| pcl::_PointWithViewpoint | |
| pcl::_PointXYZ | |
| pcl::traits::asEnum< T > | |
| pcl::traits::asEnum< double > | |
| pcl::traits::asEnum< float > | |
| pcl::traits::asEnum< int16_t > | |
| pcl::traits::asEnum< int32_t > | |
| pcl::traits::asEnum< int8_t > | |
| pcl::traits::asEnum< uint16_t > | |
| pcl::traits::asEnum< uint32_t > | |
| pcl::traits::asEnum< uint8_t > | |
| pcl::BivariatePolynomialT< real > | This represents a bivariate polynomial and provides some functionality for it |
| pcl::BorderDescription | |
| pcl::Boundary | |
| 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 |
| sensor_msgs::CameraInfo_< ContainerAllocator > | |
| pcl::ComparisonBase< PointT > | The (abstract) base class for the comparison object |
| pcl::ConcaveHull< PointInT > | ConcaveHull (alpha shapes) using libqhull library |
| pcl::ConditionalRemoval< PointT > | ConditionalRemoval filters data that satisfies certain conditions |
| pcl::ConditionAnd< PointT > | AND condition |
| pcl::ConditionBase< PointT > | Base condition class |
| pcl::ConditionOr< PointT > | OR condition |
| pcl::ConvexHull< PointInT > | ConvexHull using libqhull library |
| pcl::registration::Correspondence | Class representing a match between two descriptors |
| pcl::registration::CorrespondenceEstimation< PointSource, PointTarget > | |
| pcl::registration::CorrespondenceRejector | |
| pcl::registration::CorrespondenceRejectorDistance | |
| pcl::registration::CorrespondenceRejectorOneToOne | |
| pcl::registration::CorrespondenceRejectorReciprocal | |
| pcl::registration::CorrespondenceRejectorSampleConsensus< PointT > | |
| pcl::registration::CorrespondenceRejectorTrimmed | |
| pcl::traits::datatype< PointT, Tag > | |
| ros::message_traits::DataType< ::pcl::ModelCoefficients_< ContainerAllocator > > | |
| ros::message_traits::DataType< ::pcl::PointIndices_< ContainerAllocator > > | |
| ros::message_traits::DataType< ::pcl::PolygonMesh_< ContainerAllocator > > | |
| ros::message_traits::DataType< ::pcl::Vertices_< ContainerAllocator > > | |
| pcl::traits::decomposeArray< T > | |
| pcl::DefaultPointRepresentation< PointDefault > | DefaultPointRepresentation extends PointRepresentation to define default behavior for common point types |
| pcl::DefaultPointRepresentation< FPFHSignature33 > | |
| pcl::DefaultPointRepresentation< PFHSignature125 > | |
| pcl::DefaultPointRepresentation< PointNormal > | |
| pcl::DefaultPointRepresentation< PointXYZ > | |
| pcl::DefaultPointRepresentation< PointXYZI > | |
| ros::message_traits::Definition< ::pcl::ModelCoefficients_< ContainerAllocator > > | |
| ros::message_traits::Definition< ::pcl::PointIndices_< ContainerAllocator > > | |
| ros::message_traits::Definition< ::pcl::PolygonMesh_< ContainerAllocator > > | |
| ros::message_traits::Definition< ::pcl::Vertices_< ContainerAllocator > > | |
| pcl::GreedyProjectionTriangulation< PointInT >::doubleEdge | Struct for storing the edges starting from a fringe point |
| pcl::EuclideanClusterExtraction< PointT > | EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense |
| pcl::ExtractIndices< PointT > | ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud |
| pcl::ExtractIndices< sensor_msgs::PointCloud2 > | ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud |
| pcl::ExtractPolygonalPrismData< PointT > | ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism. The polygonal prism is then used to segment all points lying inside it |
| 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 |
| pcl::registration::CorrespondenceEstimation< PointSource, PointTarget >::FeatureContainer< FeatureType > | An inner class containing pointers to the source and target feature clouds along with the KdTree 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 FeatureType --- these methods can thus be called from a pointer to FeatureContainerInterface without casting to the derived class |
| pcl::Registration< PointSource, PointTarget >::FeatureContainer< FeatureType > | An inner class containing pointers to the source and target feature clouds along with the KdTree 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 FeatureType --- these methods can thus be called from a pointer to FeatureContainerInterface without casting to the derived class |
