Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
pcl::_Axis
pcl::_Intensity
pcl::_Intensity32u
pcl::_Intensity8u
pcl::_Normal
pcl::tracking::_ParticleXYR
pcl::tracking::_ParticleXYRP
pcl::tracking::_ParticleXYRPY
pcl::tracking::_ParticleXYZR
pcl::tracking::_ParticleXYZRPY
pcl::ihs::_PointIHS
pcl::_PointNormal
pcl::_PointSurfel
pcl::_PointWithRange
pcl::_PointWithScale
pcl::_PointWithViewpoint
pcl::_PointXYZ
pcl::_PointXYZHSV
pcl::_PointXYZIA point structure representing Euclidean xyz coordinates, and the intensity value
pcl::_PointXYZINormal
pcl::_PointXYZL
pcl::_PointXYZRGB
pcl::_PointXYZRGBA
pcl::_PointXYZRGBL
pcl::_PointXYZRGBNormal
pcl::_ReferenceFrameA structure representing the Local Reference Frame of a point
pcl::_RGB
mets::abstract_cooling_scheduleCooling criteria (for Simulated Annealing)
mets::abstract_search< move_manager_type >An abstract search
pcl::keypoints::agast::AbstractAgastDetectorAbstract detector class for AGAST corner point detectors
pcl::modeler::AbstractItem
AbstractMetadataAbstract interface for outofcore metadata file types
pcl::cloud_composer::AbstractTool
pcl::modeler::AbstractWorker
pcl::cloud_composer::ActionPair
pcl::AdaptiveRangeCoderAdaptiveRangeCoder compression class
std::tr1::gtest_internal::AddRef< T >
std::tr1::gtest_internal::AddRef< T & >
testing::internal::AddReference< T >
testing::internal::AddReference< T & >
pcl::poisson::Octree< Degree >::AdjacencyCountFunction
pcl::poisson::OctNode< NodeData, Real >::AdjacencyCountFunction
pcl::poisson::Octree< Degree >::AdjacencySetFunction
pcl::keypoints::internal::AgastApplyNonMaxSuppresion< Out >
pcl::keypoints::internal::AgastApplyNonMaxSuppresion< pcl::PointUV >
AGASTDemo< PointT >
pcl::keypoints::internal::AgastDetector< Out >
pcl::keypoints::agast::AgastDetector5_8Detector class for AGAST corner point detector (5_8)
pcl::keypoints::agast::AgastDetector7_12sDetector class for AGAST corner point detector (7_12s)
pcl::keypoints::internal::AgastDetector< pcl::PointUV >
pcl::AgastKeypoint2D< PointInT, PointOutT >Detects 2D AGAST corner points. Based on the original work and paper reference by
pcl::AgastKeypoint2D< pcl::PointXYZ, pcl::PointUV >Detects 2D AGAST corner points. Based on the original work and paper reference by
pcl::AgastKeypoint2DBase< PointInT, PointOutT, IntensityT >Detects 2D AGAST corner points. Based on the original work and paper reference by
pcl::poisson::Allocator< T >
pcl::poisson::AllocatorState
pcl::ApproximateVoxelGrid< PointT >ApproximateVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::tracking::ApproxNearestPairPointCloudCoherence< PointInT >ApproxNearestPairPointCloudCoherence computes coherence between two pointclouds using the approximate nearest point pairs
pcl::visualization::AreaPickingEvent
pcl::FastBilateralFilter< PointT >::Array3D
pcl::ASCIIReaderAscii Point Cloud Reader. Read any ASCII file by setting the separating characters and input point fields
pcl::traits::asEnum< T >
pcl::traits::asEnum< double >
pcl::traits::asEnum< float >
pcl::traits::asEnum< int16_t >
pcl::traits::asEnum< int32_t >
pcl::traits::asEnum< int8_t >
pcl::traits::asEnum< uint16_t >
pcl::traits::asEnum< uint32_t >
pcl::traits::asEnum< uint8_t >
mets::aspiration_criteria_chainFunction object expressing an aspiration criteria
testing::internal::AssertHelper
testing::internal::AssertHelper::AssertHelperData
testing::AssertionResult
pcl::traits::asType< int >
pcl::traits::asType< pcl::PCLPointField::FLOAT32 >
pcl::traits::asType< pcl::PCLPointField::FLOAT64 >
pcl::traits::asType< pcl::PCLPointField::INT16 >
pcl::traits::asType< pcl::PCLPointField::INT32 >
pcl::traits::asType< pcl::PCLPointField::INT8 >
pcl::traits::asType< pcl::PCLPointField::UINT16 >
pcl::traits::asType< pcl::PCLPointField::UINT32 >
pcl::traits::asType< pcl::PCLPointField::UINT8 >
Axes
pcl::AxisA point structure representing an Axis using its normal coordinates. (SSE friendly)
pcl::cloud_composer::BackgroundDelegate
mets::best_ever_criteriaAspiration criteria implementation
mets::best_ever_solutionThe best ever solution recorder can be used as a simple solution recorder that just records the best copyable solution found during its lifetime
BFGS< FunctorType >
BFGSDummyFunctor< _Scalar, NX >
pcl::BilateralFilter< PointT >A bilateral filter implementation for point cloud data. Uses the intensity data channel
pcl::BilateralUpsampling< PointInT, PointOutT >Bilateral filtering implementation, based on the following paper: * Kopf, Johannes and Cohen, Michael F. and Lischinski, Dani and Uyttendaele, Matt - Joint Bilateral Upsampling, * ACM Transations in Graphics, July 2007
pcl::poisson::BinaryNode< Real >
pcl::BivariatePolynomialT< real >This represents a bivariate polynomial and provides some functionality for it
ON_RTreeMemPool::Blk
pcl::BOARDLocalReferenceFrameEstimation< PointInT, PointNT, PointOutT >BOARDLocalReferenceFrameEstimation implements the BOrder Aware Repeatable Directions algorithm for local reference frame estimation as described here:
testing::internal::bool_constant< bool_value >
pcl::modeler::BoolParameter
pcl::BorderDescriptionA structure to store if a point in a range image lies on a border between an obstacle and the background
pcl::BoundaryA point structure representing a description of whether a point is lying on a surface boundary or not
pcl::BoundaryEstimation< PointInT, PointNT, PointOutT >BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The code makes use of the estimated surface normals at each point in the input dataset
pcl::recognition::BVH< UserData >::BoundedObject
pcl::BoundingBoxXYZ
pcl::BoxClipper3D< PointT >Implementation of a box clipper in 3D. Actually it allows affine transformations, thus any parallelepiped in general pose. The affine transformation is used to transform the point before clipping it using the unit cube centered at origin and with an extend of -1 to +1 in each dimension
pcl::ihs::OpenGLViewer::BoxCoefficientsCoefficients for the wireframe box
pcl::segmentation::grabcut::BoykovKolmogorov
pcl::search::BruteForce< PointT >Implementation of a simple brute force search algorithm
pcl::poisson::BSplineData< Degree, Real >::BSplineComponents
pcl::poisson::BSplineData< Degree, Real >
pcl::poisson::BSplineElementCoefficients< Degree >
pcl::poisson::BSplineElements< Degree >
Buffer
BufferAndSize
pcl::octree::BufferedBranchNode< ContainerT >
pcl::recognition::BVH< UserData >This class is an implementation of bounding volume hierarchies. Use the build method to construct the data structure. To use the class, construct an std::vector of pointers to BVH::BoundedObject objects and pass it to the build method. BVH::BoundedObject is a template class, so you can save user-defined data in it
std::tr1::gtest_internal::ByRef< T >
std::tr1::gtest_internal::ByRef< T & >
callback_args
CallbackParameters
pcl::io::ply::ply_parser::scalar_property_definition_callbacks_type::callbacks_element< T >
pcl::io::ply::ply_parser::list_property_definition_callbacks_type::callbacks_element< T >
Camera
pcl::texture_mapping::CameraStructure to store camera pose and focal length
pcl::visualization::CameraCamera class holds a set of camera parameters together with the window pos/size
pcl::io::CameraParametersBasic camera parameters placeholder
pcl::apps::RenderViewsTesselatedSphere::camPosConstraintsAllTrue
pcl::ColorGradientDOTModality< PointInT >::Candidate
pcl::ColorModality< PointInT >::Candidate
pcl::ColorGradientModality< PointInT >::CandidateCandidate for a feature (used in feature extraction methods)
pcl::SurfaceNormalModality< PointInT >::CandidateCandidate for a feature (used in feature extraction methods)
pcl::modeler::ChannelActorItem
pcl::visualization::context_items::Circle
cJSON
cJSON_Hooks
mesh_circulators.Class
pcl::cloud_composer::ClickTrackballStyleInteractor
pcl::Clipper3D< PointT >Base class for 3D clipper objects
mets::clonableAn interface for prototype objects
pcl::on_nurbs::ClosingBoundaryFunctions for finding the common boundary of adjacent NURBS patches
CloudA wrapper which allows to use any implementation of cloud provided by a third-party library
cloud_point_index_idx
pcl::cloud_show< CloudT >
pcl::cloud_show_base
pcl::visualization::CloudActor
pcl::cloud_composer::CloudBrowser
pcl::cloud_composer::CloudCommand
pcl::cloud_composer::CloudComposerItem
OutofcoreCloud::CloudDataCacheItem
CloudEditorWidgetClass declaration for the widget for editing and viewing point clouds
pcl::apps::optronic_viewer::CloudFilterInterface for a class that implements a filter for a point cloud
pcl::apps::optronic_viewer::CloudFilterFactoryFactory class to create a filter
pcl::apps::optronic_viewer::CloudFilterFactory2< T, name >Helper class for the factory to simplify implementation of new cloud filters. This class makes the implementation of a separate factory class obsolete, e.g.:
pcl::common::CloudGenerator< PointT, GeneratorT >
pcl::common::CloudGenerator< pcl::PointXY, GeneratorT >
pcl::cloud_composer::CloudItem
pcl::CloudIterator< PointT >Iterator class for point clouds with or without given indices
pcl::modeler::CloudMesh
pcl::modeler::CloudMeshItem
pcl::modeler::CloudMeshItemUpdater
pcl::CloudSurfaceProcessing< PointInT, PointOutT >CloudSurfaceProcessing represents the base class for algorithms that takes a point cloud as input and produces a new output cloud that has been modified towards a better surface representation. These types of algorithms include surface smoothing, hole filling, cloud upsampling etc
CloudTransformToolThe cloud transform tool computes the transform matrix from user's mouse operation. It then updates the cloud with the new transform matrices to make the cloud be rendered appropriately
pcl::cloud_composer::CloudViewView class for displaying ProjectModel data using PCLVisualizer
pcl::cloud_composer::CloudViewerTabbed widget for containing CloudView widgets
pcl::visualization::CloudViewerSimple point cloud visualization class
pcl::visualization::CloudViewer::CloudViewer_impl
CMyBrepIsSolidSetter
code
pcl::segmentation::grabcut::ColorStructure to save RGB colors into floats
pcl::octree::ColorCoding< PointT >ColorCoding class
pcl::ColorGradientDOTModality< PointInT >
pcl::ColorGradientModality< PointInT >Modality based on max-RGB gradients
pcl::ColorModality< PointInT >
pcl::modeler::ColorParameter
CommandThe abstract parent class of all the command classes. Commands are non-copyable
CommandQueueA structure for managing commands
pcl::Comparator< PointT >Comparator is the base class for comparators that compare two points given some function. Currently intended for use with OrganizedConnectedComponentSegmentation
pcl::search::Search< PointT >::Compare
pcl::SamplingSurfaceNormal< PointT >::CompareDimCompareDim is a comparator object for sorting across a specific dimenstion (i,.e X, Y or Z)
pcl::keypoints::agast::AbstractAgastDetector::CompareScoreIndexScore index comparator
pcl::ComparisonBase< PointT >The (abstract) base class for the comparison object
testing::internal::CompileAssert< bool >
testing::internal::CompileAssertTypesEqual< T, T >
pcl::cloud_composer::ComposerMainWindowMainWindow of cloud_composer application
pcl::io::CompressionPointTraits< PointT >
pcl::io::CompressionPointTraits< PointXYZRGB >
pcl::io::CompressionPointTraits< PointXYZRGBA >
pcl::ihs::OfflineIntegration::ComputationFPSHelper object for the computation thread. Please have a look at the documentation of calcFPS
pcl::ihs::InHandScanner::ComputationFPSHelper object for the computation thread. Please have a look at the documentation of calcFPS
pcl::ComputeFailedException
pcl::ConditionalEuclideanClustering< PointT >ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition
pcl::ConditionalRemoval< PointT >ConditionalRemoval filters data that satisfies certain conditions
pcl::ConditionAnd< PointT >AND condition
pcl::ConditionBase< PointT >Base condition class
pcl::ConditionOr< PointT >OR condition
ConditionThresholdHSV< PointT >
config_s
pcl::io::configurationProfile_t
pcl::cloud_composer::SignalMultiplexer::Connection
testing::internal::ConstCharPtr
pcl::ConstCloudIterator< PointT >Iterator class for point clouds with or without given indices
pcl::ConstCloudIterator< PointT >::ConstIteratorIdx
pcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey3
pcl::poisson::OctNode< NodeData, Real >::ConstNeighborKey5
pcl::poisson::OctNode< NodeData, Real >::ConstNeighbors3
pcl::poisson::OctNode< NodeData, Real >::ConstNeighbors5
Consumer< PointT >
boost::container_gen< eigen_listS, ValueType >
boost::container_gen< eigen_vecS, ValueType >
pcl::registration::ConvergenceCriteriaConvergenceCriteria represents an abstract base class for different convergence criteria used in registration loops
pcl::filters::Convolution< PointIn, PointOut >
pcl::filters::Convolution3D< PointIn, PointOut, KernelT >
pcl::filters::ConvolvingKernel< PointInT, PointOutT >Class ConvolvingKernel base class for all convolving kernels
pcl::filters::ConvolvingKernel< PointT, pcl::Normal >
pcl::filters::ConvolvingKernel< PointT, pcl::PointXY >
mets::copyableAn interface for copyable objects
CopyBufferBuffer holding the points being copied and a set of operations for manipulating the buffer
CopyCommand
pcl::CopyIfFieldExists< PointInT, OutT >A helper functor that can copy a specific value if the given field exists
pcl::poisson::CoredEdgeIndex
pcl::poisson::CoredFileMeshData
pcl::poisson::CoredFileMeshData2
pcl::poisson::CoredMeshData
pcl::poisson::CoredMeshData2
pcl::poisson::CoredPointIndex
pcl::poisson::CoredVectorMeshData
pcl::poisson::CoredVectorMeshData2
pcl::poisson::CoredVertexIndex
pcl::poisson::SortedTreeNodes::CornerIndices
pcl::poisson::SortedTreeNodes::CornerTableData
pcl::CorrespondenceCorrespondence represents a match between two entities (e.g., points, descriptors, etc). This is represesented via the indices of a source point and a target point, and the distance between them
pcl::registration::CorrespondenceEstimation< PointSource, PointTarget, Scalar >CorrespondenceEstimation represents the base class for determining correspondences between target and query point sets/features
pcl::registration::CorrespondenceEstimationBackProjection< PointSource, PointTarget, NormalT, Scalar >CorrespondenceEstimationBackprojection computes correspondences as points in the target cloud which have minimum
pcl::registration::CorrespondenceEstimationBase< PointSource, PointTarget, Scalar >Abstract CorrespondenceEstimationBase class. All correspondence estimation methods should inherit from this
pcl::registration::CorrespondenceEstimationNormalShooting< PointSource, PointTarget, NormalT, Scalar >CorrespondenceEstimationNormalShooting computes correspondences as points in the target cloud which have minimum distance to normals computed on the input cloud
pcl::registration::CorrespondenceEstimationOrganizedProjection< PointSource, PointTarget, Scalar >CorrespondenceEstimationOrganizedProjection computes correspondences by projecting the source point cloud onto the target point cloud using the camera intrinsic and extrinsic parameters. The correspondences can be trimmed by a depth threshold and by a distance threshold. It is not as precise as a nearest neighbor search, but it is much faster, as it avoids the usage of any additional structures (i.e., kd-trees)
pcl::CorrespondenceGrouping< PointModelT, PointSceneT >Abstract base class for Correspondence Grouping algorithms
pcl::registration::CorrespondenceRejectionOrganizedBoundaryImplements a simple correspondence rejection measure. For each pair of points in correspondence, it checks whether they are on the boundary of a silhouette. This is done by counting the number of NaN dexels in a window around the points (the threshold and window size can be set by the user)
