@inproceedings{03ae785499fd4c4bbbc981551aa2e0ea,
title = "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.",
keywords = "Correspondence, Laplace-Beltrami operator, Shape matching",
author = "Alon Shtern and Ron Kimmel",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 2nd International Conference on 3D Vision, 3DV 2014 ; Conference date: 08-12-2014 Through 11-12-2014",
year = "2015",
month = feb,
day = "6",
doi = "10.1109/3DV.2014.24",
language = "الإنجليزيّة",
series = "Proceedings - 2014 International Conference on 3D Vision, 3DV 2014",
pages = "499--505",
booktitle = "Proceedings - 2014 International Conference on 3D Vision, 3DV 2014",
}