| pcl::registration::CorrespondenceEstimation< PointSource, PointTarget >::FeatureContainerInterface | |
| pcl::Registration< PointSource, PointTarget >::FeatureContainerInterface | |
| pcl::FeatureFromNormals< PointInT, PointNT, PointOutT > | |
| pcl::Narf::FeaturePointRepresentation | |
| pcl::detail::FieldAdder< PointT > | |
| pcl::FieldComparison< PointT > | The field-based specialization of the comparison object |
| pcl::traits::fieldList< PointT > | |
| pcl::detail::FieldMapper< PointT > | |
| pcl::detail::FieldMapping | |
| pcl::Filter< PointT > | Filter represents the base filter class. Some generic 3D operations that are applicable to all filters are defined here as static methods |
| pcl::Filter< sensor_msgs::PointCloud2 > | Filter represents the base filter class. Some generic 3D operations that are applicable to all filters are defined here as static methods |
| pcl::for_each_type_impl< done > | |
| pcl::for_each_type_impl< false > | |
| pcl::FPFHEstimation< PointInT, PointNT, PointOutT > | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |
| pcl::FPFHEstimationOMP< PointInT, PointNT, PointOutT > | FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
| pcl::FPFHSignature33 | |
| GeneralPoint | |
| 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::GridProjection< PointNT > | Grid projection surface reconstruction method |
| ros::message_traits::HasHeader< ::pcl::ModelCoefficients_< ContainerAllocator > > | |
| ros::message_traits::HasHeader< ::pcl::PointIndices_< ContainerAllocator > > | |
| ros::message_traits::HasHeader< ::pcl::PolygonMesh_< ContainerAllocator > > | |
| ros::message_traits::HasHeader< const ::pcl::ModelCoefficients_< ContainerAllocator > > | |
| ros::message_traits::HasHeader< const ::pcl::PointIndices_< ContainerAllocator > > | |
| ros::message_traits::HasHeader< const ::pcl::PolygonMesh_< ContainerAllocator > > | |
| roslib::Header_< ContainerAllocator > | |
| pcl::Histogram< N > | |
| sensor_msgs::Image_< ContainerAllocator > | |
| pcl::IntegralImage2D< DataType, IIDataType > | Generic implementation for creating 2D integral images (including second order integral images) |
| pcl::IntegralImageNormalEstimation | Surface normal estimation on dense data using integral images |
| pcl::IntensityGradient | |
| pcl::IntensityGradientEstimation< PointInT, PointNT, PointOutT > | 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::InterestPoint | |
| pcl::InvalidConversionException | An exception that is thrown when a PointCloud2 message cannot be converted into a PCL type |
| pcl::PosesFromMatches::PoseEstimate::IsBetter | |
| pcl::IsNotDenseException | An exception that is thrown when a PointCloud is not dense but is attemped to be used as dense |
| pcl::IterativeClosestPoint< PointSource, PointTarget > | IterativeClosestPoint is an implementation of the Iterative Closest Point algorithm based on Singular Value Decomposition (SVD) |
| pcl::IterativeClosestPointNonLinear< PointSource, PointTarget > | IterativeClosestPointNonLinear is an ICP variant that uses Levenberg-Marquardt optimization backend. The resultant transformation is optimized as a quaternion |
| pcl::KdTree< PointT > | KdTree represents the base spatial locator class for nearest neighbor estimation. All types of spatial locators should inherit from KdTree |
| pcl::KdTreeFLANN< PointT > | 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::Keypoint< PointInT, PointOutT > | Keypoint represents the base class for key points |
| KeypointT | |
| pcl::VoxelGrid< PointT >::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |
| pcl::VoxelGrid< sensor_msgs::PointCloud2 >::Leaf | Simple structure to hold an nD centroid and the number of points in a leaf |
| pcl::GridProjection< PointNT >::Leaf | Data 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::RangeImageBorderExtractor::LocalSurface | Stores some information extracted from the neighborhood of a point |
| 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 |
