Iterative Closest Spectral Kernel Maps

Abstract

An important operation in geometry processing is finding the correspondences between pairs of shapes. Measures of dissimilarity between surfaces, has been found to be highly useful for nonrigid shape comparison. Here, we analyze the applicability of the spectral kernel distance, for solving the shape matching problem. To align the spectral kernels, we introduce the iterative closest spectral kernel maps (ICSKM) algorithm. The ICSKM algorithm farther extends the iterative closest point algorithm to the class of deformable shapes. The proposed method achieves state-of-the-art results on the Princeton isometric shape matching protocol applied, as usual, to the TOSCA and SCAPE benchmarks.