Spectral classification of 3D articulated shapes

A large number of 3D models distributed on internet has created the demand for automatic shape classification. This paper presents a novel classification method for 3D mesh shapes. Each shape is represented by the eigenvalues of an appropriately defined affinity matrix, forming a spectral embedding which achieves invariance against rigid-body transformations, uniform scaling, and shape articulation. And then, Adaboost algorithm is applied to classify the 3D models in the spectral space according to its immunity to overfitting. We evaluate the approach on the McGill 3D shape benchmark and compare the results with previous classification method, and it achieves higher classification accuracy. This method is suitable for automatic classification of 3D articulated shapes.