MATE: A visual based 3D shape descriptor

Since 3D models have been widely applied in many research areas, the techniques for content-based 3D model retrieval become necessary. In this paper, a novel visual based 3D shape descriptor called MATE is proposed. A modified Principal component analysis (PCA) method for model normalization is presented at first. Secondly, a new Adjacent angle distance Fourier (AADF) algorithm is proposed. Then the original two-viewed Dbuffer method is presented to extract characteristics of projected images. Finally, based on the modified PCA method, the shape descriptor MATE is proposed by combining AADF, Tchebichef and two-viewed Dbuffer. Experimental results show that the descriptor MATE provides better retrieval performance than the best current descriptors.