| ros::message_traits::MD5Sum< ::pcl::ModelCoefficients_< ContainerAllocator > > | |
| ros::message_traits::MD5Sum< ::pcl::PointIndices_< ContainerAllocator > > | |
| ros::message_traits::MD5Sum< ::pcl::PolygonMesh_< ContainerAllocator > > | |
| ros::message_traits::MD5Sum< ::pcl::Vertices_< ContainerAllocator > > | |
| 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::msg::_ModelCoefficients::ModelCoefficients | |
| pcl::ModelCoefficients_< ContainerAllocator > | |
| pcl::MomentInvariants | |
| pcl::MomentInvariantsEstimation< PointInT, PointOutT > | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
| pcl::MovingLeastSquares< PointInT, NormalOutT > | MovingLeastSquares represent an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation |
| MyPoint | |
| MyPointRepresenationXY | |
| MyPointXYZ | |
| pcl::traits::name< PointT, Tag, dummy > | |
| pcl::Narf | NARF (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 |
| pcl::Narf36 | |
| pcl::NarfDescriptor | |
| pcl::NarfKeypoint | NARF (Normal Aligned Radial Feature) keypoints. Input is a range image, output the indices of the keypoints |
| pcl::NdCentroidFunctor< PointT > | Helper functor structure for n-D centroid estimation |
| pcl::NdConcatenateFunctor< PointInT, PointOutT > | Helper functor structure for concatenate |
| pcl::NdCopyEigenPointFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
| pcl::NdCopyPointEigenFunctor< PointT > | Helper functor structure for copying data between an Eigen::VectorXf and a PointT |
| pcl::GreedyProjectionTriangulation< PointInT >::nnAngle | Struct for storing the angles to nearest neighbors |
| pcl::Normal | |
| pcl::NormalEstimation< PointInT, PointOutT > | NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures |
| pcl::NormalEstimationOMP< PointInT, PointOutT > | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |
| pcl::NormalEstimationTBB< PointInT, PointOutT > | NormalEstimationTBB estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using Intel's Threading Building Blocks library |
| pcl::traits::offset< PointT, Tag > | |
| pcl::OrganizedDataIndex< PointT > | OrganizedDataIndex is a type of spatial locator used to query organized datasets, such as point clouds acquired using dense stereo devices. The class best supports square data blocks for now in the form of (k*k+1)^2 |
| pcl::PackedHSIComparison< PointT > | A packed HSI specialization of the comparison object |
| pcl::PackedRGBComparison< PointT > | A packed rgb specialization of the comparison object |
| pcl::NarfKeypoint::Parameters | Parameters used in this class |
| pcl::NarfDescriptor::Parameters | |
| pcl::PosesFromMatches::Parameters | Parameters used in this class |
| pcl::RangeImageBorderExtractor::Parameters | Parameters used in this class |
| pcl::PolynomialCalculationsT< real >::Parameters | Parameters used in this class |
| pcl::PassThrough< PointT > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |
| pcl::PassThrough< sensor_msgs::PointCloud2 > | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |
| pcl::PCDReader | Point Cloud Data (PCD) file format reader |
| pcl::PCDWriter | Point Cloud Data (PCD) file format writer |
| pcl::PCLBase< PointT > | PCL base class. Implements methods that are used by all PCL objects |
| pcl::PCLBase< sensor_msgs::PointCloud2 > | |
| pcl::PCLException | A base class for all pcl exceptions which inherits from std::runtime_error |
| pcl::PFHEstimation< PointInT, PointNT, PointOutT > | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |
| pcl::PFHSignature125 | |
| pcl::PiecewiseLinearFunction | This provides functionalities to efficiently return values for piecewise linear function |
| pcl::traits::POD< PointT > | |
| pcl::PointCloud< PointT > | |
| sensor_msgs::PointCloud2_< ContainerAllocator > | |
| pcl::PointCorrespondence | Representation of a (possible) correspondence between two points in two different coordinate frames (e.g. from feature matching) |
| pcl::PointCorrespondence3D | Representation of a (possible) correspondence between two 3D points in two different coordinate frames (e.g. from feature matching) |
