D2 shape distribution and artificial neural networks for 3D objects recognition

Abstract

In this paper, we propose a 3D object recognition approach, based on the shape distribution D2 and artificial neural networks. The challenge is to discriminate between similar and dissimilar shapes by finding a shape signature that can be constructed and classified quickly. We propose a connectionist system to recognize 3D objects in VRML (Virtual Reality Modeling Language) format. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The proposed strategy is the following: from a polygon object to be recognized, a triangulation is performed. Then, distances are calculated between two random points of the triangulated surface of the 3D object. The frequency of these distances will be represented by a normalized histogram. The values of these histograms feed a multi-layer neural network with back- propagation training. We demonstrate the potential of this approach in a set of experiments, which proved our system could achieve above 91.7% recognition rate. In addition, to evaluate the efficiency of our method, we compare our classifier with Support vector machine and k- nearest neighbours. The simulation results highlight the performance of the proposed approach.