching class
►Cpcl::StopWatch	Simple stopwatch
Cpcl::ScopeTime	Class to measure the time spent in a scope
Cpcl::cuda::StorageAllocator< Storage, T >
Cpcl::cuda::StorageAllocator< Device, T >
Cpcl::cuda::StorageAllocator< Host, T >
Cpcl::cuda::StoragePointer< Storage, T >
Cpcl::cuda::StoragePointer< Device, T >
Cpcl::cuda::StoragePointer< Host, T >
Cpcl::Supervoxel< PointT >	Supervoxel container class - stores a cluster extracted using supervoxel clustering
►Cpcl::SVM	Base class for SVM SVM (Support Vector Machines)
Cpcl::SVMClassify	SVM (Support Vector Machines) classification of a dataset
Cpcl::SVMTrain	SVM (Support Vector Machines) training class for the SVM machine learning
►Csvm_model
Cpcl::SVMModel	The structure initialize a model created by the SVM (Support Vector Machines) classifier (pcl::SVMTrain)
Csvm_node
►Csvm_parameter
Cpcl::SVMParam	The structure stores the parameters for the classificationa nd must be initialized and passed to the training method pcl::SVMTrain
Csvm_problem
Csvm_scaling
Cpcl::SVMData	The structure stores the features and the label of a single sample which has to be used for the training or the classification of the SVM (Support Vector Machines)
Cpcl::SVMDataPoint	The structure initialize a single feature value for the classification using SVM (Support Vector Machines)
Cpcl::SynchronizedQueue< T >
Cpcl::SynchronizedQueue< boost::shared_array< unsigned char > >
Cpcl::SynchronizedQueue< std::uint8_t * >
Cpcl::Synchronizer< T1, T2 >	/brief This template class synchronizes two data streams of different types
Cpcl::Synchronizer< openni_wrapper::Image::Ptr, openni_wrapper::DepthImage::Ptr >
Cpcl::Synchronizer< openni_wrapper::IRImage::Ptr, openni_wrapper::DepthImage::Ptr >
Cpcl::deprecated::T
CTAccPixDist< Tfrom >
CTAccPixDist< float1 >
CTAccPixDist< float3 >
CTAccPixDist< float4 >
CTAccPixDist< uchar1 >
CTAccPixDist< uchar3 >
CTAccPixDist< uchar4 >
CTAccPixDist< ushort1 >
CTAccPixDist< ushort3 >
CTAccPixDist< ushort4 >
CTAccPixWeighted< T >
CTAccPixWeighted< float1 >
CTAccPixWeighted< float3 >
CTAccPixWeighted< float4 >
CTAccPixWeighted< uchar1 >
CTAccPixWeighted< uchar3 >
CTAccPixWeighted< uchar4 >
CTAccPixWeighted< ushort1 >
CTAccPixWeighted< ushort3 >
CTAccPixWeighted< ushort4 >
CtagON_2dex
CtagON_3dex
CtagON_4dex
CtagON_RECT
Cpcl::io::TARHeader	A TAR file's header, as described on https://en.wikipedia.org/wiki/Tar_%28file_format%29
CTConvBase2Vec< TBase, NC >
CTConvBase2Vec< Ncv16u, 1 >
CTConvBase2Vec< Ncv16u, 3 >
CTConvBase2Vec< Ncv16u, 4 >
CTConvBase2Vec< Ncv32f, 1 >
CTConvBase2Vec< Ncv32f, 3 >
CTConvBase2Vec< Ncv32f, 4 >
CTConvBase2Vec< Ncv32u, 1 >
CTConvBase2Vec< Ncv32u, 3 >
CTConvBase2Vec< Ncv32u, 4 >
CTConvBase2Vec< Ncv64f, 1 >
CTConvBase2Vec< Ncv64f, 3 >
CTConvBase2Vec< Ncv64f, 4 >
CTConvBase2Vec< Ncv8u, 1 >
CTConvBase2Vec< Ncv8u, 3 >
CTConvBase2Vec< Ncv8u, 4 >
CTConvVec2Base< Tvec >
CTConvVec2Base< double1 >
CTConvVec2Base< double3 >
CTConvVec2Base< double4 >
CTConvVec2Base< float1 >
CTConvVec2Base< float3 >
CTConvVec2Base< float4 >
CTConvVec2Base< uchar1 >
CTConvVec2Base< uchar3 >
CTConvVec2Base< uchar4 >
CTConvVec2Base< uint1 >
CTConvVec2Base< uint3 >
CTConvVec2Base< uint4 >
CTConvVec2Base< ushort1 >
CTConvVec2Base< ushort3 >
CTConvVec2Base< ushort4 >
Cpcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TermCriteria	This structure is used for determining the end of the k-means clustering process
Cpcl::TexMaterial
Cpcl::gpu::TextureBinder
Cpcl::TextureMapping< PointInT >	The texture mapping algorithm
Cpcl::TextureMesh
Cpcl::console::TicToc
Cpcl::gpu::Timer
Cpcl::TimeTrigger	Timer class that invokes registered callback methods periodically
►Cboost::totally_ordered
Cpcl::detail::MeshIndex< IndexTagT >
Cpcl::face_detection::TrainingExample
