Object recognition using sparse, reduced-dimension point cloud data

Abstract

It has been shown that point separations as a feature derived from point clouds can be used to discriminate between two objects of similar class. Here we show that the same feature derived from sparse point clouds can maintain significant discrimination capability. Using the point-separation feature, templates created from random realizations of a point cloud are developed for several vehicles. The templates are then used in two-class discrimination tests. The point-separation feature is shown to produce reliable discrimination using a log-likelihood ratio between two objects.