The following figure shows a simple example of a selected query point, and its selected k-neighborhood.Īn example of two of the most widely used geometric point features are the underlying surface’s estimated curvature and normal at a query point p. The data space selected around the query point is usually referred to as the k-neighborhood. 3D features are representations at certain 3D points, or positions, in space, which describe geometrical patterns based on the information available around the point. The features library contains data structures and mechanisms for 3D feature estimation from point cloud data. By assuming that the resulting distribution is Gaussian with a mean and a standard deviation, all points whose mean distances are outside an interval defined by the global distances mean and standard deviation can be considered as outliers and trimmed from the dataset.Ī theoretical primer explaining how features work in PCL can be found in the 3D Features tutorial. For each point, the mean distance from it to all its neighbors is computed.
The sparse outlier removal implementation in PCL is based on the computation of the distribution of point to neighbor distances in the input dataset. Some of these outliers can be filtered by performing a statistical analysis on each point’s neighborhood, and trimming those that do not meet a certain criteria. This complicates the estimation of local point cloud 3D features.
Due to measurement errors, certain datasets present a large number of shadow points. An example of noise removal is presented in the figure below.