Dense Non-Rigid Shape Correspondence using Random Forests

We propose a shape matching method that produces dense correspondences tuned to a specific class of shapes and deformations. In a scenario where this class is rep-resented by a small set of example shapes, the proposed method learns a shape descriptor capturing the variabil-ity of the deformations in the given class. The approach enables the wave kernel signature to extend the class of rec-ognized deformations from near isometries to the deforma-tions appearing in the example set by means of a random forest classifier. With the help of the introduced spatial reg-ularization, the proposed method achieves significant im-provements over the baseline approach and obtains state-of-the-art results while keeping short computation times. 1