| pcl::PointCorrespondence6D | Representation of a (possible) correspondence between two points (e.g. from feature matching), that encode complete 6DOF transoformations |
| pcl::PointDataAtOffset< PointT > | A datatype that enables type-correct comparisons |
| sensor_msgs::PointField_< ContainerAllocator > | |
| pcl::msg::_PointIndices::PointIndices | |
| pcl::PointIndices_< ContainerAllocator > | |
| pcl::PointNormal | |
| pcl::PointRepresentation< PointT > | PointRepresentation provides a set of methods for converting a point structs/object into an n-dimensional vector |
| pcl::PointSurfel | |
| pcl::PointWithRange | |
| pcl::PointWithScale | |
| pcl::PointWithViewpoint | |
| pcl::PointXY | |
| pcl::PointXYZ | |
| PointXYZFPFH33 | |
| pcl::PointXYZI | |
| pcl::PointXYZINormal | |
| pcl::PointXYZRGB | |
| pcl::PointXYZRGBA | |
| pcl::PointXYZRGBNormal | |
| pcl::msg::_PolygonMesh::PolygonMesh | |
| pcl::PolygonMesh_< ContainerAllocator > | |
| pcl::PolynomialCalculationsT< real > | This provides some functionality for polynomials, like finding roots or approximating bivariate polynomials |
| pcl::PosesFromMatches::PoseEstimate | A result of the pose estimation process |
| pcl::PosesFromMatches | Calculate 3D transformation based on point correspondencdes |
| pcl::PrincipalCurvatures | |
| 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::PrincipalRadiiRSD | |
| ros::message_operations::Printer< ::pcl::ModelCoefficients_< ContainerAllocator > > | |
| ros::message_operations::Printer< ::pcl::PointIndices_< ContainerAllocator > > | |
| ros::message_operations::Printer< ::pcl::PolygonMesh_< ContainerAllocator > > | |
| ros::message_operations::Printer< ::pcl::Vertices_< ContainerAllocator > > | |
| 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< sensor_msgs::PointCloud2 > | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |
| pcl::RadiusOutlierRemoval< PointT > | RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K |
| pcl::RadiusOutlierRemoval< sensor_msgs::PointCloud2 > | RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K |
| pcl::RandomizedMEstimatorSampleConsensus< PointT > | RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus) |
| pcl::RandomizedRandomSampleConsensus< PointT > | RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RAndom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O. Chum and J. Matas, Proc. British Machine Vision Conf. (BMVC '02), vol. 2, BMVA, pp. 448-457, 2002 |
| 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::RangeImage | RangeImage is derived from pcl/PointCloud and provides functionalities with focus on situations where a 3D scene was captured from a specific view point |
| pcl::RangeImageBorderExtractor | Extract obstacle borders from range images, meaning positions where there is a transition from foreground to background |
| pcl::RangeImagePlanar | RangeImagePlanar is derived from the original range image and differs from it because it's not a spherical projection, but using a projection plane (as normal cameras do), therefore being better applicable for range sensors that already provide a range image by themselves (stereo cameras, kinect, ToF-cameras), so that a conversion to point cloud and then to a spherical range image becomes unnecessary |
| sensor_msgs::RegionOfInterest_< ContainerAllocator > | |
| pcl::Registration< PointSource, PointTarget > | Registration represents the base registration class. All 3D registration methods should inherit from this class |
| RegistrationWrapper< PointSource, PointTarget > | |
| 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::RSDEstimation< PointInT, PointNT, PointOutT > | RSDEstimation estimates the Radius-based Surface Descriptor (minimal and maximal radius of the local surface's curves) for a given point cloud dataset containing points and normals |
| pcl::SACSegmentation< PointT > | SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation |
| pcl::SACSegmentationFromNormals< PointT, PointNT > | SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation |
