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In this paper, we present a novel 3D model alignment method by analyzing the voxels of 3D meshes and a visual similarity based 3D model matching and retrieving method using active tabu search. Firstly, each 3D model is voxelized and applied voxels based PCA transformation, then it is represented by six depth images which are projected by rendering in the PCA coordinate system. Hybrid descriptors are extracted from these depth images to represent the origin 3D model shape features. Matching and retrieving is performed when geometric manifold entropy based active tabu search is used to index all the models in the library by its associated sets of depth images, then the dissimilarity between 3D models are computed from this indexed depth images dataset. Finally, in order to accelerate our proposed approach, all the key operations were implemented on GPU platform using its high parallel architecture. Experimental results show that our proposed method achieve better shape matching effect and gain absolutely improvement in retrieval performances on the Princeton 3D Shape Benchmark database.