Efficient Retrieval of 3D Models Based on Depth Weighted Normal Map

In this paper, we propose a novel 3D model retrieval algorithm based on a new shape descriptor, “depth weighted normal map”. By means of uniform orthogonal sampling, we represent the shape signature of each model as a statistical distribution of its surface normals weighted by relative depth. The distribution is further processed by spherical harmonics analysis to construct the final shape representation. By calculating the distance between shape descriptors of individual 3D models, a faithful similarity measurement is achieved.