Nearest Neighbor Sampling of Point Sets using Random Rays

We propose a new framework for the sampling, compression, and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces. A set of randomly selected rays are projected onto their closest points in the data set, forming the ray signature. From the signature, statistical information about the data set, as well as certain geometrical information, can be extracted, independent of the ray set. We present promising results from "RayNN", a neural network for the classification of point clouds based on ray signatures.