Abstract: Feature extraction method for 3D model was studied. A method of WMPCA(Weighted Multi-linear Principal Component Analysis) for feature extraction based on MPCA(Multi-Linear Principal Component Analysis) was proposed, and it was applied to 3D model feature extraction. Firstly, the 3D model was transformed into multi-angle 2D projection images, and then were extracted features using tensor from multi-direction. Finally, the extracted features were applied to 3D model retrieval. Experimental results on Princeton Shape Benchmark show that the feature extraction method is better than classical shape distribution method and traditional MPCA method.

Key words: 3D model retrieval, multi-linear principal component analysis (MPCA), tensor subspace learning