Abstract:

We present a 3D feature descriptor that represents local topologies within a set of folded concentric rings by distances from local points to a projection plane. This feature, called as Concentric Ring Signature (CORS), possesses similar computational advantages to point signatures yet provides more accurate matches. It produces more compact and discriminative descriptors than shape context. It robust to noise and occlusions. As opposed to spin images, CORS does not require the point normal estimations, therefore it is directly applicable to sparse point clouds where the point densities are insufficiently low. Under the same settings, we demonstrate that the discriminative power of CORS is superior to conventional approaches producing twice as good estimates with the percentage of correct match scores improving from 39% to
88%.