pcl::registration::CorrespondenceRejector
pcl::registration::CorrespondenceRejectorDistance
pcl::registration::CorrespondenceRejectorFeaturesCorrespondenceRejectorFeatures implements a correspondence rejection method based on a set of feature descriptors. Given an input feature space, the method checks if each feature in the source cloud has a correspondence in the target cloud, either by checking the first K (given) point correspondences, or by defining a tolerance threshold via a radius in feature space
pcl::registration::CorrespondenceRejectorMedianDistanceCorrespondenceRejectorMedianDistance implements a simple correspondence rejection method based on thresholding based on the median distance between the correspondences
pcl::registration::CorrespondenceRejectorOneToOneCorrespondenceRejectorOneToOne implements a correspondence rejection method based on eliminating duplicate match indices in the correspondences. Correspondences with the same match index are removed and only the one with smallest distance between query and match are kept. That is, considering match->query 1-m correspondences are removed leaving only 1-1 correspondences
pcl::registration::CorrespondenceRejectorPoly< SourceT, TargetT >CorrespondenceRejectorPoly implements a correspondence rejection method that exploits low-level and pose-invariant geometric constraints between two point sets by forming virtual polygons of a user-specifiable cardinality on each model using the input correspondences. These polygons are then checked in a pose-invariant manner (i.e. the side lengths must be approximately equal), and rejection is performed by thresholding these edge lengths
pcl::registration::CorrespondenceRejectorSampleConsensus< PointT >CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
pcl::registration::CorrespondenceRejectorSampleConsensus2D< PointT >CorrespondenceRejectorSampleConsensus2D implements a pixel-based correspondence rejection using Random Sample Consensus to identify inliers (and reject outliers)
pcl::registration::CorrespondenceRejectorSurfaceNormal
pcl::registration::CorrespondenceRejectorTrimmedCorrespondenceRejectorTrimmed implements a correspondence rejection for ICP-like registration algorithms that uses only the best 'k' correspondences where 'k' is some estimate of the overlap between the two point clouds being registered
pcl::registration::CorrespondenceRejectorVarTrimmed
pcl::CovarianceSampling< PointT, PointNT >Point Cloud sampling based on the 6D covariances. It selects the points such that the resulting cloud is as stable as possible for being registered (against a copy of itself) with ICP. The algorithm adds points to the resulting cloud incrementally, while trying to keep all the 6 eigenvalues of the covariance matrix as close to each other as possible. This class also comes with the computeConditionNumber method that returns a number which shows how stable a point cloud will be when used as input for ICP (the closer the value it is to 1.0, the better)
createSHOTDesc< FeatureEstimation, PointT, NormalT, OutputT >
createSHOTDesc< FeatureEstimation, PointT, NormalT, SHOT1344 >
createSHOTDesc< FeatureEstimation, PointT, NormalT, SHOT352 >
createSHOTDesc< ShapeContext3DEstimation< PointT, NormalT, OutputT >, PointT, NormalT, OutputT >
createSHOTDesc< UniqueShapeContext< PointT, OutputT >, PointT, NormalT, OutputT >
pcl::CrfNormalSegmentation< PointT >
pcl::CRHAlignment< PointT, nbins_ >CRHAlignment uses two Camera Roll Histograms (CRH) to find the roll rotation that aligns both views. See:
pcl::CRHEstimation< PointInT, PointNT, PointOutT >CRHEstimation estimates the Camera Roll Histogram (CRH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in:
pcl::CropBox< PointT >CropBox is a filter that allows the user to filter all the data inside of a given box
pcl::CropBox< pcl::PCLPointCloud2 >CropBox is a filter that allows the user to filter all the data inside of a given box
pcl::CropHull< PointT >Filter points that lie inside or outside a 3D closed surface or 2D closed polygon, as generated by the ConvexHull or ConcaveHull classes
ct_data_s
pcl::poisson::Cube
CurveJoinEndData
CurveJoinSeg
CUserDataHeaderInfo
pcl::CustomPointRepresentation< PointDefault >CustomPointRepresentation extends PointRepresentation to allow for sub-part selection on the point
CutCommand
pcl::CVFHEstimation< PointInT, PointNT, PointOutT >CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset containing XYZ data and normals, as presented in:
pcl::recognition::ORROctree::Node::Data
pcl::registration::DataContainer< PointT, NormalT >
pcl::registration::DataContainerInterface
pcl::traits::datatype< PointT, Tag >
DBLBLK
pcl::io::DeBayerVarious debayering methods
pcl::traits::decomposeArray< T >
pcl::ConstCloudIterator< PointT >::DefaultConstIterator
pcl::registration::DefaultConvergenceCriteria< Scalar >DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the following criteria for registration loop evaluation:
pcl::DefaultFeatureRepresentation< PointDefault >DefaulFeatureRepresentation extends PointRepresentation and is intended to be used when defining the default behavior for feature descriptor types (i.e., copy each element of each field into a float array)
testing::internal::DefaultGlobalTestPartResultReporter
pcl::DefaultIterator< PointT >
pcl::geometry::DefaultMeshTraits< VertexDataT, HalfEdgeDataT, EdgeDataT, FaceDataT >The mesh traits are used to set up compile time settings for the mesh
testing::internal::DefaultPerThreadTestPartResultReporter
pcl::DefaultPointRepresentation< PointDefault >DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types
pcl::DefaultPointRepresentation< FPFHSignature33 >
pcl::DefaultPointRepresentation< Narf36 >
pcl::DefaultPointRepresentation< NormalBasedSignature12 >
pcl::DefaultPointRepresentation< PFHRGBSignature250 >
pcl::DefaultPointRepresentation< PFHSignature125 >
pcl::DefaultPointRepresentation< PointNormal >
pcl::DefaultPointRepresentation< PointXYZ >
pcl::DefaultPointRepresentation< PointXYZI >
pcl::DefaultPointRepresentation< PPFSignature >
pcl::DefaultPointRepresentation< ShapeContext1980 >
pcl::DefaultPointRepresentation< SHOT1344 >
pcl::DefaultPointRepresentation< SHOT352 >
pcl::DefaultPointRepresentation< VFHSignature308 >
DeleteCommand
pcl::cloud_composer::DeleteItemCommand
DenoiseCommand
DenoiseParameterForm
pcl::DenseQuantizedMultiModTemplate
pcl::DenseQuantizedSingleModTemplate
mets::dereferenced_equal_to< Tp >Functor class to allow hash_set of moves (used by tabu list)
pcl::LineRGBD< PointXYZT, PointRGBT >::DetectionA LineRGBD detection
pcl::apps::optronic_viewer::Device
pcl::DifferenceOfNormalsEstimation< PointInT, PointNT, PointOutT >A Difference of Normals (DoN) scale filter implementation for point cloud data
pcl::DinastGrabberGrabber for DINAST devices (i.e., IPA-1002, IPA-1110, IPA-2001)
DinastProcessor< PointType >
pcl::visualization::context_items::Disk
pcl::tracking::DistanceCoherence< PointInT >DistanceCoherence computes coherence between two points from the distance between them. the coherence is calculated by 1 / (1 + weight * d^2 )
pcl::DistanceMapRepresents a distance map obtained from a distance transformation
pcl::modeler::DockWidget
pcl::ihs::Dome
pcl::apps::DominantPlaneSegmentation< PointType >DominantPlaneSegmentation performs euclidean segmentation on a scene assuming that a dominant plane exists
pcl::DOTMODTemplate matching using the DOTMOD approach
pcl::DOTModality
pcl::DOTMODDetection
pcl::GreedyProjectionTriangulation< PointInT >::doubleEdgeStruct for storing the edges starting from a fringe point
pcl::modeler::DoubleParameter
Driver
Dummy
pcl::EarClippingThe ear clipping triangulation algorithm. The code is inspired by Flavien Brebion implementation, which is in n^3 and does not handle holes
pcl::poisson::Edge
pcl::EdgeAwarePlaneComparator< PointT, PointNT >EdgeAwarePlaneComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data
pcl::geometry::EdgeIndexIndex used to access elements in the half-edge mesh. It is basically just a wrapper around an integer with a few added methods
pcl::poisson::EdgeIndex
pcl::poisson::SortedTreeNodes::EdgeIndices
EDGEINFO
pcl::registration::LUM< PointT >::EdgeProperties
pcl::poisson::SortedTreeNodes::EdgeTableData
boost::eigen_listS
boost::eigen_vecS
pcl::registration::ELCH< PointT >ELCH (Explicit Loop Closing Heuristic) class
pcl::io::ply::ply_parser::element
testing::EmptyTestEventListener
testing::internal::EnableIf< true >
pcl::EnergyMapsStores a set of energy maps
pcl::search::OrganizedNeighbor< PointT >::Entry
pcl::recognition::RotationSpaceCell::Entry
pcl::search::BruteForce< PointT >::Entry
pcl::modeler::EnumParameter< T >
testing::Environment
testing::internal::EqHelper< lhs_is_null_literal >
testing::internal::EqHelper< true >
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::ErrorFunctor
pcl::ESFEstimation< PointInT, PointOutT >ESFEstimation estimates the ensemble of shape functions descriptors for a given point cloud dataset containing points. Shape functions are D2, D3, A3. For more information about the ESF descriptor, see: Walter Wohlkinger and Markus Vincze, "Ensemble of Shape Functions for 3D Object Classification", IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO), 2011
pcl::ESFSignature640A point structure representing the Ensemble of Shape Functions (ESF)
pcl::EuclideanClusterComparator< PointT, PointNT, PointLT >EuclideanClusterComparator is a comparator used for finding clusters supported by planar surfaces. This needs to be run as a second pass after extracting planar surfaces, using MultiPlaneSegmentation for example
pcl::EuclideanClusterExtraction< PointT >EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
pcl::cloud_composer::EuclideanClusteringTool
pcl::cloud_composer::EuclideanClusteringToolFactory
pcl::EuclideanPlaneCoefficientComparator< PointT, PointNT >EuclideanPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data
mets::evaluable_solutionA copyable and evaluable solution implementation,
EventHelper
pcl::visualization::PCLHistogramVisualizer::ExitCallback
pcl::visualization::ImageViewer::ExitCallback
pcl::visualization::PCLPlotter::ExitCallback
pcl::visualization::PCLVisualizer::ExitCallback
pcl::visualization::Window::ExitCallback
pcl::visualization::PCLHistogramVisualizer::ExitMainLoopTimerCallback
pcl::visualization::ImageViewer::ExitMainLoopTimerCallback
pcl::visualization::PCLPainter2D::ExitMainLoopTimerCallback
pcl::visualization::PCLPlotter::ExitMainLoopTimerCallback
pcl::visualization::PCLVisualizer::ExitMainLoopTimerCallback
pcl::visualization::Window::ExitMainLoopTimerCallback
mets::exponential_coolingOriginal ECS proposed by Kirkpatrick
CloudEditorWidget::ExtCompare
pcl::ExtractIndices< PointT >ExtractIndices extracts a set of indices from a point cloud
pcl::ExtractIndices< pcl::PCLPointCloud2 >ExtractIndices extracts a set of indices from a point cloud.
Usage examples:
pcl::ExtractPolygonalPrismData< PointT >ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism. The polygonal prism is then used to segment all points lying inside it
pcl::geometry::FaceA face is a closed loop of edges
pcl::geometry::FaceAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the face of the outer half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getFaceAroundFaceCirculator ()
pcl::geometry::FaceAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the face of the outgoing half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getFaceAroundVertexCirculator ()
pcl::poisson::Octree< Degree >::FaceEdgesFunction
pcl::geometry::FaceIndexIndex used to access elements in the half-edge mesh. It is basically just a wrapper around an integer with a few added methods
pcl::ihs::detail::FaceVertexMeshMesh format more efficient for visualization than the half-edge data structure
pcl::apps::optronic_viewer::FastBilateralCFWrapper for the fast-bilateral filter. Applies the fast- bilateral filter on a cloud for smoothing
pcl::FastBilateralFilter< PointT >Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and Frdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06)
pcl::FastBilateralFilterOMP< PointT >Implementation of a fast bilateral filter for smoothing depth information in organized point clouds Based on the following paper: * Sylvain Paris and FrŽdo Durand "A Fast Approximation of the Bilateral Filter using a Signal Processing Approach" European Conference on Computer Vision (ECCV'06)
mets::feasible_solutionInterface of a feasible solution space to be searched with tabu search
pcl::Feature< PointInT, PointOutT >Feature represents the base feature class. Some generic 3D operations that are applicable to all features are defined here as static methods
FeatureCloud
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainer< FeatureT >An inner class containing pointers to the source and target feature clouds and the parameters needed to perform the correspondence search. This class extends FeatureContainerInterface, which contains abstract methods for any methods that do not depend on the FeatureT --- these methods can thus be called from a pointer to FeatureContainerInterface without casting to the derived class
pcl::registration::CorrespondenceRejectorFeatures::FeatureContainerInterface
pcl::FeatureFromLabels< PointInT, PointLT, PointOutT >
pcl::FeatureFromNormals< PointInT, PointNT, PointOutT >
pcl::Narf::FeaturePointRepresentation
pcl::FeatureWithLocalReferenceFrames< PointInT, PointRFT >FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which need a local reference frame at each input keypoint
pcl::visualization::FEllipticArc2DClass for storing EllipticArc; every ellipse , circle are covered by this
pcl::detail::FieldAdder< PointT >
pcl::FieldComparison< PointT >The field-based specialization of the comparison object
pcl::traits::fieldList< PointT >
pcl::detail::FieldMapper< PointT >
pcl::detail::FieldMapping
pcl::FieldMatches< PointT, Tag >
pcl::visualization::Figure2DAbstract class for storing figure information. All the derived class uses the same method draw() to invoke different drawing function of vtkContext2D
pcl::FileGrabber< PointT >FileGrabber provides a container-style interface for grabbers which operate on fixed-size input
testing::internal::FilePath
pcl::FileReaderPoint Cloud Data (FILE) file format reader interface. Any (FILE) format file reader should implement its virtual methodes
pcl::FileWriterPoint Cloud Data (FILE) file format writer. Any (FILE) format file reader should implement its virtual methodes
pcl::visualization::context_items::FilledRectangle
pcl::Filter< PointT >Filter represents the base filter class. All filters must inherit from this interface
pcl::Filter< pcl::PCLPointCloud2 >Filter represents the base filter class. All filters must inherit from this interface
pcl::FilterIndices< PointT >FilterIndices represents the base class for filters that are about binary point removal.
All derived classes have to implement the filter (PointCloud &output) and the filter (std::vector<int> &indices) methods. Ideally they also make use of the negative_, keep_organized_ and extract_removed_indices_ systems. The distinguishment between the negative_ and extract_removed_indices_ systems only makes sense if the class automatically filters non-finite entries in the filtering methods (recommended)
pcl::FilterIndices< pcl::PCLPointCloud2 >FilterIndices represents the base class for filters that are about binary point removal.