►Cpcl::registration::TransformationEstimation< PointSource, PointTarget, Scalar >	TransformationEstimation represents the base class for methods for transformation estimation based on:
►Cpcl::registration::TransformationEstimationSVD< PointT, PointT, Scalar >
Cpcl::recognition::TrimmedICP< PointT, Scalar >
►Cpcl::registration::TransformationEstimation< PointSource, PointTarget, float >
►Cpcl::registration::TransformationEstimationLM< PointSource, PointTarget, float >
►Cpcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget, float >
Cpcl::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
Cpcl::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
►Cpcl::registration::TransformationEstimationSVD< PointSource, PointTarget, float >
Cpcl::registration::TransformationEstimationSVDScale< PointSource, PointTarget, Scalar >	TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences
Cpcl::registration::TransformationEstimation2D< PointSource, PointTarget, Scalar >	TransformationEstimation2D implements a simple 2D rigid transformation estimation (x, y, theta) for a given pair of datasets
Cpcl::registration::TransformationEstimation3Point< PointSource, PointTarget, Scalar >	TransformationEstimation3Points represents the class for transformation estimation based on:
Cpcl::registration::TransformationEstimationDQ< PointSource, PointTarget, Scalar >	TransformationEstimationDQ implements dual quaternion based estimation of the transformation aligning the given correspondences
Cpcl::registration::TransformationEstimationDualQuaternion< PointSource, PointTarget, Scalar >	TransformationEstimationDualQuaternion implements dual quaternion based estimation of the transformation aligning the given correspondences
Cpcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >	TransformationEstimationLM implements Levenberg Marquardt-based estimation of the transformation aligning the given correspondences
Cpcl::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
Cpcl::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
►Cpcl::registration::TransformationEstimationSVD< PointSource, PointTarget, Scalar >	TransformationEstimationSVD implements SVD-based estimation of the transformation aligning the given correspondences
Cpcl::recognition::TrimmedICP< pcl::PointXYZ, float >
Cpcl::registration::TransformationEstimationSymmetricPointToPlaneLLS< PointSource, PointTarget, Scalar >	TransformationEstimationSymmetricPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the symmetric point-to-plane distance between two clouds of corresponding points with normals
►Cpcl::registration::TransformationEstimation< PointSource, PointTarget, MatScalar >
►Cpcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >
►Cpcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget, MatScalar >
Cpcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >
Cpcl::TransformationFromCorrespondences	Calculates a transformation based on corresponding 3D points
Cpcl::registration::TransformationValidation< PointSource, PointTarget, Scalar >	TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation
Cpcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >	TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset
Cpcl::detail::Transformer< Scalar >	A helper struct to apply an SO3 or SE3 transform to a 3D point
Cpcl::gpu::people::Tree2	This structure contains all parameters to describe the segmented tree
Ctree_desc_s
Cpcl::poisson::TreeNodeData
Cpcl::poisson::Triangle
Cpcl::poisson::TriangleIndex
Cpcl::geometry::TriangleMeshTag	Tag describing the type of the mesh
Cpcl::poisson::Triangulation< Real >
Cpcl::poisson::TriangulationEdge
Cpcl::poisson::TriangulationTriangle
►Cstd::true_type
Cpcl::detail::compat_with_flann< std::size_t >
