Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 123456]
 Cpcl_ros::BasePublisher
 Cpcl_ros::Publisher< PointT >
 Cpcl_ros::Publisher< sensor_msgs::PointCloud2 >
 Cros::message_traits::DataType< pcl::PointCloud< T > >
 Cros::message_traits::Definition< pcl::PointCloud< T > >
 Cros::message_traits::FalseType [external]
 Cros::message_traits::HasHeader< pcl::PointCloud< T > >
 Cpcl::detail::FieldsLength< PointT >
 Cpcl::detail::FieldStreamer< Stream, PointT >
 Cros::message_traits::FrameId< pcl::PointCloud< T > >
 Cros::message_traits::MD5Sum< pcl::PointCloud< T > >
 Cnodelet::Nodelet [external]
 Cnodelet_topic_tools::NodeletLazy [external]
 Cpcl_ros::PCLNodeletPCLNodelet represents the base PCL Nodelet class. All PCL nodelets should inherit from this class
 Cpcl_ros::ConvexHull2DConvexHull2D represents a 2D ConvexHull implementation
 Cpcl_ros::EuclideanClusterExtractionEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
 Cpcl_ros::ExtractPolygonalPrismDataExtractPolygonalPrismData 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
 Cpcl_ros::FeatureFeature represents the base feature class. Some generic 3D operations that are applicable to all features are defined here as static methods
 Cpcl_ros::FeatureFromNormals
 Cpcl_ros::BoundaryEstimationBoundaryEstimation 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
 Cpcl_ros::FPFHEstimationFPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
 Cpcl_ros::FPFHEstimationOMPFPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard
 Cpcl_ros::PFHEstimationPFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
 Cpcl_ros::PrincipalCurvaturesEstimationPrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals
 Cpcl_ros::SHOTEstimationSHOTEstimation estimates SHOT descriptor
 Cpcl_ros::SHOTEstimationOMPSHOTEstimation estimates SHOT descriptor using OpenMP
 Cpcl_ros::VFHEstimationVFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals
 Cpcl_ros::MomentInvariantsEstimationMomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
 Cpcl_ros::NormalEstimationNormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures
 Cpcl_ros::NormalEstimationOMPNormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
 Cpcl_ros::NormalEstimationTBBNormalEstimationTBB estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using Intel's Threading Building Blocks library
 Cpcl_ros::FilterFilter represents the base filter class. Some generic 3D operations that are applicable to all filters are defined here as static methods
 Cpcl_ros::CropBoxCropBox is a filter that allows the user to filter all the data inside of a given box
 Cpcl_ros::ExtractIndicesExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud
 Cpcl_ros::PassThroughPassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
 Cpcl_ros::ProjectInliersProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud
 Cpcl_ros::RadiusOutlierRemovalRadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K
 Cpcl_ros::StatisticalOutlierRemovalStatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check:
 Cpcl_ros::VoxelGridVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
 Cpcl_ros::MovingLeastSquaresMovingLeastSquares represents a nodelet using the MovingLeastSquares implementation. The type of the output is the same as the input, it only smooths the XYZ coordinates according to the parameters. Normals are estimated at each point as well and published on a separate topic
 Cpcl_ros::PCDReaderPoint Cloud Data (PCD) file format reader
 Cpcl_ros::PCDWriterPoint Cloud Data (PCD) file format writer
 Cpcl_ros::SACSegmentationSACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation
 Cpcl_ros::SACSegmentationFromNormalsSACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation
 Cpcl_ros::SegmentDifferencesSegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold
 Cpcl_ros::PointCloudConcatenateDataSynchronizerPointCloudConcatenateFieldsSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on the same topic, checks their timestamps, and concatenates their fields together into a single PointCloud output message
 Cpcl_ros::PointCloudConcatenateFieldsSynchronizerPointCloudConcatenateFieldsSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on the same topic, checks their timestamps, and concatenates their fields together into a single PointCloud output message
 Cpcl_ros::BAGReaderBAG PointCloud file format reader
 CPCDGenerator
 CPointCloudToImage
 CPointCloudToPCD
 Cros::serialization::Serializer< pcl::PointCloud< T > >
 Cros::message_traits::TimeStamp< pcl::PointCloud< T > >


pcl_ros
Author(s): Open Perception, Julius Kammerl , William Woodall
autogenerated on Mon Jun 10 2019 14:19:19