All derived classes have to implement the filter (PointCloud &output) and the filter (std::vector<int> &indices) methods. Ideally they also make use of the negative_, keep_organized_ and extract_removed_indices_ systems. The distinguishment between the negative_ and extract_removed_indices_ systems only makes sense if the class automatically filters non-finite entries in the filtering methods (recommended)
pcl::apps::optronic_viewer::FilterWindowWindow class for wizards to create new filters
pcl::on_nurbs::FittingCurve2d::FitParameter
pcl::on_nurbs::FittingCurve2dAPDM::FitParameter
pcl::on_nurbs::FittingCurve2dPDM::FitParameter
pcl::on_nurbs::FittingCurveFitting a 3D B-Spline curve to point-clouds using point-distance-minimization and optionally asymmetric-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCurve2dFitting a 2D B-Spline curve to 2D point-clouds using point-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCurve2dAPDMFitting a 2D B-Spline curve to 2D point-clouds using asymmetric point-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCurve2dASDMFitting a 2D B-Spline curve to 2D point-clouds using asymmetric squared-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCurve2dATDMFitting a 2D B-Spline curve to 2D point-clouds using asymmetric-tangent-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCurve2dPDMFitting a 2D B-Spline curve to 2D point-clouds using point-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCurve2dSDMFitting a 2D B-Spline curve to 2D point-clouds using squared-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCurve2dTDMFitting a 2D B-Spline curve to 2D point-clouds using tangent-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingCylinderFitting a cylindric (dim 0 clamped, dim 1 periodic) B-Spline surface to 3D point-clouds using point-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingSphereFitting a cylindric (dim 0 clamped, dim 1 periodic) B-Spline surface to 3D point-clouds using point-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingSurfaceFitting a B-Spline surface to 3D point-clouds using point-distance-minimization Based on paper: TODO
pcl::on_nurbs::FittingSurfaceIM
pcl::on_nurbs::FittingSurfaceTDMFitting a B-Spline surface to 3D point-clouds using tangent-distance-minimization Based on paper: TODO
pcl::search::FlannSearch< PointT, FlannDistance >::FlannIndexCreatorHelper class that creates a FLANN index from a given FLANN matrix. To use a FLANN index type with FlannSearch, implement this interface and pass an object of the new type to the FlannSearch constructor. See the implementation of KdTreeIndexCreator for an example
pcl::search::FlannSearch< PointT, FlannDistance >search::FlannSearch is a generic FLANN wrapper class for the new search interface. It is able to wrap any FLANN index type, e.g. the kd tree as well as indices for high-dimensional searches and intended as a more powerful and cleaner successor to KdTreeFlann
pcl::visualization::FloatImageUtils
testing::internal::FloatingPoint< RawType >
testing::internal::FloatingPoint< RawType >::FloatingPointUnion
pcl::for_each_type_impl< done >
pcl::for_each_type_impl< false >
mets::forever
pcl::apps::optronic_viewer::FotonicDevice
pcl::FPFHEstimation< PointInT, PointNT, PointOutT >FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
pcl::FPFHEstimationOMP< PointInT, PointNT, PointOutT >FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard
pcl::cloud_composer::FPFHEstimationTool
pcl::cloud_composer::FPFHEstimationToolFactory
pcl::cloud_composer::FPFHItem
pcl::FPFHSignature33A point structure representing the Fast Point Feature Histogram (FPFH)
pcl::visualization::FPoints2DClass for storing Points
pcl::visualization::FPolygon2DClass for Polygon
pcl::visualization::FPolyLine2DClass for PolyLine
pcl::ihs::OpenGLViewer::FPSPlease have a look at the documentation of calcFPS
pcl::visualization::PCLVisualizer::FPSCallback
pcl::visualization::FQuad2DClass for storing Quads
Frame
pcl::FrustumCulling< PointT >FrustumCulling filters points inside a frustum given by pose and field of view of the camera
pcl::poisson::FunctionData< Degree, Real >
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::Functor< _Scalar, NX, NY >
pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::Functor< _Scalar, NX, NY >
pcl::Functor< _Scalar, NX, NY >
pcl::segmentation::grabcut::GaussianGaussian structure
pcl::segmentation::grabcut::GaussianFitter
pcl::GaussianKernel
pcl::filters::GaussianKernel< PointInT, PointOutT >Gaussian kernel implementation interface Use this as implementation reference
pcl::filters::GaussianKernelRGB< PointInT, PointOutT >Gaussian kernel implementation interface with RGB channel handling Use this as implementation reference
pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >GeneralizedIterativeClosestPoint is an ICP variant that implements the generalized iterative closest point algorithm as described by Alex Segal et al. in http://www.stanford.edu/~avsegal/resources/papers/Generalized_ICP.pdf The approach is based on using anistropic cost functions to optimize the alignment after closest point assignments have been made. The original code uses GSL and ANN while in ours we use an eigen mapped BFGS and FLANN
pcl::GeometricConsistencyGrouping< PointModelT, PointSceneT >Class implementing a 3D correspondence grouping enforcing geometric consistency among feature correspondences
Geometry
std::tr1::gtest_internal::Get< 0 >
std::tr1::gtest_internal::Get< 1 >
std::tr1::gtest_internal::Get< 2 >
std::tr1::gtest_internal::Get< 3 >
std::tr1::gtest_internal::Get< 4 >
std::tr1::gtest_internal::Get< 5 >
std::tr1::gtest_internal::Get< 6 >
std::tr1::gtest_internal::Get< 7 >
std::tr1::gtest_internal::Get< 8 >
std::tr1::gtest_internal::Get< 9 >
pcl::GFPFHEstimation< PointInT, PointLT, PointOutT >GFPFHEstimation estimates the Global Fast Point Feature Histogram (GFPFH) descriptor for a given point cloud dataset containing points and labels
pcl::GFPFHSignature16A point structure representing the GFPFH descriptor with 16 bins
pcl::GlobalHypothesesVerification< ModelT, SceneT >A hypothesis verification method proposed in "A Global Hypotheses Verification Method for 3D Object Recognition", A. Aldoma and F. Tombari and L. Di Stefano and Markus Vincze, ECCV 2012
pcl::on_nurbs::GlobalOptimizationFitting and optimizing multiple B-Spline surfaces to 3D point-clouds using point-distance-minimization in a single system of equations (global). Based on paper: TODO
pcl::on_nurbs::GlobalOptimizationTDMFitting and optimizing multiple B-Spline surfaces to 3D point-clouds using tangent-distance-minimization (TDM) in a single system of equations (global). Based on paper: TODO
pcl::segmentation::grabcut::GMM
pcl::GrabberGrabber interface for PCL 1.x device drivers
pcl::GrabCut< PointT >Implementation of the GrabCut segmentation in "GrabCut — Interactive Foreground Extraction using Iterated Graph Cuts" by Carsten Rother, Vladimir Kolmogorov and Andrew Blake
pcl::GradientXYA point structure representing Euclidean xyz coordinates, and the intensity value
pcl::registration::GraphHandler< GraphT >GraphHandler class is a wrapper for a general SLAM graph The actual graph class must fulfil the following boost::graph concepts:
pcl::registration::GraphOptimizer< GraphT >GraphOptimizer class; derive and specialize for each graph type
pcl::GraphRegistration< GraphT >GraphRegistration class is the base class for graph-based registration methods
pcl::GreedyProjectionTriangulation< PointInT >GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections. It assumes locally smooth surfaces and relatively smooth transitions between areas with different point densities
pcl::GreedyVerification< ModelT, SceneT >A greedy hypothesis verification method
Grid
pcl::GridProjection< PointNT >Grid projection surface reconstruction method
pcl::people::GroundBasedPeopleDetectionApp< PointT >
pcl::GroundPlaneComparator< PointT, PointNT >GroundPlaneComparator is a Comparator for detecting smooth surfaces suitable for driving. In conjunction with OrganizedConnectedComponentSegmentation, this allows smooth groundplanes / road surfaces to be segmented from point clouds
testing::internal::GTestFlagSaver
testing::internal::GTestLog
testing::internal::GTestMutexLock
gz_header_s
pcl::geometry::HalfEdgeAn edge is a connection between two vertices. In a half-edge mesh the edge is split into two half-edges with opposite orientation. Each half-edge stores the index to the terminating vertex, the next half-edge, the previous half-edge and the face it belongs to. The opposite half-edge is accessed implicitly
pcl::geometry::HalfEdgeIndexIndex used to access elements in the half-edge mesh. It is basically just a wrapper around an integer with a few added methods
pcl::HarrisKeypoint2D< PointInT, PointOutT, IntensityT >HarrisKeypoint2D detects Harris corners family points
pcl::HarrisKeypoint3D< PointInT, PointOutT, NormalT >HarrisKeypoint3D uses the idea of 2D Harris keypoints, but instead of using image gradients, it uses surface normals
pcl::HarrisKeypoint6D< PointInT, PointOutT, NormalT >Keypoint detector for detecting corners in 3D (XYZ), 2D (intensity) AND mixed versions of these
__gnu_cxx::hash< const long long >
__gnu_cxx::hash< const unsigned long long >
__gnu_cxx::hash< long long >
__gnu_cxx::hash< unsigned long long >
mets::hashableAn interface for hashable objects
pcl::PPFHashMapSearch::HashKeyStructData structure to hold the information for the key in the feature hash map of the PPFHashMapSearch class
testing::internal::HasNewFatalFailureHelper
pcl::HDLGrabber::HDLDataPacket
pcl::HDLGrabber::HDLFiringData
pcl::HDLGrabberGrabber for the Velodyne High-Definition-Laser (HDL)
pcl::HDLGrabber::HDLLaserCorrection
pcl::HDLGrabber::HDLLaserReturn
pcl::ApproximateVoxelGrid< PointT >::he
pcl::people::HeadBasedSubclustering< PointT >
pcl::people::HeightMap2D< PointT >
pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT, NrDims >
pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor::Helper< Key, FieldT[NrDims], NrDims >
pcl::ihs::HelpWindow
pcl::Histogram< N >A point structure representing an N-D histogram
pcl::people::HOGHOG represents a class for computing the HOG descriptor described in Dalal, N. and Triggs, B., "Histograms of oriented gradients for human detection", CVPR 2005
pcl::Hough3DGrouping< PointModelT, PointSceneT, PointModelRfT, PointSceneRfT >Class implementing a 3D correspondence grouping algorithm that can deal with multiple instances of a model template found into a given scene. Each correspondence casts a vote for a reference point in a 3D Hough Space. The remaining 3 DOF are taken into account by associating each correspondence with a local Reference Frame. The suggested PointModelRfT is pcl::ReferenceFrame
pcl::recognition::HoughSpace3DHoughSpace3D is a 3D voting space. Cast votes can be interpolated in order to better deal with approximations introduced by bin quantization. A weight can also be associated with each vote
pcl::tracking::HSVColorCoherence< PointInT >HSVColorCoherence computes coherence between the two points from the color difference between them. the color difference is calculated in HSV color space. the coherence is calculated by 1 / ( 1 + w * (w_h^2 * h_diff^2 + w_s^2 * s_diff^2 + w_v^2 * v_diff^2))
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::HuberPenalty
pcl::recognition::Hypothesis
pcl::recognition::HypothesisBase
pcl::recognition::ObjRecRANSAC::HypothesisCreator
pcl::HypothesisVerification< ModelT, SceneT >Abstract class for hypotheses verification methods
ICCVTutorial< FeatureType >
pcl::ihs::ICPIterative Closest Point registration
pcl::modeler::ICPRegistrationWorker
pcl::ImageGrabber< PointT >
pcl::ImageGrabberBaseBase class for Image file grabber
pcl::ImageGrabberBase::ImageGrabberImpl
pcl::visualization::ImageViewerImageViewer is a class for 2D image visualization
pcl::visualization::ImageViewerInteractorStyleAn image viewer interactor style, tailored for ImageViewer
testing::internal::ImplicitlyConvertible< From, To >
pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >This class implements Implicit Shape Model algorithm described in "Hough Transforms and 3D SURF for robust three dimensional classication" by Jan Knopp1, Mukta Prasad, Geert Willems1, Radu Timofte, and Luc Van Gool. It has two main member functions. One for training, using the data for which we know which class it belongs to. And second for investigating a cloud for the presence of the class of interest. Implementation of the ISM algorithm described in "Hough Transforms and 3D SURF for robust three dimensional classication" by Jan Knopp, Mukta Prasad, Geert Willems, Radu Timofte, and Luc Van Gool
mets::improvement_logger< neighborhood_t >
IncIndexSimple functor that produces sequential integers from an initial value
pcl::geometry::IncomingHalfEdgeAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the incoming half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getIncomingHalfEdgeAroundVertexCirculator ()
pcl::DefaultFeatureRepresentation< PointDefault >::IncrementFunctor
inflate_state
pcl::ihs::InHandScanner
pcl::InitFailedExceptionAn exception thrown when init can not be performed should be used in all the PCLBase class inheritants
pcl::geometry::InnerHalfEdgeAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the inner half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getInnerHalfEdgeAroundFaceCirculator ()
pcl::ihs::InputDataProcessingBundles methods that are applied to the input data from the sensor
pcl::IntegralImage2D< DataType, Dimension >Determines an integral image representation for a given organized data array
pcl::IntegralImage2D< DataType, 1 >Partial template specialization for integral images with just one channel
pcl::IntegralImageNormalEstimation< PointInT, PointOutT >Surface normal estimation on organized data using integral images
pcl::IntegralImageTypeTraits< DataType >
pcl::IntegralImageTypeTraits< char >
pcl::IntegralImageTypeTraits< float >
pcl::IntegralImageTypeTraits< int >
pcl::IntegralImageTypeTraits< short >
pcl::IntegralImageTypeTraits< unsigned char >
pcl::IntegralImageTypeTraits< unsigned int >
pcl::IntegralImageTypeTraits< unsigned short >
pcl::ihs::IntegrationIntegrate several clouds into a common mesh
pcl::IntensityA point structure representing the grayscale intensity in single-channel images. Intensity is represented as a float value
pcl::Intensity32uA point structure representing the grayscale intensity in single-channel images. Intensity is represented as a uint8_t value
pcl::Intensity8uA point structure representing the grayscale intensity in single-channel images. Intensity is represented as a uint8_t value
pcl::common::IntensityFieldAccessor< PointT >
pcl::common::IntensityFieldAccessor< pcl::PointNormal >
pcl::common::IntensityFieldAccessor< pcl::PointXYZ >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGB >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBA >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBL >
pcl::common::IntensityFieldAccessor< pcl::PointXYZRGBNormal >
pcl::IntensityGradientA point structure representing the intensity gradient of an XYZI point cloud
pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT, IntensitySelectorT >IntensityGradientEstimation estimates the intensity gradient for a point cloud that contains position and intensity values. The intensity gradient at a given point will be a vector orthogonal to the surface normal and pointing in the direction of the greatest increase in local intensity; the vector's magnitude indicates the rate of intensity change
pcl::IntensitySpinEstimation< PointInT, PointOutT >IntensitySpinEstimation estimates the intensity-domain spin image descriptors for a given point cloud dataset containing points and intensity. For more information about the intensity-domain spin image descriptor, see:
pcl::cloud_composer::InteractorStyleSwitch
pcl::InterestPointA point structure representing an interest point with Euclidean xyz coordinates, and an interest value
internal_state
pcl::intersect< Sequence1, Sequence2 >
pcl::modeler::IntParameter
pcl::InvalidConversionExceptionAn exception that is thrown when a PCLPointCloud2 message cannot be converted into a PCL type
pcl::InvalidSACModelTypeExceptionAn exception that is thrown when a sample consensus model doesn't have the correct number of samples defined in model_types.h
mets::invert_full_neighborhoodGenerates a the full subsequence inversion neighborhood
mets::invert_subsequenceA mets::mana_move that swaps a subsequence of elements in a mets::permutation_problem
pcl::IOExceptionAn exception that is thrown during an IO error (typical read/write errors)
openni_wrapper::IRImageClass containing just a reference to IR meta data
testing::internal::is_pointer< T >
testing::internal::is_pointer< T * >
boost::detail::is_random_access< eigen_listS >
boost::detail::is_random_access< eigen_vecS >
testing::internal::IsAProtocolMessage< T >
pcl::PosesFromMatches::PoseEstimate::IsBetter
pcl::features::ISMModelThe assignment of this structure is to store the statistical/learned weights and other information of the trained Implict Shape Model algorithm
pcl::ISMPeakThis struct is used for storing peak
pcl::features::ISMVoteList< PointT >This class is used for storing, analyzing and manipulating votes obtained from ISM algorithm
pcl::IsNotDenseExceptionAn exception that is thrown when a PointCloud is not dense but is attemped to be used as dense
pcl::ISSKeypoint3D< PointInT, PointOutT, NormalT >ISSKeypoint3D detects the Intrinsic Shape Signatures keypoints for a given point cloud. This class is based on a particular implementation made by Federico Tombari and Samuele Salti and it has been explicitly adapted to PCL
pcl::cloud_composer::ItemInspectorView class for displaying properties of an item
mets::iteration_logger< neighborhood_t >
mets::iteration_termination_criteriaTermination criteria based on the number of iterations
pcl::IterativeClosestPoint< PointSource, PointTarget, Scalar >IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm. The transformation is estimated based on Singular Value Decomposition (SVD)
pcl::IterativeClosestPointNonLinear< PointSource, PointTarget, Scalar >IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend. The resultant transformation is optimized as a quaternion
pcl::IterativeClosestPointWithNormals< PointSource, PointTarget, Scalar >IterativeClosestPointWithNormals is a special case of IterativeClosestPoint, that uses a transformation estimated based on Point to Plane distances by default
pcl::CloudIterator< PointT >::Iterator
pcl::ConstCloudIterator< PointT >::Iterator
pcl::IteratorIdx< PointT >
pcl::octree::IteratorState
testing::internal::IteratorTraits< Iterator >
testing::internal::IteratorTraits< const T * >
testing::internal::IteratorTraits< T * >
pcl::search::KdTree< PointT >search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search functions using KdTree structure. KdTree is a generic type of 3D spatial locator using kD-tree structures. The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe
pcl::KdTree< PointT >KdTree represents the base spatial locator class for kd-tree implementations
pcl::KdTreeFLANN< PointT, Dist >KdTreeFLANN is a generic type of 3D spatial locator using kD-tree structures. The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project by Marius Muja and David Lowe
pcl::search::FlannSearch< PointT, FlannDistance >::KdTreeIndexCreatorCreates a FLANN KdTreeSingleIndex from the given input data
pcl::KernelWidthTooSmallExceptionAn exception that is thrown when the kernel size is too small
KeyboardCallback
pcl::visualization::KeyboardEvent
pcl::Keypoint< PointInT, PointOutT >Keypoint represents the base class for key points
KeypointT
kiss_fft_cpx
kiss_fft_state
kiss_fftr_state
pcl::tracking::KLDAdaptiveParticleFilterOMPTracker< PointInT, StateT >KLDAdaptiveParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method. The number of the particles changes adaptively based on KLD sampling [D. Fox, NIPS-01], [D.Fox, IJRR03]. and the computation of the weights of the particles is parallelized using OpenMP
pcl::tracking::KLDAdaptiveParticleFilterTracker< PointInT, StateT >KLDAdaptiveParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method. The number of the particles changes adaptively based on KLD sampling [D. Fox, NIPS-01], [D.Fox, IJRR03]
pcl::search::FlannSearch< PointT, FlannDistance >::KMeansIndexCreatorCreates a FLANN KdTreeSingleIndex from the given input data
pcl::Label
pcl::LabeledEuclideanClusterExtraction< PointT >LabeledEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense, with label info
pcl::visualization::ImageViewer::LayerInternal structure describing a layer
pcl::visualization::ImageViewer::LayerComparator
pcl::VoxelGridCovariance< PointT >::LeafSimple structure to hold a centroid, covarince and the number of points in a leaf. Inverse covariance, eigen vectors and engen values are precomputed
pcl::UniformSampling< PointInT >::LeafSimple structure to hold an nD centroid and the number of points in a leaf
pcl::GridProjection< PointNT >::LeafData leaf
pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGrid::Leaf
pcl::LeastMedianSquares< PointT >LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm. LMedS is a RANSAC-like model-fitting algorithm that can tolerate up to 50% outliers without requiring thresholds to be set. See Andrea Fusiello's "Elements of Geometric Computer Vision" (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html#x1-520007) for more details
pcl::visualization::context_items::Line
mets::linear_coolingAlternative LCS proposed by Randelman and Grest
pcl::LinearizedMapsStores a set of linearized maps
pcl::LinearLeastSquaresNormalEstimation< PointInT, PointOutT >Surface normal estimation on dense data using a least-squares estimation based on a first-order Taylor approximation
pcl::LineIteratorOrganized Index Iterator for iterating over the "pixels" for a given line using the Bresenham algorithm. Supports 4 and 8 neighborhood connectivity
pcl::LINEMODTemplate matching using the LINEMOD approach
pcl::LINEMOD_OrientationMapMap that stores orientations
pcl::LINEMODDetectionRepresents a detection of a template using the LINEMOD approach
pcl::LineRGBD< PointXYZT, PointRGBT >High-level class for template matching using the LINEMOD approach based on RGB and Depth data
testing::internal::linked_ptr< T >
testing::internal::linked_ptr_internal
pcl::io::ply::ply_parser::list_property< SizeType, ScalarType >
pcl::io::ply::ply_parser::list_property_begin_callback_type< SizeType, ScalarType >
pcl::io::ply::ply_parser::list_property_definition_callback_type< SizeType, ScalarType >
pcl::io::ply::ply_parser::list_property_definition_callbacks_type
pcl::io::ply::ply_parser::list_property_element_callback_type< SizeType, ScalarType >
pcl::io::ply::ply_parser::list_property_end_callback_type< SizeType, ScalarType >
mets::local_search< move_manager_type >Local search algorithm
pcl::RangeImageBorderExtractor::LocalSurfaceStores some information extracted from the neighborhood of a point
pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::LocationInfoThis structure stores the information about the keypoint
LRUCache< KeyT, CacheItemT >
LRUCacheItem< T >
pcl::registration::LUM< PointT >Globally Consistent Scan Matching based on an algorithm by Lu and Milios
pcl::io::LZFBayer8ImageReaderPCL-LZF 8-bit Bayer image format reader
pcl::io::LZFBayer8ImageWriterPCL-LZF 8-bit Bayer image format writer
pcl::io::LZFDepth16ImageReaderPCL-LZF 16-bit depth image format reader
pcl::io::LZFDepth16ImageWriterPCL-LZF 16-bit depth image format writer
pcl::io::LZFImageReaderPCL-LZF image format reader. The PCL-LZF image format is nothing else but a LZF-modified compression over an existing file type (e.g., PNG). However, in certain situations, like RGB data for example, an [RGBRGB...RGB] array will be first reordered into [RR...RGG...GBB...B] in order to ensure better compression
pcl::io::LZFImageWriterPCL-LZF image format writer. The PCL-LZF image format is nothing else but a LZF-modified compression over an existing file type (e.g., PNG). However, in certain situations, like RGB data for example, an [RGBRGB...RGB] array will be first reordered into [RR...RGG...GBB...B] in order to ensure better compression
pcl::io::LZFRGB24ImageReaderPCL-LZF 24-bit RGB image format reader
pcl::io::LZFRGB24ImageWriterPCL-LZF 24-bit RGB image format writer
pcl::io::LZFYUV422ImageReaderPCL-LZF 8-bit Bayer image format reader
pcl::io::LZFYUV422ImageWriterPCL-LZF 16-bit YUV422 image format writer
pcl::apps::optronic_viewer::MainWindow
MainWindowClass for point cloud editor
pcl::ihs::MainWindow
pcl::modeler::MainWindow
mets::mana_moveA Mana Move is a move that can be automatically made tabu by the mets::simple_tabu_list
mets::mana_move_hashFunctor class to allow hash_set of moves (used by tabu list)
pcl::cloud_composer::ManipulationEvent
ManualRegistration
ON_SerialNumberMap::MAP_VALUE
pcl::poisson::MapReduceVector< T2 >
pcl::poisson::MarchingCubes
pcl::MarchingCubes< PointNT >The marching cubes surface reconstruction algorithm. This is an abstract class that takes a grid and extracts the isosurface as a mesh, based on the original marching cubes paper:
pcl::MarchingCubesHoppe< PointNT >The marching cubes surface reconstruction algorithm, using a signed distance function based on the distance from tangent planes, proposed by Hoppe et. al. in: Hoppe H., DeRose T., Duchamp T., MC-Donald J., Stuetzle W., "Surface reconstruction from unorganized points", SIGGRAPH '92
pcl::MarchingCubesRBF< PointNT >The marching cubes surface reconstruction algorithm, using a signed distance function based on radial basis functions. Partially based on: Carr J.C., Beatson R.K., Cherrie J.B., Mitchell T.J., Fright W.R., McCallum B.C. and Evans T.R., "Reconstruction and representation of 3D objects with radial basis functions" SIGGRAPH '01
pcl::poisson::MarchingSquares
pcl::MaskMap
pcl::poisson::MatrixEntry< T >
pcl::MaximumLikelihoodSampleConsensus< PointT >MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000
pcl::apps::optronic_viewer::MedianCFWrapper for a Median filter. Applies the Median filter on a cloud
pcl::MedianFilter< PointT >Implementation of the median filter. The median filter is one of the simplest and wide-spread image processing filters. It is known to perform well with "shot"/impulse noise (some individual pixels having extreme values), it does not reduce contrast across steps in the function (as compared to filters based on averaging), and it is robust to outliers. Furthermore, it is simple to implement and efficient, as it requires a single pass over the image. It consists of a moving window of fixed size that replaces the pixel in the center with the median inside the window
ON_MeshTopology::memchunk
pcl::cloud_composer::MergeCloudCommand
pcl::cloud_composer::MergeCloudTool
pcl::cloud_composer::MergeSelection
Mesh
pcl::geometry::MeshBase< DerivedT, MeshTraitsT, MeshTagT >Base class for the half-edge mesh
pcl::MeshConstruction< PointInT >MeshConstruction represents a base surface reconstruction class. All mesh constructing methods that take in a point cloud and generate a surface that uses the original data as vertices should inherit from this class
pcl::geometry::MeshIO< MeshT >Read / write the half-edge mesh from / to a file
pcl::ihs::MeshProcessingContains methods that take advantage of the connectivity information in the mesh
pcl::MeshProcessingMeshProcessing represents the base class for mesh processing algorithms
pcl::MeshQuadricDecimationVTKPCL mesh decimation based on vtkQuadricDecimation from the VTK library. Please check out the original documentation for more details on the inner workings of the algorithm Warning: This wrapper does two fairly computationally expensive conversions from the PCL PolygonMesh data structure to the vtkPolyData data structure and back
pcl::MeshSmoothingLaplacianVTKPCL mesh smoothing based on the vtkSmoothPolyDataFilter algorithm from the VTK library. Please check out the original documentation for more details on the inner workings of the algorithm Warning: This wrapper does two fairly computationally expensive conversions from the PCL PolygonMesh data structure to the vtkPolyData data structure and back
pcl::MeshSmoothingWindowedSincVTKPCL mesh smoothing based on the vtkWindowedSincPolyDataFilter algorithm from the VTK library. Please check out the original documentation for more details on the inner workings of the algorithm Warning: This wrapper does two fairly computationally expensive conversions from the PCL PolygonMesh data structure to the vtkPolyData data structure and back
pcl::MeshSubdivisionVTKPCL mesh smoothing based on the vtkLinearSubdivisionFilter, vtkLoopSubdivisionFilter, vtkButterflySubdivisionFilter depending on the selected MeshSubdivisionVTKFilterType algorithm from the VTK library. Please check out the original documentation for more details on the inner workings of the algorithm Warning: This wrapper does two fairly computationally expensive conversions from the PCL PolygonMesh data structure to the vtkPolyData data structure and back
MeshTraits< IsManifoldT >
TestMeshCirculators::MeshTraits
testing::Message
pcl::MEstimatorSampleConsensus< PointT >MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000
pcl::poisson::MinimalAreaTriangulation< Real >
pcl::MovingLeastSquares< PointInT, PointOutT >::MLSResultData structure used to store the results of the MLS fitting
pcl::MovingLeastSquares< PointInT, PointOutT >::MLSVoxelGridA minimalistic implementation of a voxel grid, necessary for the point cloud upsampling
pcl::recognition::ModelLibrary::ModelStores some information about the model
pcl::ModelCoefficients
pcl::GreedyVerification< ModelT, SceneT >::modelIndices
pcl::recognition::ModelLibrary
pcl::cloud_composer::ModifyItemCommand
pcl::cloud_composer::ModifyItemTool
pcl::MomentInvariantsA point structure representing the three moment invariants
pcl::MomentInvariantsEstimation< PointInT, PointOutT >MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
MonitorQueue< DataT >
pcl::visualization::MouseEvent
mets::moveMove to be operated on a feasible solution
pcl::GlobalHypothesesVerification< ModelT, SceneT >::move
mets::move_managerA neighborhood generator
pcl::GlobalHypothesesVerification< ModelT, SceneT >::move_manager
pcl::MovingLeastSquares< PointInT, PointOutT >MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation. It also contains methods for upsampling the resulting cloud based on the parametric fit. Reference paper: "Computing and Rendering Point Set Surfaces" by Marc Alexa, Johannes Behr, Daniel Cohen-Or, Shachar Fleishman, David Levin and Claudio T. Silva www.sci.utah.edu/~shachar/Publications/crpss.pdf
pcl::MultiscaleFeaturePersistence< PointSource, PointFeature >Generic class for extracting the persistent features from an input point cloud It can be given any Feature estimator instance and will compute the features of the input over a multiscale representation of the cloud and output the unique ones over those scales
testing::internal::Mutex
MyPoint
MyPointRepresentation
pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >::MyPointRepresentationInternal point representation uses only 3D coordinates for L2
MyPointRepresentationXY
pcl::on_nurbs::FittingSurface::myvec
MyVertexData
pcl::traits::name< PointT, Tag, dummy >
pcl::NarfNARF (Normal Aligned Radial Features) is a point feature descriptor type for 3D data. Please refer to pcl/features/narf_descriptor.h if you want the class derived from pcl Feature. See B. Steder, R. B. Rusu, K. Konolige, and W. Burgard Point Feature Extraction on 3D Range Scans Taking into Account Object Boundaries In Proc. of the IEEE Int. Conf. on Robotics &Automation (ICRA). 2011
pcl::Narf36A point structure representing the Narf descriptor
pcl::NarfDescriptor
pcl::NarfKeypointNARF (Normal Aligned Radial Feature) keypoints. Input is a range image, output the indices of the keypoints See B. Steder, R. B. Rusu, K. Konolige, and W. Burgard Point Feature Extraction on 3D Range Scans Taking into Account Object Boundaries In Proc. of the IEEE Int. Conf. on Robotics &Automation (ICRA). 2011
testing::internal::NativeArray< Element >
pcl::NdCentroidFunctor< PointT, Scalar >Helper functor structure for n-D centroid estimation
pcl::NdConcatenateFunctor< PointInT, PointOutT >Helper functor structure for concatenate
pcl::NdCopyEigenPointFunctor< PointOutT >Helper functor structure for copying data between an Eigen type and a PointT
pcl::NdCopyPointEigenFunctor< PointInT >Helper functor structure for copying data between an Eigen type and a PointT
pcl::DefaultFeatureRepresentation< PointDefault >::NdCopyPointFunctor
pcl::ndt2d::NDT2D< PointT >Build a Normal Distributions Transform of a 2D point cloud. This consists of the sum of four overlapping models of the original points with normal distributions. The value and derivatives of the model at any point can be evaluated with the test (...) function
pcl::ndt2d::NDTSingleGrid< PointT >Build a set of normal distributions modelling a 2D point cloud, and provide the value and derivatives of the model at any point via the test (...) function
pcl::tracking::NearestPairPointCloudCoherence< PointInT >NearestPairPointCloudCoherence computes coherence between two pointclouds using the nearest point pairs
pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >::Neighbor
pcl::poisson::OctNode< NodeData, Real >::NeighborKey3
pcl::poisson::OctNode< NodeData, Real >::NeighborKey5
pcl::poisson::OctNode< NodeData, Real >::Neighbors3
pcl::poisson::OctNode< NodeData, Real >::Neighbors5
pcl::cloud_composer::NewItemCloudCommand
pcl::cloud_composer::NewItemTool
NILinemod
pcl::GrabCut< PointT >::NLinks
pcl::GreedyProjectionTriangulation< PointInT >::nnAngleStruct for storing the angles to nearest neighbors
pcl::NNClassification< PointT >Nearest neighbor search based classification of PCL point type features. FLANN is used to identify a neighborhood, based on which different scoring schemes can be employed to obtain likelihood values for a specified list of classes
mets::no_moves_errorException risen when some algorithm has no more moves to make
pcl::geometry::NoDataNo data is associated with the vertices / half-edges / edges / faces
pcl::recognition::BVH< UserData >::Node
pcl::recognition::ORRGraph< NodeData >::Node
pcl::recognition::ORROctree::Node
pcl::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar >::Node
mets::noimprove_termination_criteriaTermination criteria based on the number of iterations without an improvement
pcl::NormalA point structure representing normal coordinates and the surface curvature estimate. (SSE friendly)
pcl::common::normal_distribution< T >Normal distribution
pcl::NormalBasedSignature12A point structure representing the Normal Based Signature for a feature matrix of 4-by-3
pcl::NormalBasedSignatureEstimation< PointT, PointNT, PointFeature >Normal-based feature signature estimation class. Obtains the feature vector by applying Discrete Cosine and Fourier Transforms on an NxM array of real numbers representing the projection distances of the points in the input cloud to a disc around the point of interest. Please consult the following publication for more details: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria
pcl::tracking::NormalCoherence< PointInT >NormalCoherence computes coherence between two points from the angle between their normals. the coherence is calculated by 1 / (1 + weight * theta^2 )
pcl::ndt2d::NormalDist< PointT >A normal distribution estimation class
pcl::NormalDistributionsTransform< PointSource, PointTarget >A 3D Normal Distribution Transform registration implementation for point cloud data
pcl::NormalDistributionsTransform2D< PointSource, PointTarget >NormalDistributionsTransform2D provides an implementation of the Normal Distributions Transform algorithm for scan matching
pcl::NormalEstimation< PointInT, PointOutT >NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point. If PointOutT is specified as pcl::Normal, the normal is stored in the first 3 components (0-2), and the curvature is stored in component 3
pcl::NormalEstimationOMP< PointInT, PointOutT >NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
pcl::cloud_composer::NormalEstimationTool
pcl::cloud_composer::NormalEstimationToolFactory
pcl::modeler::NormalEstimationWorker
pcl::common::NormalGenerator< T >NormalGenerator class generates a random number from a normal distribution specified by (mean, sigma)
pcl::NormalRefinement< NormalT >Normal vector refinement class
pcl::modeler::NormalsActorItem
pcl::cloud_composer::NormalsItem
pcl::NormalSpaceSampling< PointT, NormalT >NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every point
pcl::NotEnoughPointsExceptionAn exception that is thrown when the number of correspondants is not equal to the minimum required
pcl::registration::NullEstimateNullEstimate struct
pcl::registration::NullMeasurementNullMeasurement struct
Eigen::NumTraits< pcl::ndt2d::NormalDist< PointT > >
pcl::on_nurbs::NurbsDataCurveData structure for 3D NURBS curve fitting (FittingCurve)
pcl::on_nurbs::NurbsDataCurve2dData structure for 2D NURBS curve fitting (FittingCurve2d, FittingCurve2dTDM, FittingCurve2dSDM)
pcl::on_nurbs::NurbsDataSurfaceData structure for NURBS surface fitting (FittingSurface, FittingSurfaceTDM, FittingCylinder, GlobalOptimization, GlobalOptimizationTDM)
pcl::on_nurbs::NurbsSolveSolving the linear system of equations using Eigen or UmfPack. (can be defined in on_nurbs.cmake)
pcl::on_nurbs::NurbsToolsSome useful tools for initialization, point search, ..