Cpcl::gpu::kinfuLS::tsdf_buffer	Structure to handle buffer addresses
Cpcl::gpu::kinfuLS::TsdfVolume	TsdfVolume class
Cpcl::gpu::TsdfVolume	TsdfVolume class
Cpcl::TSDFVolume< VoxelT, WeightT >
Cpcl::io::ply::type_traits< ScalarType >
CLoki::TL::TypeAt< TList, index >
CLoki::TL::TypeAt< Typelist< Head, Tail >, 0 >
CLoki::TL::TypeAt< Typelist< Head, Tail >, i >
CLoki::Typelist< T, U >
Cpcl::UnaryClassifier< PointT >
Cpcl::common::uniform_distribution< T, T2 >	Uniform distribution dummy struct
Cpcl::common::uniform_distribution< T, std::enable_if_t< std::is_floating_point< T >::value > >	Uniform distribution float specialized
Cpcl::common::uniform_distribution< T, std::enable_if_t< std::is_integral< T >::value > >	Uniform distribution int specialized
Cpcl::common::UniformGenerator< T >	UniformGenerator class generates a random number from range [min, max] at each run picked according to a uniform distribution i.e each number within [min, max] has almost the same probability of being drawn
Cpcl::UniqueShapeContext1960	A point structure representing a Unique Shape Context
Cpcl::poisson::UpSampleData
Cpcl::texture_mapping::UvIndex	Structure that links a uv coordinate to its 3D point and face
Cpcl::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
Cpcl::poisson::Vector< T >
►Cstd::vector
Cpcl::DecisionForest< pcl::face_detection::RFTreeNode >
Cpcl::DecisionForest< NodeType >	Class representing a decision forest
Cpcl::poisson::BSplineElements< Degree >
Cpcl::VectorAverage< real, dimension >	Calculates the weighted average and the covariance matrix
Cpcl::geometry::Vertex	A vertex is a node in the mesh
Cpcl::poisson::CoredMeshData2::Vertex
Cpcl::registration::ELCH< PointT >::Vertex
Cpcl::poisson::VertexData
Cpcl::registration::LUM< PointT >::VertexProperties
Cpcl::Vertices	Describes a set of vertices in a polygon mesh, by basically storing an array of indices
Cpcl::device::VFHEstimationImpl
Cpcl::VFHSignature308	A point structure representing the Viewpoint Feature Histogram (VFH)
CViewport
Cpcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::VisualWordStat	Structure for storing the visual word
Cpcl::SupervoxelClustering< PointT >::VoxelData	VoxelData is a structure used for storing data within a pcl::octree::OctreePointCloudAdjacencyContainer
Cpcl::recognition::VoxelStructure< T, REAL >	This class is a box in R3 built of voxels ordered in a regular rectangular grid
Cpcl::recognition::VoxelStructure< HashTableCell, float >
►CvtkCommand
Cpcl::visualization::ImageViewer::ExitCallback
Cpcl::visualization::ImageViewer::ExitMainLoopTimerCallback
Cpcl::visualization::PointPickingCallback
Cpcl::visualization::Window::ExitCallback
Cpcl::visualization::Window::ExitMainLoopTimerCallback
►CvtkContextItem
Cpcl::visualization::PCLContextImageItem	Struct PCLContextImageItem a specification of vtkContextItem, used to add an image to the scene in the ImageViewer class
►Cpcl::visualization::PCLContextItem	Struct PCLContextItem represents our own custom version of vtkContextItem, used by the ImageViewer class
►Cpcl::visualization::context_items::Circle
Cpcl::visualization::context_items::Disk
Cpcl::visualization::context_items::Line
Cpcl::visualization::context_items::Point
►Cpcl::visualization::context_items::Points
Cpcl::visualization::context_items::Markers
Cpcl::visualization::context_items::Polygon
►Cpcl::visualization::context_items::Rectangle
Cpcl::visualization::context_items::FilledRectangle
Cpcl::visualization::context_items::Text
Cpcl::visualization::PCLPainter2D	PCL Painter2D main class
►CvtkImageCanvasSource2D
Cpcl::visualization::PCLImageCanvasSource2D	PCLImageCanvasSource2D represents our own custom version of vtkImageCanvasSource2D, used by the ImageViewer class
►CvtkInteractorStyleImage
