  We propose a new framework for the sampling, compression, and analysis of
distributions of point sets and other geometric objects embedded in Euclidean
spaces. Our approach involves constructing a tensor called the RaySense sketch,
which captures nearest neighbors from the underlying geometry of points along a
set of rays. We explore various operations that can be performed on the
RaySense sketch, leading to different properties and potential applications.
Statistical information about the data set can be extracted from the sketch,
independent of the ray set. Line integrals on point sets can be efficiently
computed using the sketch. We also present several examples illustrating
applications of the proposed strategy in practical scenarios.
