A similarity measure for 3D rigid registration of point clouds using image-based descriptors with low overlap

This paper introduces a novel similarity measure for 3D rigid registration algorithms that use comparison between image-based descriptors in order to find correspondences between two partial 3D point clouds belonging to the same object. Unlike the similarity measures based on correlation coefficient, joint entropy, mutual information or others that have been used by the most popular 3D registration algorithms this similarity measure is based on distance between pixels and takes into account the problems of clutter and occlusion that can appear in real situations that need 3D registration or object recognition.