Cpcl::visualization::ImageViewerInteractorStyle	An image viewer interactor style, tailored for ImageViewer
►CvtkInteractorStyleRubberBandPick
Cpcl::visualization::PCLVisualizerInteractorStyle	PCLVisualizerInteractorStyle defines an unique, custom VTK based interactory style for PCL Visualizer applications
►CvtkInteractorStyleTrackballCamera
Cpcl::visualization::PCLHistogramVisualizerInteractorStyle	PCL histogram visualizer interactory style class
►CvtkRenderWindowInteractor
Cpcl::vtkXRenderWindowInteractor
CvtkSmartPointer< T >
CvtkSmartPointer< ExitCallback >
CvtkSmartPointer< ExitMainLoopTimerCallback >
CvtkSmartPointer< FPSCallback >
CvtkSmartPointer< pcl::visualization::ImageViewer::ExitCallback >
CvtkSmartPointer< pcl::visualization::ImageViewer::ExitMainLoopTimerCallback >
CvtkSmartPointer< pcl::visualization::ImageViewerInteractorStyle >
CvtkSmartPointer< pcl::visualization::PCLVisualizerInteractorStyle >
CvtkSmartPointer< pcl::visualization::PointPickingCallback >
CvtkSmartPointer< pcl::visualization::Window::ExitCallback >
CvtkSmartPointer< pcl::visualization::Window::ExitMainLoopTimerCallback >
CvtkSmartPointer< vtkActor >
CvtkSmartPointer< vtkActorCollection >
CvtkSmartPointer< vtkAxes >
CvtkSmartPointer< vtkCallbackCommand >
CvtkSmartPointer< vtkCamera >
CvtkSmartPointer< vtkCameraActor >
CvtkSmartPointer< vtkChartXY >
CvtkSmartPointer< vtkColorSeries >
CvtkSmartPointer< vtkContextActor >
CvtkSmartPointer< vtkContextView >
CvtkSmartPointer< vtkIdTypeArray >
CvtkSmartPointer< vtkImageData >
CvtkSmartPointer< vtkImageFlip >
CvtkSmartPointer< vtkImageSlice >
CvtkSmartPointer< vtkImageViewer >
CvtkSmartPointer< vtkInteractorStyleTrackballCamera >
CvtkSmartPointer< vtkLegendScaleActor >
CvtkSmartPointer< vtkLODActor >
CvtkSmartPointer< vtkMatrix4x4 >
CvtkSmartPointer< vtkOrientationMarkerWidget >
CvtkSmartPointer< vtkPNGWriter >
CvtkSmartPointer< vtkPointPicker >
CvtkSmartPointer< vtkPolyData >
CvtkSmartPointer< vtkRectilinearGrid >
CvtkSmartPointer< vtkRenderer >
CvtkSmartPointer< vtkRendererCollection >
CvtkSmartPointer< vtkRenderWindow >
CvtkSmartPointer< vtkRenderWindowInteractor >
CvtkSmartPointer< vtkScalarBarActor >
CvtkSmartPointer< vtkTextActor >
CvtkSmartPointer< vtkWindowToImageFilter >
CvtkSmartPointer< vtkXYPlotActor >
Cpcl::VTKUtils
CvtkXRenderWindowInteractor	X event driven interface for a RenderWindow
Cpcl::device::kinfuLS::Warp
Cpcl::device::Warp
Cpcl::registration::WarpPointRigid< PointSourceT, PointTargetT, Scalar >	Base warp point class
Cpcl::registration::WarpPointRigid< PointSource, PointTarget, float >
Cpcl::registration::WarpPointRigid< PointSource, PointTarget, MatScalar >
►Cpcl::registration::WarpPointRigid< PointSourceT, PointTargetT, float >
Cpcl::registration::WarpPointRigid3D< PointSourceT, PointTargetT, Scalar >	WarpPointRigid3D enables 3D (1D rotation + 2D translation) transformations for points
Cpcl::registration::WarpPointRigid6D< PointSourceT, PointTargetT, Scalar >	WarpPointRigid3D enables 6D (3D rotation + 3D translation) transformations for points
Cpcl::visualization::Window
Cpcl::kinfuLS::WorldModel< PointT >	WorldModel maintains a 3D point cloud that can be queried and updated via helper functions
Cpcl::kinfuLS::WorldModel< pcl::PointXYZI >
Cpcl::xNdCopyEigenPointFunctor< PointT >	Helper functor structure for copying data between an Eigen::VectorXf and a PointT
Cpcl::xNdCopyPointEigenFunctor< PointT >	Helper functor structure for copying data between an Eigen::VectorXf and a PointT
Cpcl::cuda::YUV2RGB< Storage >
Cpcl::cuda::YUV2RGBKernel< Storage >
Cz_stream_s
Cpcl::occlusion_reasoning::ZBuffering< ModelT, SceneT >	Class to reason about occlusions
►Cplus
Cpcl::device::plusWeighted< T, W >
►Cunary_function
Cpcl::device::bit_not< T >