pcl::poisson::NVector< T, Dim >
pcl::keypoints::agast::OastDetector9_16Detector class for AGAST corner point detector (OAST 9_16)
Object
ObjectFeatures
ObjectModel
ObjectRecognition
ObjectRecognitionParameters
ObjectSelection< PointT >
pcl::recognition::ObjRecRANSACThis is a RANSAC-based 3D object recognition method. Do the following to use it: (i) call addModel() k times with k different models representing the objects to be recognized and (ii) call recognize() with the 3D scene in which the objects should be recognized. Recognition means both object identification and pose (position + orientation) estimation. Check the method descriptions for more details
mets::observer< observed_subject >Template base class for the observers of some observed_subject
pcl::poisson::OctNode< NodeData, Real >
pcl::search::Octree< PointT, LeafTWrap, BranchTWrap, OctreeT >search::Octree is a wrapper class which implements nearest neighbor search operations based on the pcl::octree::Octree structure
pcl::poisson::Octree< Degree >
pcl::octree::Octree2BufBase< LeafContainerT, BranchContainerT >Octree double buffer class
pcl::octree::OctreeBase< LeafContainerT, BranchContainerT >Octree class
pcl::octree::OctreeBranchNode< ContainerT >Abstract octree branch class
pcl::octree::OctreeBreadthFirstIterator< OctreeT >Octree iterator class
pcl::octree::OctreeContainerBaseOctree container class that can serve as a base to construct own leaf node container classes
pcl::octree::OctreeContainerEmptyOctree container class that does not store any information
pcl::octree::OctreeContainerPointIndexOctree container class that does store a single point index
pcl::octree::OctreeContainerPointIndicesOctree container class that does store a vector of point indices
pcl::octree::OctreeDepthFirstIterator< OctreeT >Octree iterator class
pcl::octree::OctreeIteratorBase< OctreeT >Abstract octree iterator class
pcl::octree::OctreeKeyOctree key class
pcl::octree::OctreeLeafNode< ContainerT >Abstract octree leaf class
pcl::octree::OctreeLeafNodeIterator< OctreeT >Octree leaf node iterator class
pcl::octree::OctreeNodeAbstract octree node class
pcl::octree::OctreeNodePool< NodeT >Octree node pool
OctreePointCloud
pcl::octree::OctreePointCloud< PointT, LeafContainerT, BranchContainerT, OctreeT >Octree pointcloud class
pcl::octree::OctreePointCloudAdjacency< PointT, LeafContainerT, BranchContainerT >Octree pointcloud voxel class used for adjacency calculation
pcl::octree::OctreePointCloudAdjacencyContainer< PointInT, DataT >Octree adjacency leaf container class- stores set of pointers to neighbors, number of points added, and a DataT value
pcl::octree::OctreePointCloudChangeDetector< PointT, LeafContainerT, BranchContainerT >Octree pointcloud change detector class
pcl::io::OctreePointCloudCompression< PointT, LeafT, BranchT, OctreeT >Octree pointcloud compression class
pcl::octree::OctreePointCloudDensity< PointT, LeafContainerT, BranchContainerT >Octree pointcloud density class
pcl::octree::OctreePointCloudDensityContainerOctree pointcloud density leaf node class
pcl::octree::OctreePointCloudOccupancy< PointT, LeafContainerT, BranchContainerT >Octree pointcloud occupancy class
pcl::octree::OctreePointCloudPointVector< PointT, LeafContainerT, BranchContainerT, OctreeT >Octree pointcloud point vector class
pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >Octree pointcloud search class
pcl::octree::OctreePointCloudSinglePoint< PointT, LeafContainerT, BranchContainerT, OctreeT >Octree pointcloud single point class
pcl::octree::OctreePointCloudVoxelCentroid< PointT, LeafContainerT, BranchContainerT >Octree pointcloud voxel centroid class
pcl::octree::OctreePointCloudVoxelCentroidContainer< PointT >Octree pointcloud voxel centroid leaf node class
OctreeT
OctreeViewer
pcl::ihs::OfflineIntegrationRead the clouds and transformations from files and integrate them into one common model
pcl::traits::offset< PointT, Tag >
ON_2dexMap
ON_2dPoint
ON_2dPointArray
ON_2dVector
ON_2dVectorArray
ON_2fPoint
ON_2fPointArray
ON_2fVector
ON_2fVectorArray
ON_3DM_BIG_CHUNK
ON_3DM_CHUNK
ON_3dmAnnotationSettings
ON_3dmApplication
ON_3dmConstructionPlane
ON_3dmConstructionPlaneGridDefaults
ON_3dmGoo
ON_3dmIOSettings
ON_3dmNotes
ON_3dmObjectAttributes
ON_3dmPageSettings
ON_3dmProperties
ON_3dmRenderSettings
ON_3dmRevisionHistory
ON_3dmSettings
ON_3dmUnitsAndTolerances
ON_3dmView
ON_3dmViewPosition
ON_3dmViewTraceImage
ON_3dmWallpaperImage
ON_3dPoint
ON_3dPointArray
ON_3dRay
ON_3dVector
ON_3dVectorArray
ON_3fPoint
ON_3fPointArray
ON_3fVector
ON_3fVectorArray
ON_4dPoint
ON_4dPointArray
ON_4fPoint
ON_4fPointArray
ON__3dmV1_XDATA
ON__3dmV1LayerIndex
ON__CChangeTextureCoordinateHelper
ON__CIndexMaps
ON__CIndexPair
ON__ClassIdDumpNode
ON__CMeshFaceTC
ON__CNewMeshFace
ON__EDGE_ENDS
ON__IDefAlternativePathUserData
ON__IDefLayerSettingsUserData
ON__LayerExtensions
ON__LayerPerViewSettings
ON__LayerSettingsUserData
ON__MESHEDGE
ON__NEWVI
ON__OBSOLETE__CircleCurve
ON_AngularDimension
ON_AngularDimension2
ON_AngularDimension2Extra
ON_Annotation
ON_Annotation2
ON_Annotation2Text
ON_AnnotationArrow
ON_AnnotationTextDot
ON_AnnotationTextFormula
ON_Arc
ON_ArcCurve
ON_aStringHeader
ON_Base64EncodeImplementation
ON_Base64EncodeStream
ON_BezierCage
ON_BezierCageMorph
ON_BezierCurve
ON_BezierSurface
ON_BinaryArchive
ON_BinaryArchiveBuffer
ON_BinaryFile
ON_Bitmap
ON_BoolValue
ON_BoundingBox
ON_Box
ON_Brep
ON_BrepEdge
ON_BrepEdgeArray
ON_BrepFace
ON_BrepFaceArray
ON_BrepFaceSide
ON_BrepFaceSideArray
ON_BrepLoop
ON_BrepLoopArray
ON_BrepRegion
ON_BrepRegionArray
ON_BrepRegionTopology
ON_BrepRegionTopologyUserData
ON_BrepTrim
ON_BrepTrimArray
ON_BrepTrimPoint
ON_BrepVertex
ON_BrepVertexArray
ON_Buffer
ON_BUFFER_SEGMENT
ON_BumpFunction
ON_CageMorph
ON_CheckSum
ON_Circle
ON_ClassArray< T >
ON_ClassId
ON_ClippingPlane
ON_ClippingPlaneInfo
ON_ClippingPlaneSurface
ON_ClippingRegion
ON_Color
ON_ColorValue
ON_CompressedBuffer
ON_CompressedBufferHelper
ON_CompressStream
ON_Cone
ON_Curve
ON_CurveArray
ON_CurveOnSurface
ON_CurveProxy
ON_CurveProxyHistory
ON_Cylinder
ON_DecodeBase64
ON_DetailView
ON_DimensionExtra
ON_DimStyle
ON_DimStyleExtra
ON_DisplayMaterialRef
ON_DocumentUserStringList
ON_DoubleValue
ON_DummyValue
ON_EarthAnchorPoint
ON_Ellipse
ON_EmbeddedBitmap
ON_EmbeddedFile
ON_Evaluator
ON_Extrusion
ON_Extrusion_BrepForm_FaceInfo
ON_FileIterator
ON_FileStream
ON_FixedSizePool
ON_FixedSizePoolIterator
ON_Font
ON_Geometry
ON_GeometryValue
ON_Group
ON_Hatch
ON_HatchExtra
ON_HatchLine
ON_HatchLoop
ON_HatchPattern
ON_HistoryRecord
ON_InstanceDefinition
ON_InstanceRef
ON_Interval
ON_IntValue
ON_Layer
ON_Leader
ON_Leader2
ON_Light
ON_Line
ON_LinearDimension
ON_LinearDimension2
ON_LineCurve
ON_Linetype
ON_LinetypeSegment
ON_Localizer
ON_LocalZero1
ON_MappingChannel
ON_MappingRef
ON_MappingTag
ON_Material
ON_MaterialRef
ON_Matrix
ON_Mesh
ON_MeshCurvatureStats
ON_MeshCurveParameters
ON_MeshDoubleVertices
ON_MeshEdgeRef
ON_MeshFace
ON_MeshFaceRef
ON_MeshFaceSide
ON_MeshNgon
ON_MeshNgonList
ON_MeshNgonUserData
ON_MeshParameters
ON_MeshPart
ON_MeshPartition
ON_MeshTopology
ON_MeshTopologyEdge
ON_MeshTopologyFace
ON_MeshTopologyVertex
ON_MeshVertexRef
ON_MorphControl
ON_NGON_MEMBLK
ON_NurbsCage
ON_NurbsCurve
ON_NurbsSurface
ON_Object
ON_ObjectArray< T >
ON_ObjectRenderingAttributes
ON_ObjRef
ON_ObjRef_IRefID
ON_ObjRefEvaluationParameter
ON_ObjRefValue
ON_OBSOLETE_CCustomMeshUserData
ON_OffsetSurface
ON_OffsetSurfaceFunction
ON_OffsetSurfaceValue
ON_OrdinateDimension2
ON_PerObjectMeshParameters
ON_PgonPt
ON_Plane
ON_PlaneEquation
ON_PlaneSurface
ON_PlugInRef
ON_Point
ON_PointCloud
ON_PointGrid
ON_PointValue
ON_PolyCurve
ON_PolyEdgeCurve
ON_PolyEdgeHistory
ON_PolyEdgeHistoryValue
ON_PolyEdgeSegment
ON_Polyline
ON_PolylineCurve
ON_PolynomialCurve
ON_PolynomialSurface
ON_RadialDimension
ON_RadialDimension2
ON_RANDOM_NUMBER_CONTEXT
ON_Read3dmBufferArchive
ON_ReadChunkHelper
ON_RenderingAttributes
ON_RevolutionTensor
ON_RevSurface
ON_RTree
ON_RTreeBBox
ON_RTreeBranch
ON_RTreeCapsule
ON_RTreeIterator
ON_RTreeLeaf
ON_RTreeListNodeA link list of nodes for reinsertion after a delete operation
ON_RTreeMemPool
ON_RTreeNode
ON_RTreePairSearchCallbackResult
ON_RTreePairSearchCallbackResultBool
ON_RTreePairSearchResult
ON_RTreePartitionVarsVariables for finding a split partition
ON_RTreeSearchResult
ON_RTreeSearchResultCallback
ON_RTreeSphere
ON_SerialNumberMap
ON_SimpleArray< T >
ON_SimpleFixedSizePool< T >
ON_SpaceMorph
ON_Sphere
ON_String
ON_StringValue
ON_Sum
ON_SumSurface
ON_SumTensor
ON_Surface
ON_SurfaceArray
ON_SurfaceCurvature
ON_SurfaceProperties
ON_SurfaceProxy
ON_TensorProduct
ON_TextDot
ON_TextEntity
ON_TextEntity2
ON_TextExtra
ON_TextLog
ON_Texture
ON_TextureCoordinates
ON_TextureMapping
ON_Torus
ON_U
ON_UncompressStream
ON_UnicodeErrorParameters
ON_UnitSystem
ON_UnknownUserData
ON_UnknownUserDataArchive
ON_UserData
ON_UserDataHolder
ON_UserString
ON_UserStringList
ON_UUID
ON_UuidIndexList
ON_UuidList
ON_UuidPair
ON_UuidPairList
ON_UuidValue
ON_Value
ON_VectorValue
ON_Viewport
ON_WindowsBitmap
ON_WindowsBitmapEx
ON_WindowsBITMAPINFO
ON_WindowsBITMAPINFOHEADER
ON_WindowsRGBQUAD
ON_Workspace
ON_Workspace_FBLK
ON_Workspace_MBLK
ON_Write3dmBufferArchive
ON_wString
ON_wStringHeader
ON_Xform
ON_XformValue
ON_ZlibImplementation
ONX_Model
ONX_Model_Object
ONX_Model_RenderLight
ONX_Model_UserData
pcl::ihs::OpenGLViewerViewer for the in-hand scanner based on Qt and OpenGL
OpenNI3DConcaveHull< PointType >
OpenNI3DConvexHull< PointType >
OpenNICapture
OpenNIChangeViewer
pcl::apps::optronic_viewer::OpenNIDevice
OpenNIFastMesh< PointType >
OpenNIFeaturePersistence< PointType >
OpenNIFrameSource::OpenNIFrameSource
pcl::apps::optronic_viewer::OpenNIGrabberWrapper for the grabbing from an OpenNI device. Wrapper is used to run the grabbing in a separate thread (QThread)
OpenNIGrabFrame< PointType >
OpenNIIntegralImageNormalEstimation< PointType >
OpenNIOrganizedMultiPlaneSegmentation
OpenNIPassthrough< PointType >
OpenNIPlanarSegmentation< PointType >
OpenNISegmentTracking< PointType >
OpenNISmoothing< PointType >
OpenNIUniformSampling
OpenNIViewer< PointType >
OpenNIVoxelGrid< PointType >
pcl::SampleConsensusModelCylinder< PointT, PointNT >::OptimizationFunctorFunctor for the optimization function
pcl::SampleConsensusModelCircle3D< PointT >::OptimizationFunctorFunctor for the optimization function
pcl::SampleConsensusModelSphere< PointT >::OptimizationFunctor
pcl::SampleConsensusModelCone< PointT, PointNT >::OptimizationFunctorFunctor for the optimization function
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctor
pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctor
pcl::SampleConsensusModelCircle2D< PointT >::OptimizationFunctorFunctor for the optimization function
pcl::GeneralizedIterativeClosestPoint< PointSource, PointTarget >::OptimizationFunctorWithIndicesOptimization functor structure
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices
pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >::OptimizationFunctorWithIndices
pcl::OrganizedConnectedComponentSegmentation< PointT, PointLT >OrganizedConnectedComponentSegmentation allows connected components to be found within organized point cloud data, given a comparison function. Given an input cloud and a comparator, it will output a PointCloud of labels, giving each connected component a unique id, along with a vector of PointIndices corresponding to each component. See OrganizedMultiPlaneSegmentation for an example application
pcl::io::OrganizedConversion< PointT, false >
pcl::io::OrganizedConversion< PointT, true >
pcl::OrganizedFastMesh< PointInT >Simple triangulation/surface reconstruction for organized point clouds. Neighboring points (pixels in image space) are connected to construct a triangular mesh
pcl::OrganizedIndexIteratorBase class for iterators on 2-dimensional maps like images/organized clouds etc
pcl::OrganizedMultiPlaneSegmentation< PointT, PointNT, PointLT >OrganizedMultiPlaneSegmentation finds all planes present in the input cloud, and outputs a vector of plane equations, as well as a vector of point clouds corresponding to the inliers of each detected plane. Only planes with more than min_inliers points are detected. Templated on point type, normal type, and label type
pcl::search::OrganizedNeighbor< PointT >OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds
pcl::io::OrganizedPointCloudCompression< PointT >
OrganizedSegmentationDemo
pcl::cloud_composer::OrganizedSegmentationTool
pcl::cloud_composer::OrganizedSegmentationToolFactory
pcl::recognition::ObjRecRANSAC::OrientedPointPair
pcl::recognition::ORRGraph< NodeData >
pcl::recognition::ORROctreeThat's a very specialized and simple octree class. That's the way it is intended to be, that's why no templates and stuff like this
pcl::recognition::ORROctreeZProjection
testing::internal::OsStackTraceGetter
testing::internal::OsStackTraceGetterInterface
pcl::OURCVFHEstimation< PointInT, PointNT, PointOutT >OURCVFHEstimation estimates the Oriented, Unique and Repetable Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given point cloud dataset given XYZ data and normals, as presented in:
pcl::geometry::OuterHalfEdgeAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the outer half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getOuterHalfEdgeAroundFaceCirculator ()
pcl::geometry::OutgoingHalfEdgeAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the outgoing half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getOutgoingHalfEdgeAroundVertexCirculator ()
pcl::outofcore::OutofcoreAbstractMetadata
pcl::outofcore::OutofcoreAbstractNodeContainer< PointT >
pcl::outofcore::OutofcoreBreadthFirstIterator< PointT, ContainerT >
OutofcoreCloud
pcl::outofcore::OutofcoreDepthFirstIterator< PointT, ContainerT >
pcl::outofcore::OutofcoreIteratorBase< PointT, ContainerT >Abstract octree iterator class
pcl::outofcore::OutofcoreOctreeBase< ContainerT, PointT >This code defines the octree used for point storage at Urban Robotics
pcl::outofcore::OutofcoreOctreeBaseMetadataEncapsulated class to read JSON metadata into memory, and write the JSON metadata associated with the octree root node. This is global information that is not the same as the metadata for the root node. Inherits OutofcoreAbstractMetadata interface for metadata in pcl_outofcore
pcl::outofcore::OutofcoreOctreeBaseNode< ContainerT, PointT >OutofcoreOctreeBaseNode Class internally representing nodes of an outofcore octree, with accessors to its data via the octree_disk_container class or octree_ram_container class, whichever it is templated against
pcl::outofcore::OutofcoreOctreeDiskContainer< PointT >Class responsible for serialization and deserialization of out of core point data
pcl::outofcore::OutofcoreOctreeNodeMetadataEncapsulated class to read JSON metadata into memory, and write the JSON metadata for each node
pcl::outofcore::OutofcoreOctreeRamContainer< PointT >Storage container class which the outofcore octree base is templated against
pcl::outofcore::OutofcoreParams
OutofcoreTest
pcl::recognition::ObjRecRANSAC::OutputThis is an output item of the ObjRecRANSAC::recognize() method. It contains the recognized model, its name (the ones passed to ObjRecRANSAC::addModel()), the rigid transform which aligns the model with the input scene and the match confidence which is a number in the interval (0, 1] which gives the fraction of the model surface area matched to the scene. E.g., a match confidence of 0.3 means that 30% of the object surface area was matched to the scene points. If the scene is represented by a single range image, the match confidence can not be greater than 0.5 since the range scanner sees only one side of each object
pcl::cloud_composer::OutputPair
pcl::PackedHSIComparison< PointT >A packed HSI specialization of the comparison object
pcl::PackedRGBComparison< PointT >A packed rgb specialization of the comparison object
pcl::io::ply::ply_parser::list_property_definition_callbacks_type::pair_with< T >
pcl::PairwiseGraphRegistration< GraphT, PointT >PairwiseGraphRegistration class aligns the clouds two by two
pcl::PapazovHV< ModelT, SceneT >A hypothesis verification method proposed in "An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes", C. Papazov and D. Burschka, ACCV 2010
boost::parallel_edge_traits< eigen_listS >
boost::parallel_edge_traits< eigen_vecS >
pcl::on_nurbs::FittingSurfaceIM::ParameterParameters for fitting
pcl::on_nurbs::ClosingBoundary::Parameter
pcl::on_nurbs::FittingCurve2d::ParameterParameters for fitting
