MATE: A Visual Based 3D Shape Descriptor

Since 3D models have been widely appliedin many research areas, the techniques for content-based3D model retrieval become necessary. In this paper, anovel visual based 3D shape descriptor called MATE isproposed. 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 Dbuffermethod is presented to extract characteristics of projectedimages. Finally, based on the modified PCA method, theshape descriptor MATE is proposed by combining AADF,Tchebichef and two-viewed Dbuffer. Experimental resultsshow that the descriptor MATE provides better retrievalperformance than the best current descriptors.