| pcl::SampleConsensus< T > | SampleConsensus represents the base class. 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::SampleConsensusModelCylinder< PointT, PointNT > | SampleConsensusModelCylinder defines a model for 3D cylinder segmentation |
| pcl::SampleConsensusModelFromNormals< PointT, PointNT > | SampleConsensusModelFromNormals represents the base model class for models that require the use of surface normals for estimation |
| pcl::SampleConsensusModelLine< PointT > | SampleConsensusModelLine defines a model for 3D line segmentation |
| pcl::SampleConsensusModelNormalParallelPlane< PointT, PointNT > | SampleConsensusModelNormalParallelPlane defines a model for 3D plane segmentation using additional surface normal constraints. The plane must lie parallel to a user-specified axis |
| pcl::SampleConsensusModelNormalPlane< PointT, PointNT > | SampleConsensusModelNormalPlane defines a model for 3D plane segmentation using additional surface normal constraints |
| pcl::SampleConsensusModelParallelLine< PointT > | SampleConsensusModelParallelLine defines a model for 3D line segmentation using additional angular constraints |
| pcl::SampleConsensusModelParallelPlane< PointT > | SampleConsensusModelParallelPlane defines a model for 3D plane segmentation using additional angular constraints. The plane must be parallel to a user-specified axis within a user-specified angle threshold |
| pcl::SampleConsensusModelPerpendicularPlane< PointT > | SampleConsensusModelPerpendicularPlane defines a model for 3D plane segmentation using additional angular constraints. The plane must be perpendicular to a user-specified axis, up to a user-specified angle threshold |
| pcl::SampleConsensusModelPlane< PointT > | SampleConsensusModelPlane defines a model for 3D plane segmentation |
| pcl::SampleConsensusModelRegistration< PointT > | SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection |
| pcl::SampleConsensusModelSphere< PointT > | SampleConsensusModelSphere defines a model for 3D sphere segmentation |
| pcl::ScopeTime | Class to measure the time spent in a scope |
| 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 |
| ros::serialization::Serializer< ::pcl::ModelCoefficients_< ContainerAllocator > > | |
| ros::serialization::Serializer< ::pcl::PointIndices_< ContainerAllocator > > | |
| ros::serialization::Serializer< ::pcl::PolygonMesh_< ContainerAllocator > > | |
| ros::serialization::Serializer< ::pcl::Vertices_< ContainerAllocator > > | |
| pcl::RangeImageBorderExtractor::ShadowBorderIndices | Stores the indices of the shadow border corresponding to obstacle borders |
| 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. For more information about the image-based SIFT interest operator, see: |
| pcl::registration::sortCorrespondencesByDistance | |
| pcl::registration::sortCorrespondencesByMatchIndex | |
| pcl::registration::sortCorrespondencesByMatchIndexAndDistance | |
| pcl::registration::sortCorrespondencesByQueryIndex | |
| pcl::registration::sortCorrespondencesByQueryIndexAndDistance | |
| pcl::StatisticalOutlierRemoval< PointT > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check: |
| pcl::StatisticalOutlierRemoval< sensor_msgs::PointCloud2 > | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check: |
| pcl::SurfaceReconstruction< PointInT > | SurfaceReconstruction represents the base surface reconstruction class |
| pcl::TBB_NormalEstimationTBB< PointInT, PointOutT > | |
| pcl::registration::TransformationEstimation< PointSource, PointTarget > | |
| pcl::registration::TransformationEstimationSVD< PointSource, PointTarget > | |
| pcl::TransformationFromCorrespondences | Calculates a transformation based on corresponding 3D points |
| pcl::VectorAverage< real, dimension > | Calculates the weighted average and the covariance matrix |
| pcl::msg::_Vertices::Vertices | |
| pcl::Vertices_< ContainerAllocator > | |
| pcl::VFHEstimation< PointInT, PointNT, PointOutT > | VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals |
| pcl::VFHSignature308 | |
| pcl::View< PointT > | |
| pcl::VoxelGrid< PointT > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
| pcl::VoxelGrid< sensor_msgs::PointCloud2 > | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |
