Efficient measurement of shape dissimilarity between 3D models using Z-buffer and surface roving method

Abstract

Estimation of the shape dissimilarity between 3D models is a very important problem in both computer vision and graphics for 3D surface reconstruction, modeling, matching, and compression. In this paper, we propose a novel method called surface roving technique to estimate the shape dissimilarity between 3D models. Unlike conventional methods, our surface roving approach exploits a virtual camera and Z-buffer, which is commonly used in 3D graphics. The corresponding points on different 3D models can be easily identified, and also the distance between them is determined efficiently, regardless of the representation types of the 3D models. Moreover, by employing the viewpoint sampling technique, the overall computation can be greatly reduced so that the dissimilarity is obtained rapidly without loss of accuracy. Experimental results show that the proposed algorithm achieves fast and accurate measurement of shape dissimilarity for different types of 3D object models.