pcl::on_nurbs::FittingCurve2dAPDM::ParameterParameters for fitting
pcl::on_nurbs::FittingCurve2dPDM::ParameterParameters for fitting
pcl::on_nurbs::FittingSurface::ParameterParameters for fitting
pcl::on_nurbs::SequentialFitter::Parameter
pcl::on_nurbs::FittingCurve::Parameter
pcl::modeler::Parameter
pcl::on_nurbs::GlobalOptimization::ParameterParameters for fitting
pcl::modeler::ParameterDelegate
pcl::modeler::ParameterDialog
pcl::modeler::ParameterModel
pcl::NarfKeypoint::ParametersParameters used in this class
pcl::common::UniformGenerator< T >::Parameters
pcl::RangeImageBorderExtractor::ParametersParameters used in this class
pcl::PolynomialCalculationsT< real >::ParametersParameters used in this class
pcl::PosesFromMatches::ParametersParameters used in this class
pcl::NarfDescriptor::Parameters
pcl::common::NormalGenerator< T >::Parameters
BFGS< FunctorType >::Parameters
pcl::on_nurbs::FittingSurfaceTDM::ParameterTDMParameters with TDM extensions for fitting
pcl::on_nurbs::GlobalOptimizationTDM::ParameterTDMParameters for fitting
pcl::tracking::ParticleFilterOMPTracker< PointInT, StateT >ParticleFilterOMPTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method in parallel, using the OpenMP standard
pcl::tracking::ParticleFilterTracker< PointInT, StateT >ParticleFilterTracker tracks the PointCloud which is given by setReferenceCloud within the measured PointCloud using particle filter method
pcl::tracking::ParticleXYR
pcl::tracking::ParticleXYRP
pcl::tracking::ParticleXYRPY
pcl::tracking::ParticleXYZR
pcl::tracking::ParticleXYZRPY
pcl::PassThrough< PointT >PassThrough passes points in a cloud based on constraints for one particular field of the point type
pcl::PassThrough< pcl::PCLPointCloud2 >PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
pcl::apps::optronic_viewer::PassThroughCFWrapper for a pass-through filter. Removes all points that are out of a specified range for a specified component (e.g. x, y, or z)
PasteCommand
pcl::PCA< PointT >
PCD
PCDBuffer< PointT >
PCDComparator
pcl::PCDGrabber< PointT >
pcl::PCDGrabberBaseBase class for PCD file grabber
pcl::PCDGrabberBase::PCDGrabberImpl
PCDOrganizedMultiPlaneSegmentation< PointT >
OutofcoreCloud::PcdQueueItem
pcl::PCDReaderPoint Cloud Data (PCD) file format reader
PCDVideoPlayer
pcl::PCDWriterPoint Cloud Data (PCD) file format writer
pcl::PCLBase< PointT >PCL base class. Implements methods that are used by most PCL algorithms
pcl::PCLBase< pcl::PCLPointCloud2 >
pcl::visualization::PCLContextImageItem
pcl::visualization::PCLContextItem
pcl::PCLExceptionA base class for all pcl exceptions which inherits from std::runtime_error
pcl::PCLHeader
pcl::visualization::PCLHistogramVisualizerPCL histogram visualizer main class
pcl::visualization::PCLHistogramVisualizerInteractorStylePCL histogram visualizer interactory style class
pcl::PCLImage
pcl::visualization::PCLImageCanvasSource2DPCLImageCanvasSource2D represents our own custom version of vtkImageCanvasSource2D, used by the ImageViewer class
PCLMobileServer< PointType >
pcl::visualization::PCLPainter2DPCL Painter2D main class. Class for drawing 2D figures
pcl::visualization::PCLPlotterPCL Plotter main class. Given point correspondences this class can be used to plot the data one against the other and display it on the screen. It also has methods for providing plot for important functions like histogram etc. Important functions of PCLHistogramVisualizer are redefined here so that this single class can take responsibility of all plotting related functionalities
pcl::PCLPointCloud2
pcl::PCLPointField
pcl::visualization::PCLSimpleBufferVisualizerPCL simple buffer visualizer main class
pcl::PCLSurfaceBase< PointInT >Pure abstract class. All types of meshing/reconstruction algorithms in libpcl_surface must inherit from this, in order to make sure we have a consistent API. The methods that we care about here are:
pcl::visualization::PCLVisualizerPCL Visualizer main class
pcl::visualization::PCLVisualizerInteractorThe PCLVisualizer interactor
pcl::visualization::PCLVisualizerInteractorStylePCLVisualizerInteractorStyle defines an unique, custom VTK based interactory style for PCL Visualizer applications. Besides defining the rendering style, we also create a list of custom actions that are triggered on different keys being pressed:
pcl::CRHAlignment< PointT, nbins_ >::peaks_orderingSorts peaks
mets::permutation_problemAn abstract permutation problem
pcl::people::PersonClassifier< PointT >
pcl::people::PersonCluster< PointT >
pcl::PFHEstimation< PointInT, PointNT, PointOutT >PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
pcl::PFHRGBEstimation< PointInT, PointNT, PointOutT >
pcl::PFHRGBSignature250A point structure representing the Point Feature Histogram with colors (PFHRGB)
pcl::PFHSignature125A point structure representing the Point Feature Histogram (PFH)
pcl::PiecewiseLinearFunctionThis provides functionalities to efficiently return values for piecewise linear function
pcl::recognition::ORROctreeZProjection::Pixel
pcl::PlanarPolygon< PointT >PlanarPolygon represents a planar (2D) polygon, potentially in a 3D space
pcl::PlanarPolygonFusion< PointT >PlanarPolygonFusion takes a list of 2D planar polygons and attempts to reduce them to a minimum set that best represents the scene, based on various given comparators
pcl::PlanarRegion< PointT >PlanarRegion represents a set of points that lie in a plane. Inherits summary statistics about these points from Region3D, and summary statistics of a 3D collection of points
pcl::apps::optronic_viewer::PlaneCFWrapper for a filter that finds the dominant plane in the cloud and either keeps only the plane or everything else
pcl::PlaneClipper3D< PointT >Implementation of a plane clipper in 3D
pcl::PlaneCoefficientComparator< PointT, PointNT >PlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data
pcl::PlaneRefinementComparator< PointT, PointNT, PointLT >PlaneRefinementComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data
Player
pcl::io::ply::ply_parser
ply_to_obj_converter
ply_to_ply_converter
ply_to_raw_converter
pcl::PLYReaderPoint Cloud Data (PLY) file format reader
pcl::PLYWriterPoint Cloud Data (PLY) file format writer
pcl::traits::POD< PointT >
pcl::visualization::context_items::Point
pcl::poisson::Point3D< Real >
pcl::PointCloud< PointT >PointCloud represents the base class in PCL for storing collections of 3D points
PointCloudBuffers
pcl::tracking::PointCloudCoherence< PointInT >PointCloudCoherence is a base class to compute coherence between the two PointClouds
pcl::visualization::PointCloudColorHandler< PointT >Base Handler class for PointCloud colors
pcl::visualization::PointCloudColorHandler< pcl::PCLPointCloud2 >Base Handler class for PointCloud colors
pcl::visualization::PointCloudColorHandlerCustom< PointT >Handler for predefined user colors. The color at each point will be drawn as the use given R, G, B values
pcl::visualization::PointCloudColorHandlerCustom< pcl::PCLPointCloud2 >Handler for predefined user colors. The color at each point will be drawn as the use given R, G, B values
pcl::visualization::PointCloudColorHandlerGenericField< PointT >Generic field handler class for colors. Uses an user given field to extract 1D data and display the color at each point using a min-max lookup table
pcl::visualization::PointCloudColorHandlerGenericField< pcl::PCLPointCloud2 >Generic field handler class for colors. Uses an user given field to extract 1D data and display the color at each point using a min-max lookup table
pcl::visualization::PointCloudColorHandlerHSVField< PointT >HSV handler class for colors. Uses the data present in the "h", "s", "v" fields as the color at each point
pcl::visualization::PointCloudColorHandlerHSVField< pcl::PCLPointCloud2 >HSV handler class for colors. Uses the data present in the "h", "s", "v" fields as the color at each point
pcl::visualization::PointCloudColorHandlerRandom< PointT >Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen)
pcl::visualization::PointCloudColorHandlerRandom< pcl::PCLPointCloud2 >Handler for random PointCloud colors (i.e., R, G, B will be randomly chosen)
pcl::visualization::PointCloudColorHandlerRGBField< PointT >RGB handler class for colors. Uses the data present in the "rgb" or "rgba" fields as the color at each point
pcl::visualization::PointCloudColorHandlerRGBField< pcl::PCLPointCloud2 >RGB handler class for colors. Uses the data present in the "rgb" or "rgba" fields as the color at each point
pcl::visualization::PointCloudGeometryHandler< PointT >Base handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandler< pcl::PCLPointCloud2 >Base handler class for PointCloud geometry
pcl::visualization::PointCloudGeometryHandlerCustom< PointT >Custom handler class for PointCloud geometry. Given an input dataset and three user defined fields, all data present in them is extracted and displayed on screen as XYZ data
pcl::visualization::PointCloudGeometryHandlerCustom< pcl::PCLPointCloud2 >Custom handler class for PointCloud geometry. Given an input dataset and three user defined fields, all data present in them is extracted and displayed on screen as XYZ data
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< PointT >Surface normal handler class for PointCloud geometry. Given an input dataset, all data present in fields "normal_x", "normal_y", and "normal_z" is extracted and dislayed on screen as XYZ data
pcl::visualization::PointCloudGeometryHandlerSurfaceNormal< pcl::PCLPointCloud2 >Surface normal handler class for PointCloud geometry. Given an input dataset, all data present in fields "normal_x", "normal_y", and "normal_z" is extracted and dislayed on screen as XYZ data
pcl::visualization::PointCloudGeometryHandlerXYZ< PointT >XYZ handler class for PointCloud geometry. Given an input dataset, all XYZ data present in fields "x", "y", and "z" is extracted and displayed on screen
pcl::visualization::PointCloudGeometryHandlerXYZ< pcl::PCLPointCloud2 >XYZ handler class for PointCloud geometry. Given an input dataset, all XYZ data present in fields "x", "y", and "z" is extracted and displayed on screen
pcl::octree::PointCoding< PointT >PointCoding class
pcl::tracking::PointCoherence< PointInT >PointCoherence is a base class to compute coherence between the two points
pcl::PointCorrespondence3DRepresentation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g. from feature matching)
pcl::PointCorrespondence6DRepresentation of a (possible) correspondence between two points (e.g. from feature matching), that encode complete 6DOF transoformations
pcl::poisson::Octree< Degree >::PointData
pcl::PointDataAtOffset< PointT >A datatype that enables type-correct comparisons
pcl::ihs::PointIHS
pcl::PointIndices
pcl::poisson::Octree< Degree >::PointInfo
pcl::PointNormalA point structure representing Euclidean xyz coordinates, together with normal coordinates and the surface curvature estimate. (SSE friendly)
pcl::visualization::PointPickingCallback
pcl::visualization::PointPickingEvent
pcl::PointRepresentation< PointT >PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector
pcl::PointRGBA point structure for representing RGB color
pcl::visualization::context_items::Points
pcl::modeler::PointsActorItem
pcl::PointSurfelA surfel, that is, a point structure representing Euclidean xyz coordinates, together with normal coordinates, a RGBA color, a radius, a confidence value and the surface curvature estimate
pcl::PointUVA 2D point structure representing pixel image coordinates
pcl::PointWithRangeA point structure representing Euclidean xyz coordinates, padded with an extra range float
pcl::PointWithScaleA point structure representing a 3-D position and scale
pcl::PointWithViewpointA point structure representing Euclidean xyz coordinates together with the viewpoint from which it was seen
pcl::PointXYA 2D point structure representing Euclidean xy coordinates
pcl::PointXYZA point structure representing Euclidean xyz coordinates. (SSE friendly)
PointXYZFPFH33
pcl::PointXYZHSV
pcl::PointXYZI
pcl::PointXYZINormalA point structure representing Euclidean xyz coordinates, intensity, together with normal coordinates and the surface curvature estimate
pcl::PointXYZL
pcl::PointXYZRGBA point structure representing Euclidean xyz coordinates, and the RGB color
pcl::PointXYZRGBAA point structure representing Euclidean xyz coordinates, and the RGBA color
pcl::PointXYZRGBL
pcl::PointXYZRGBNormalA point structure representing Euclidean xyz coordinates, and the RGB color, together with normal coordinates and the surface curvature estimate. Due to historical reasons (PCL was first developed as a ROS package), the RGB information is packed into an integer and casted to a float. This is something we wish to remove in the near future, but in the meantime, the following code snippet should help you pack and unpack RGB colors in your PointXYZRGB structure:
pcl::Poisson< PointNT >The Poisson surface reconstruction algorithm
pcl::modeler::PoissonReconstructionWorker
pcl::visualization::context_items::Polygon
pcl::PolygonMesh
pcl::geometry::PolygonMesh< MeshTraitsT >General half-edge mesh that can store any polygon with a minimum number of vertices of 3
pcl::geometry::PolygonMeshTagTag describing the type of the mesh
pcl::poisson::Polynomial< Degree >
pcl::PolynomialCalculationsT< real >This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials
Eigen::PolynomialSolver< _Scalar, 2 >
pcl::registration::PoseEstimate< PointT >PoseEstimate struct
pcl::PosesFromMatches::PoseEstimateA result of the pose estimation process
pcl::registration::PoseMeasurement< VertexT, InformationT >PoseMeasurement struct
pcl::PosesFromMatchesCalculate 3D transformation based on point correspondencdes
pcl::PPFRegistration< PointSource, PointTarget >::PoseWithVotesStructure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes
pcl::PPFEstimation< PointInT, PointNT, PointOutT >Class that calculates the "surflet" features for each pair in the given pointcloud. Please refer to the following publication for more details: B. Drost, M. Ulrich, N. Navab, S. Ilic Model Globally, Match Locally: Efficient and Robust 3D Object Recognition 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 13-18 June 2010, San Francisco, CA
pcl::PPFHashMapSearch
pcl::PPFRegistration< PointSource, PointTarget >Class that registers two point clouds based on their sets of PPFSignatures. Please refer to the following publication for more details: B. Drost, M. Ulrich, N. Navab, S. Ilic Model Globally, Match Locally: Efficient and Robust 3D Object Recognition 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 13-18 June 2010, San Francisco, CA
pcl::PPFRGBEstimation< PointInT, PointNT, PointOutT >
pcl::PPFRGBRegionEstimation< PointInT, PointNT, PointOutT >
pcl::PPFRGBSignatureA point structure for storing the Point Pair Color Feature (PPFRGB) values
pcl::PPFSignatureA point structure for storing the Point Pair Feature (PPF) values
pcl::poisson::PPolynomial< Degree >
testing::internal::PrettyUnitTestResultPrinter
pcl::PrincipalCurvaturesA point structure representing the principal curvatures and their magnitudes
pcl::PrincipalCurvaturesEstimation< PointInT, PointNT, PointOutT >PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals
pcl::PrincipalRadiiRSDA point structure representing the minimum and maximum surface radii (in meters) computed using RSD
mets::printableAn interface for printable objects
pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioBranchQueueEntryPriority queue entry for branch nodes
prioPointQueueEntry
pcl::octree::OctreePointCloudSearch< PointT, LeafContainerT, BranchContainerT >::prioPointQueueEntryPriority queue entry for point candidates
Producer< PointT >
pcl::ProgressiveSampleConsensus< PointT >RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Matching with PROSAC – Progressive Sample Consensus", Chum, O. and Matas, J.G., CVPR, I: 220-226 2005
pcl::ProjectInliers< PointT >ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
pcl::ProjectInliers< pcl::PCLPointCloud2 >ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
pcl::cloud_composer::ProjectModel
pcl::cloud_composer::PropertiesModel
pcl::io::ply::ply_parser::property
pcl::filters::Pyramid< PointT >
pcl::PyramidFeatureHistogram< PointFeature >Class that compares two sets of features by using a multiscale representation of the features inside a pyramid. Each level of the pyramid offers information about the similarity of the two feature sets
