Spherical mapping based descriptors for 3D object matching

Abstract

Matching 3D objects by their similarity is a fundamental problem in computer vision, multimedia databases, molecular biology, computer graphics and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature/descriptor that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. We find that the major problems in comparing 3D mesh objects lie in the non-uniform vertex sampling and level of detail distribution, in the non-uniform polygon topology and in mesh-representation anomalies, so the primary motivation behind the work presented in this paper is the introduction of mesh-parameterization which brings meshes into a form having uniform vertex sampling, uniform polygon topology and filtered anomalies, by spherically mapping the mesh surface. Further, we present two approaches in inferring shapedescriptors from the spherically mapped objects and the results from the conducted experiments.