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 data set. FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals. 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. SHOTEstimation estimates SHOT descriptor for a given point cloud dataset containing points and normals. SHOTEstimationOMP estimates SHOT descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard. MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point. NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard. NormalEstimationTBB estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using Intel's Threading Building Blocks library. NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures. PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals. PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals. VFHEstimation estimates the Viewpoint Feature Histogram (VFH) global descriptor for a given point cloud cluster dataset containing points and normals. NodeletMUX represent a mux nodelet for PointCloud topics: it takes N (up to 8) input topics, and publishes all of them on one output topic. NodeletDEMUX represent a demux nodelet for PointCloud topics: it publishes 1 input topic to N output topics. PCDReader reads in a PCD (Point Cloud Data) v.5 file from disk and converts it to a PointCloud message. BAGReader reads in sensor_msgs/PointCloud2 messages from BAG files. PCDWriter writes a PointCloud message to disk in a PCD (Point Cloud Data) v.5 file format. PointCloudConcatenateFieldsSynchronizer 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. PointCloudConcatenateDataSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on different topics, and concatenates them together into a single PointCloud output message. PassThrough is a filter that uses the basic Filter class mechanisms for passing data around. VoxelGrid assembles a local 3D grid over a given PointCloud, and uses that to downsample the data. ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud. ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud. StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. RadiusOutlierRemoval uses point neighborhood statistics to filter outlier data. CropBox is a filter that allows the user to filter all the data inside of a given box. 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. EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense. 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. 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. SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold. MovingLeastSquares is an implementation of the MLS algorithm for data reconstruction through bivariate polynomial fitting. ConvexHull2D represents a 2D ConvexHull implementation.