pcl::PyramidFeatureHistogram< PointFeature >::PyramidFeatureHistogramLevelStructure for representing a single pyramid histogram level
pcl::geometry::QuadMesh< MeshTraitsT >Half-edge mesh that can only store quads
pcl::geometry::QuadMeshTagTag describing the type of the mesh
pcl::QuantizableModalityInterface for a quantizable modality
pcl::QuantizedMap
pcl::QuantizedMultiModFeatureFeature that defines a position and quantized value in a specific modality
pcl::QuantizedNormalLookUpTableLook-up-table for fast surface normal quantization
pcl::apps::optronic_viewer::RadiusOutlierCFWrapper for a radius-outlier filter. Removes all points that have less than a specified number of points as neighbors (within a specified radius)
pcl::RadiusOutlierRemoval< PointT >RadiusOutlierRemoval filters points in a cloud based on the number of neighbors they have
pcl::RadiusOutlierRemoval< pcl::PCLPointCloud2 >RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K
testing::internal::Random
pcl::RandomizedMEstimatorSampleConsensus< PointT >RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus)
pcl::RandomizedRandomSampleConsensus< PointT >RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O. Chum and J. Matas, Proc. British Machine Vision Conf. (BMVC '02), vol. 2, BMVA, pp. 448-457, 2002
pcl::RandomSample< PointT >RandomSample applies a random sampling with uniform probability. Based off Algorithm A from the paper "Faster Methods for Random Sampling" by Jeffrey Scott Vitter. The algorithm runs in O(N) and results in sorted indices http://www.ittc.ku.edu/~jsv/Papers/Vit84.sampling.pdf
pcl::RandomSample< pcl::PCLPointCloud2 >RandomSample applies a random sampling with uniform probability
pcl::apps::optronic_viewer::RandomSampleCFWrapper for a random sample filter. Selects a random sample of points from the input cloud
pcl::RandomSampleConsensus< PointT >RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A. Fischler and Robert C. Bolles, Comm. Of the ACM 24: 381–395, June 1981
pcl::RangeImageRangeImage is derived from pcl/PointCloud and provides functionalities with focus on situations where a 3D scene was captured from a specific view point
pcl::RangeImageBorderExtractorExtract obstacle borders from range images, meaning positions where there is a transition from foreground to background
pcl::RangeImagePlanarRangeImagePlanar is derived from the original range image and differs from it because it's not a spherical projection, but using a projection plane (as normal cameras do), therefore being better applicable for range sensors that already provide a range image by themselves (stereo cameras, ToF-cameras), so that a conversion to point cloud and then to a spherical range image becomes unnecessary
pcl::RangeImageSphericalRangeImageSpherical is derived from the original range image and uses a slightly different spherical projection. In the original range image, the image will appear more and more "scaled down" along the y axis, the further away from the mean line of the image a point is. This class removes this scaling, which makes it especially suitable for spinning LIDAR sensors that capure a 360° view, since a rotation of the sensor will now simply correspond to a shift of the range image. (This class is similar to RangeImagePlanar, but changes less of the behaviour of the base class.)
pcl::visualization::RangeImageVisualizerRange image visualizer class
testing::internal::RE
pcl::GlobalHypothesesVerification< ModelT, SceneT >::RecognitionModel
pcl::PapazovHV< ModelT, SceneT >::RecognitionModel
pcl::GreedyVerification< ModelT, SceneT >::RecognitionModel
Recorder
pcl::visualization::context_items::Rectangle
pcl::cloud_composer::RectangularFrustumSelector
pcl::ReferenceFrame
pcl::poisson::Octree< Degree >::RefineFunction
pcl::Region3D< PointT >Region3D represents summary statistics of a 3D collection of points
pcl::RegionGrowing< PointT, NormalT >Implements the well known Region Growing algorithm used for segmentation. Description can be found in the article "Segmentation of point clouds using smoothness constraint" by T. Rabbania, F. A. van den Heuvelb, G. Vosselmanc. In addition to residual test, the possibility to test curvature is added
pcl::RegionGrowingRGB< PointT, NormalT >Implements the well known Region Growing algorithm used for segmentation based on color of points. Description can be found in the article "Color-based segmentation of point clouds" by Qingming Zhan, Yubin Liang, Yinghui Xiao
pcl::RegionXYDefines a region in XY-space
pcl::Registration< PointSource, PointTarget, Scalar >Registration represents the base registration class for general purpose, ICP-like methods
pcl::RegistrationVisualizer< PointSource, PointTarget >RegistrationVisualizer represents the base class for rendering the intermediate positions ocupied by the source point cloud during it's registration to the target point cloud. A registration algorithm is considered as input and it's covergence is rendered
RegistrationWrapper< PointSource, PointTarget >
testing::internal::RemoveConst< T >
testing::internal::RemoveConst< const T >
testing::internal::RemoveReference< T >
testing::internal::RemoveReference< T & >
pcl::apps::RenderViewsTesselatedSphereClass to render synthetic views of a 3D mesh using a tesselated sphere NOTE: This class should replace renderViewTesselatedSphere from pcl::visualization. Some extensions are planned in the near future to this class like removal of duplicated views for symmetrical objects, generation of RGB synthetic clouds when RGB available on mesh, etc
pcl::modeler::RenderWindow
pcl::modeler::RenderWindowItem
pcl::visualization::RenWinInteract
TemplateAlignment::Result
pcl::RGBA structure representing RGB color information
pcl::TexMaterial::RGB
pcl::RGBPlaneCoefficientComparator< PointT, PointNT >RGBPlaneCoefficientComparator is a Comparator that operates on plane coefficients, for use in planar segmentation. Also takes into account RGB, so we can segmented different colored co-planar regions. In conjunction with OrganizedConnectedComponentSegmentation, this allows planes to be segmented from organized data
pcl::tracking::RGBValue
pcl::RIFTEstimation< PointInT, GradientT, PointOutT >RIFTEstimation estimates the Rotation Invariant Feature Transform descriptors for a given point cloud dataset containing points and intensity. For more information about the RIFT descriptor, see:
pcl::recognition::RigidTransformSpace
pcl::RobotEyeGrabberGrabber for the Ocular Robotics RobotEye sensor
pcl::poisson::Octree< Degree >::RootData
pcl::poisson::RootInfo
pcl::recognition::RotationSpaceThis is a class for a discrete representation of the rotation space based on the axis-angle representation. This class is not supposed to be very general. That's why it is dependent on the class ModelLibrary
pcl::recognition::RotationSpaceCell
pcl::recognition::RotationSpaceCellCreator
pcl::recognition::RotationSpaceCreator
pcl::RSDEstimation< PointInT, PointNT, PointOutT >RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals
pcl::SACSegmentation< PointT >SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation
pcl::SACSegmentationFromNormals< PointT, PointNT >SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation
std::tr1::gtest_internal::SameSizeTuplePrefixComparator< 0, 0 >
std::tr1::gtest_internal::SameSizeTuplePrefixComparator< k, k >
pcl::GlobalHypothesesVerification< ModelT, SceneT >::SAModel
pcl::SampleConsensus< T >SampleConsensus represents the base class. All sample consensus methods must inherit from this class
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >SampleConsensusInitialAlignment is an implementation of the initial alignment algorithm described in section IV of "Fast Point Feature Histograms (FPFH) for 3D Registration," Rusu et al
pcl::SampleConsensusModel< PointT >SampleConsensusModel represents the base model class. All sample consensus models must inherit from this class
pcl::SampleConsensusModelCircle2D< PointT >SampleConsensusModelCircle2D defines a model for 2D circle segmentation on the X-Y plane
pcl::SampleConsensusModelCircle3D< PointT >SampleConsensusModelCircle3D defines a model for 3D circle segmentation
pcl::SampleConsensusModelCone< PointT, PointNT >SampleConsensusModelCone defines a model for 3D cone segmentation. The model coefficients are defined as:
pcl::SampleConsensusModelCylinder< PointT, PointNT >SampleConsensusModelCylinder defines a model for 3D cylinder segmentation. The model coefficients are defined as:
pcl::SampleConsensusModelFromNormals< PointT, PointNT >SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation
pcl::SampleConsensusModelLine< PointT >SampleConsensusModelLine defines a model for 3D line segmentation. The model coefficients are defined as:
pcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT >SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints. Basically this means that checking for inliers will not only involve a "distance to model" criterion, but also an additional "maximum angular deviation" between the plane's normal and the inlier points normals. In addition, the plane normal must lie parallel to an user-specified axis
pcl::SampleConsensusModelNormalPlane< PointT, PointNT >SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints. Basically this means that checking for inliers will not only involve a "distance to model" criterion, but also an additional "maximum angular deviation" between the plane's normal and the inlier points normals
pcl::SampleConsensusModelNormalSphere< PointT, PointNT >SampleConsensusModelNormalSphere defines a model for 3D sphere segmentation using additional surface normal constraints. Basically this means that checking for inliers will not only involve a "distance to model" criterion, but also an additional "maximum angular deviation" between the sphere's normal and the inlier points normals
pcl::SampleConsensusModelParallelLine< PointT >SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints. The model coefficients are defined as:
pcl::SampleConsensusModelParallelPlane< PointT >SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints. The plane must be parallel to a user-specified axis (setAxis) within an user-specified angle threshold (setEpsAngle)
pcl::SampleConsensusModelPerpendicularPlane< PointT >SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints. The plane must be perpendicular to an user-specified axis (setAxis), up to an user-specified angle threshold (setEpsAngle). The model coefficients are defined as:
pcl::SampleConsensusModelPlane< PointT >SampleConsensusModelPlane defines a model for 3D plane segmentation. The model coefficients are defined as:
pcl::SampleConsensusModelRegistration< PointT >SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection
pcl::SampleConsensusModelRegistration2D< PointT >SampleConsensusModelRegistration2D defines a model for Point-To-Point registration outlier rejection using distances between 2D pixels
pcl::SampleConsensusModelSphere< PointT >SampleConsensusModelSphere defines a model for 3D sphere segmentation. The model coefficients are defined as:
pcl::SampleConsensusModelStick< PointT >SampleConsensusModelStick defines a model for 3D stick segmentation. A stick is a line with an user given minimum/maximum width. The model coefficients are defined as:
pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >Pose estimation and alignment class using a prerejective RANSAC routine
pcl::SamplingSurfaceNormal< PointT >SamplingSurfaceNormal divides the input space into grids until each grid contains a maximum of N points, and samples points randomly within each grid. Normal is computed using the N points of each grid. All points sampled within a grid are assigned the same normal
pcl::cloud_composer::SanitizeCloudTool
pcl::cloud_composer::SanitizeCloudToolFactory
pcl::io::ply::ply_parser::scalar_property< ScalarType >
pcl::io::ply::ply_parser::scalar_property_callback_type< ScalarType >
pcl::io::ply::ply_parser::scalar_property_definition_callback_type< ScalarType >
pcl::io::ply::ply_parser::scalar_property_definition_callbacks_type
ScanParameters
Scene
pcl::modeler::SceneTree
testing::internal::scoped_ptr< T >
testing::ScopedFakeTestPartResultReporter
testing::internal::ScopedTrace
pcl::ScopeTimeClass to measure the time spent in a scope
pcl::keypoints::agast::AbstractAgastDetector::ScoreIndexStructure holding an index and the associated keypoint score
pcl::search::Search< PointT >Generic search class. All search wrappers must inherit from this
mets::search_listener< move_manager_type >An object that is called back during the search progress
pcl::SeededHueSegmentationSeededHueSegmentation
pcl::SegmentDifferences< PointT >SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold
Select1DTool
Select2DTool
pcl::cloud_composer::SelectedTrackballStyleInteractor
SelectionThis class serves as a sort of mask for performing operations on a point cloud. It keeps track of the indices of identified/selected points and provides methods for accessing those indices and modifying them
pcl::cloud_composer::SelectionEvent
SelectionTransformToolThe selection transform tool computes the transform matrix from mouse input. It then updates the cloud's transform matrix for the selected points so that the transformed and selected points will be rendered appropriately. Note that, the actual coordinates of the selected points are not updated until the end of the mouse input. At the end of a mouse input (i.e. when the mouse button is released), a transform command is created to update the actual coordinates of the selected points
mets::sequenceA sequence function object useful as an STL generator
pcl::io::ply::ply_parser::list_property_definition_callbacks_type::sequence_product< Sequence1, Sequence2 >
pcl::on_nurbs::SequentialFitter
pcl::recognition::ORROctreeZProjection::Set
pcl::SetIfFieldExists< PointOutT, InT >A helper functor that can set a specific value in a field if the field exists
testing::Environment::Setup_should_be_spelled_SetUp
testing::Test::Setup_should_be_spelled_SetUp
pcl::RangeImageBorderExtractor::ShadowBorderIndicesStores the indices of the shadow border corresponding to obstacle borders
pcl::ShadowPoints< PointT, NormalT >ShadowPoints removes the ghost points appearing on edge discontinuties
pcl::ShapeContext1980A point structure representing a Shape Context
pcl::ShapeContext3DEstimation< PointInT, PointNT, PointOutT >ShapeContext3DEstimation implements the 3D shape context descriptor as described in:
pcl::SHOT1344A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape+color
pcl::SHOT352A point structure representing the generic Signature of Histograms of OrienTations (SHOT) - shape only
pcl::SHOTColorEstimation< PointInT, PointNT, PointOutT, PointRFT >SHOTColorEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors
pcl::SHOTColorEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >SHOTColorEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points, normals and colors, in parallel, using the OpenMP standard
pcl::SHOTEstimation< PointInT, PointNT, PointOutT, PointRFT >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimationBase< PointInT, PointNT, PointOutT, PointRFT >SHOTEstimation estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals
pcl::SHOTEstimationOMP< PointInT, PointNT, PointOutT, PointRFT >SHOTEstimationOMP estimates the Signature of Histograms of OrienTations (SHOT) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard
pcl::SHOTLocalReferenceFrameEstimation< PointInT, PointOutT >SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor
pcl::SHOTLocalReferenceFrameEstimationOMP< PointInT, PointOutT >SHOTLocalReferenceFrameEstimation estimates the Local Reference Frame used in the calculation of the (SHOT) descriptor
pcl::SIFTKeypoint< PointInT, PointOutT >SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset containing points and intensity. This implementation adapts the original algorithm from images to point clouds
pcl::SIFTKeypointFieldSelector< PointT >
pcl::SIFTKeypointFieldSelector< PointNormal >
pcl::SIFTKeypointFieldSelector< PointXYZ >
pcl::SIFTKeypointFieldSelector< PointXYZRGB >
pcl::SIFTKeypointFieldSelector< PointXYZRGBA >
pcl::cloud_composer::SignalMultiplexer
mets::simple_tabu_listSimplistic implementation of a tabu-list
SimpleHDLGrabber
SimpleHDLViewer< PointType >
pcl::recognition::SimpleOctree< NodeData, NodeDataCreator, Scalar >
SimpleONIViewer< PointType >
SimpleOpenNIProcessor
SimpleOpenNIViewer< PointType >
pcl::surface::SimplificationRemoveUnusedVertices
mets::simulated_annealing< move_manager_type >Search by Simulated Annealing
testing::internal::SingleFailureChecker
pcl::SmoothedSurfacesKeypoint< PointT, PointNT >Based on the paper: Xinju Li and Igor Guskov Multi-scale features for approximate alignment of point-based surfaces Proceedings of the third Eurographics symposium on Geometry processing July 2005, Vienna, Austria
ON_SerialNumberMap::SN_BLOCK
ON_SerialNumberMap::SN_ELEMENT
mets::solution_recorderThe solution recorder is used by search algorithm, at the end of each iteration, to record the best seen solution
pcl::SolverDidntConvergeExceptionAn exception that is thrown when the non linear solver didn't converge
pcl::registration::sortCorrespondencesByDistance
pcl::registration::sortCorrespondencesByMatchIndex
pcl::registration::sortCorrespondencesByMatchIndexAndDistance
pcl::registration::sortCorrespondencesByQueryIndex
pcl::registration::sortCorrespondencesByQueryIndexAndDistance
pcl::poisson::SortedTreeNodes
pcl::GreedyVerification< ModelT, SceneT >::sortModelIndicesClass
pcl::GreedyVerification< ModelT, SceneT >::sortModelsClass
pcl::on_nurbs::SparseMatSparse matrix implementation
pcl::poisson::SparseMatrix< T >
pcl::SparseQuantizedMultiModTemplateA multi-modality template constructed from a set of quantized multi-modality features
pcl::poisson::SparseSymmetricMatrix< T >
pcl::SpinImageEstimation< PointInT, PointNT, PointOutT >Estimates spin-image descriptors in the given input points
pcl::cloud_composer::SplitCloudCommand
pcl::cloud_composer::SplitItemTool
pcl::poisson::Square
ON_RTreeIterator::StackElement
pcl::poisson::StartingPolynomial< Degree >
static_tree_desc_s
testing::internal::StaticAssertTypeEqHelper< T, T >
pcl::StaticRangeCoderStaticRangeCoder compression class
pcl::StatisticalMultiscaleInterestRegionExtraction< PointT >Class for extracting interest regions from unstructured point clouds, based on a multi scale statistical approach. Please refer to the following publications for more details: Ranjith Unnikrishnan and Martial Hebert Multi-Scale Interest Regions from Unorganized Point Clouds Workshop on Search in 3D (S3D), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) June, 2008
pcl::StatisticalOutlierRemoval< PointT >StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data
pcl::StatisticalOutlierRemoval< pcl::PCLPointCloud2 >StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check:
pcl::cloud_composer::StatisticalOutlierRemovalTool
pcl::cloud_composer::StatisticalOutlierRemovalToolFactory
pcl::modeler::StatisticalOutlierRemovalWorker
Statistics
StatisticsDialog
pcl::poisson::Octree< Degree >::Stencil< C, N >
pcl::StopWatchSimple stopwatch
testing::internal::String
mets::subject< observed_subject >Template class for subjects (cfr. Observer Design Pattern)
pcl::Supervoxel< PointT >Supervoxel container class - stores a cluster extracted using supervoxel clustering
pcl::SupervoxelClustering< PointT >Implements a supervoxel algorithm based on voxel structure, normals, and rgb values
pcl::SupervoxelClustering< PointT >::SupervoxelHelperInternal storage class for supervoxels
pcl::cloud_composer::SupervoxelsTool
pcl::cloud_composer::SupervoxelsToolFactory
pcl::modeler::SurfaceActorItem
pcl::SurfaceNormalModality< PointInT >Modality based on surface normals
pcl::SurfaceReconstruction< PointInT >SurfaceReconstruction represents a base surface reconstruction class. All surface reconstruction methods take in a point cloud and generate a new surface from it, by either re-sampling the data or generating new data altogether. These methods are thus not preserving the topology of the original data
pcl::SurfelSmoothing< PointT, PointNT >
SUSANDemo
pcl::SUSANKeypoint< PointInT, PointOutT, NormalT, IntensityT >SUSANKeypoint implements a RGB-D extension of the SUSAN detector inluding normal directions variation in top of intensity variation. It is different from Harris in that it exploits normals directly so it is faster. Original paper "SUSAN — A New Approach to Low Level Image Processing", Smith, Stephen M. and Brady, J. Michael
mets::swap_elementsA mets::mana_move that swaps two elements in a mets::permutation_problem
mets::swap_full_neighborhoodGenerates a the full swap neighborhood
mets::swap_neighborhood< random_generator >Generates a stochastic subset of the neighborhood
pcl::SynchronizedQueue< T >
pcl::Synchronizer< T1, T2 >
mets::tabu_list_chainAn abstract tabu list
mets::tabu_search< move_manager_type >Tabu Search algorithm
tagFPT
tagMESHPOINTS
tagON_2dex
tagON_3dex
tagON_4dex
tagON_RECT
tagON_SORT_CONTEXT
pcl::io::TARHeaderA TAR file's header, as described on http://en.wikipedia.org/wiki/Tar_%28file_format%29
pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::TCThis structure is used for determining the end of the k-means clustering process
TemplateAlignment
mets::termination_criteria_chainFunction object expressing a termination criteria
testing::Test
testing::TestCase
testing::internal::TestCaseNameIs
testing::TestEventListener
testing::TestEventListeners
testing::internal::TestEventRepeater
testing::internal::TestFactoryBase
testing::internal::TestFactoryImpl< TestClass >
TestGetBoundary< MeshT >
testing::TestInfo
TestMeshCirculators
TestMeshConversion< MeshTraitsT >
TestMeshIndicesTyped< MeshIndexT >
testing::TestPartResult
testing::TestPartResultArray
testing::TestPartResultReporterInterface
TestPolygonMesh< MeshT >
testing::TestProperty
testing::internal::TestPropertyKeyIs
TestQuadMesh< MeshT >
testing::TestResult
testing::internal::TestResultAccessor
TestTriangleMesh< MeshT >
pcl::TexMaterial
pcl::TextureMapping< PointInT >The texture mapping algorithm
pcl::TextureMesh
pcl::TfQuadraticXYZComparison< PointT >A comparison whether the (x,y,z) components of a given point satisfy (p'Ap + 2v'p + c [OP] 0). Here [OP] stands for the defined pcl::ComparisonOps, i.e. for GT, GE, LT, LE or EQ; p = (x,y,z) is a point of the point cloud; A is 3x3 matrix; v is the 3x1 vector; c is a scalar
pcl::modeler::ThreadController
testing::internal::ThreadLocal< T >
mets::threshold_termination_criteriaTermination criteria based on cost value
pcl::console::TicToc
pcl::TimeTriggerTimer class that invokes registered callback methods periodically
pcl::cloud_composer::ToolBoxModel
pcl::cloud_composer::ToolFactory
ToolInterfaceParent class of all the select and the transform tool classes
testing::internal::TraceInfo
TrackBall
pcl::tracking::Tracker< PointInT, StateT >Tracker represents the base tracker class
pcl::registration::TransformationEstimation< PointSource, PointTarget, Scalar >TransformationEstimation represents the base class for methods for transformation estimation based on:
pcl::registration::TransformationEstimation2D< PointSource, PointTarget, Scalar >
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, MatScalar >
pcl::registration::TransformationEstimationPointToPlane< PointSource, PointTarget, Scalar >
pcl::registration::TransformationEstimationPointToPlaneLLS< PointSource, PointTarget, Scalar >TransformationEstimationPointToPlaneLLS implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals
pcl::registration::TransformationEstimationPointToPlaneLLSWeighted< PointSource, PointTarget, Scalar >TransformationEstimationPointToPlaneLLSWeighted implements a Linear Least Squares (LLS) approximation for minimizing the point-to-plane distance between two clouds of corresponding points with normals, with the possibility of assigning weights to the correspondences
pcl::registration::TransformationEstimationPointToPlaneWeighted< PointSource, PointTarget, MatScalar >
pcl::registration::TransformationEstimationSVD< PointSource, PointTarget, Scalar >
pcl::registration::TransformationEstimationSVDScale< PointSource, PointTarget, Scalar >
pcl::TransformationFromCorrespondencesCalculates a transformation based on corresponding 3D points
pcl::registration::TransformationValidation< PointSource, PointTarget, Scalar >TransformationValidation represents the base class for methods that validate the correctness of a transformation found through TransformationEstimation
pcl::registration::TransformationValidationEuclidean< PointSource, PointTarget, Scalar >TransformationValidationEuclidean computes an L2SQR norm between a source and target dataset
pcl::cloud_composer::TransformClouds
TransformCommand
tree_desc_s
pcl::poisson::TreeNodeData
pcl::ihs::detail::FaceVertexMesh::Triangle
pcl::poisson::Triangle
pcl::poisson::TriangleIndex
pcl::geometry::TriangleMesh< MeshTraitsT >Half-edge mesh that can only store triangles
pcl::geometry::TriangleMeshTagTag describing the type of the mesh
pcl::poisson::Triangulation< Real >
pcl::on_nurbs::TriangulationFunctions for NURBS surface triangulation, trimming and curve sampling
pcl::poisson::TriangulationEdge
pcl::poisson::TriangulationTriangle
pcl::recognition::TrimmedICP< PointT, Scalar >
pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >::TruncatedError
std::tr1::tuple<>
std::tr1::tuple<>
std::tr1::tuple_element< k, Tuple >
std::tr1::tuple_size< GTEST_0_TUPLE_(T)>
std::tr1::tuple_size< GTEST_10_TUPLE_(T)>
std::tr1::tuple_size< GTEST_1_TUPLE_(T)>
std::tr1::tuple_size< GTEST_2_TUPLE_(T)>
std::tr1::tuple_size< GTEST_3_TUPLE_(T)>
std::tr1::tuple_size< GTEST_4_TUPLE_(T)>
std::tr1::tuple_size< GTEST_5_TUPLE_(T)>
std::tr1::tuple_size< GTEST_6_TUPLE_(T)>
std::tr1::tuple_size< GTEST_7_TUPLE_(T)>
std::tr1::tuple_size< GTEST_8_TUPLE_(T)>
std::tr1::tuple_size< GTEST_9_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 0, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 1, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 2, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 3, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 4, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 5, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 6, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 7, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 8, GTEST_10_TUPLE_(T)>
std::tr1::gtest_internal::TupleElement< true, 9, GTEST_10_TUPLE_(T)>
testing::internal::TypeIdHelper< T >
testing::internal2::TypeWithoutFormatter< T, kTypeKind >
testing::internal2::TypeWithoutFormatter< T, kConvertibleToInteger >
testing::internal2::TypeWithoutFormatter< T, kProtobuf >
testing::internal::TypeWithSize< size >
testing::internal::TypeWithSize< 4 >
testing::internal::TypeWithSize< 8 >
pcl::UnhandledPointTypeException
pcl::common::uniform_distribution< float >Uniform distribution float specialized
pcl::common::uniform_distribution< int >Uniform distribution int specialized
pcl::common::UniformGenerator< T >UniformGenerator class generates a random number from range [min, max] at each run picked according to a uniform distribution i.e eaach number within [min, max] has almost the same probability of being drawn
pcl::UniformSampling< PointInT >UniformSampling assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::UniqueShapeContext< PointInT, PointOutT, PointRFT >UniqueShapeContext implements the Unique Shape Context Descriptor described here:
testing::UnitTest
testing::internal::UnitTestImpl
testing::internal::UnitTestOptions
testing::internal::UniversalPrinter< T >
testing::internal::UniversalPrinter< T & >
testing::internal::UniversalPrinter< T[N]>
pcl::UnorganizedPointCloudExceptionAn exception that is thrown when an organized point cloud is needed but not provided
mets::update_observer< observed_subject >Functor class to update observers with a for_each, only intended for internal use
pcl::poisson::UpSampleData
pcl::texture_mapping::UvIndexStructure that links a uv coordinate to its 3D point and face
pcl::ndt2d::ValueAndDerivatives< N, T >Class to store vector value and first and second derivatives (grad vector and hessian matrix), so they can be returned easily from functions
pcl::poisson::Vector< T >
pcl::VectorAverage< real, dimension >Calculates the weighted average and the covariance matrix
pcl::registration::ELCH< PointT >::Vertex
pcl::poisson::CoredMeshData2::Vertex
pcl::geometry::VertexA vertex is a node in the mesh
pcl::geometry::VertexAroundFaceCirculator< MeshT >Circulates clockwise around a face and returns an index to the terminating vertex of the inner half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getVertexAroundFaceCirculator ()
pcl::geometry::VertexAroundVertexCirculator< MeshT >Circulates counter-clockwise around a vertex and returns an index to the terminating vertex of the outgoing half-edge (the target). The best way to declare the circulator is to use the method pcl::geometry::MeshBase::getVertexAroundVertexCirculator ()
pcl::poisson::VertexData
pcl::geometry::VertexIndexIndex used to access elements in the half-edge mesh. It is basically just a wrapper around an integer with a few added methods
pcl::registration::LUM< PointT >::VertexProperties
pcl::VerticesDescribes a set of vertices in a polygon mesh, by basically storing an array of indices
pcl::VFHClassifierNNUtility class for nearest neighbor search based classification of VFH features
pcl::VFHEstimation< PointInT, PointNT, PointOutT >VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals. The default VFH implementation uses 45 binning subdivisions for each of the three extended FPFH values, plus another 45 binning subdivisions for the distances between each point and the centroid and 128 binning subdivisions for the viewpoint component, which results in a 308-byte array of float values. These are stored in a pcl::VFHSignature308 point type. A major difference between the PFH/FPFH descriptors and VFH, is that for a given point cloud dataset, only a single VFH descriptor will be estimated (vfhs->points.size() should be 1), while the resultant PFH/FPFH data will have the same number of entries as the number of points in the cloud
pcl::VFHSignature308A point structure representing the Viewpoint Feature Histogram (VFH)
Viewer
Viewport
pcl::ihs::InHandScanner::VisualizationFPSHelper object for the visualization thread. Please have a look at the documentation of calcFPS
pcl::ihs::OfflineIntegration::VisualizationFPSHelper object for the visualization thread. Please have a look at the documentation of calcFPS
pcl::ism::ImplicitShapeModelEstimation< FeatureSize, PointT, NormalT >::VisualWordStatStructure for storing the visual word
pcl::SupervoxelClustering< PointT >::VoxelDataVoxelData is a structure used for storing data within a pcl::octree::OctreePointCloudAdjacencyContainer
pcl::VoxelGrid< PointT >VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::VoxelGrid< pcl::PCLPointCloud2 >VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
pcl::apps::optronic_viewer::VoxelGridCFWrapper for a voxel grid filter. Divides the space in voxels and takes a point per voxel
pcl::VoxelGridCovariance< PointT >A searchable voxel strucure containing the mean and covariance of the data
pcl::modeler::VoxelGridDownampleWorker
pcl::cloud_composer::VoxelGridDownsampleTool
pcl::cloud_composer::VoxelGridDownsampleToolFactory
pcl::VoxelGridLabel
pcl::VoxelGridOcclusionEstimation< PointT >VoxelGrid to estimate occluded space in the scene. The ray traversal algorithm is implemented by the work of 'John Amanatides and Andrew Woo, A Fast Voxel Traversal Algorithm for Ray Tracing'
pcl::recognition::VoxelStructure< T, REAL >This class is a box in R3 built of voxels ordered in a regular rectangular grid. Each voxel is of type T
pcl::cloud_composer::VPtr< T >Templated helper class for converting QVariant to/from pointer classes
pcl::VTKUtils
vtkVertexBufferObject
vtkVertexBufferObjectMapper
pcl::registration::WarpPointRigid< PointSourceT, PointTargetT, Scalar >Base warp point class
pcl::registration::WarpPointRigid3D< PointSourceT, PointTargetT, Scalar >WarpPointRigid3D enables 3D (1D rotation + 2D translation) transformations for points
pcl::registration::WarpPointRigid6D< PointSourceT, PointTargetT, Scalar >WarpPointRigid3D enables 6D (3D rotation + 3D translation) transformations for points
pcl::visualization::Window
pcl::cloud_composer::WorkQueue
Writer
testing::internal::XmlUnitTestResultPrinter
pcl::xNdCopyEigenPointFunctor< PointT >Helper functor structure for copying data between an Eigen::VectorXf and a PointT
pcl::xNdCopyPointEigenFunctor< PointT >Helper functor structure for copying data between an Eigen::VectorXf and a PointT
z_stream_s
pcl::occlusion_reasoning::ZBuffering< ModelT, SceneT >Class to reason about occlusions


pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:47:38