Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space
Vestner M, Litman R, Rodola E, Bronstein A, Cremers D (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2017-January
Pages Range: 6681-6690
Conference Proceedings Title: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Event location: Honolulu, HI, USA
ISBN: 9781538604571
Abstract
Many algorithms for the computation of correspondences between deformable shapes rely on some variant of nearest neighbor matching in a descriptor space. Such are, for example, various point-wise correspondence recovery algorithms used as a post-processing stage in the functional correspondence framework. Such frequently used techniques implicitly make restrictive assumptions (e.g., near-isometry) on the considered shapes and in practice suffer from lack of accuracy and result in poor surjectivity. We propose an alternative recovery technique capable of guaranteeing a bijective correspondence and producing significantly higher accuracy and smoothness. Unlike other methods our approach does not depend on the assumption that the analyzed shapes are isometric. We derive the proposed method from the statistical framework of kernel density estimation and demonstrate its performance on several challenging deformable 3D shape matching datasets.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Tel Aviv University
Israel (IL)
Università della Svizzera italiana (USI)
Switzerland (CH)
Technion - Israel Institute of Technology
Israel (IL)
Germany (DE)
Tel Aviv University
Israel (IL)
Università della Svizzera italiana (USI)
Switzerland (CH)
Technion - Israel Institute of Technology
Israel (IL)
How to cite
APA:
Vestner, M., Litman, R., Rodola, E., Bronstein, A., & Cremers, D. (2017). Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (pp. 6681-6690). Honolulu, HI, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Vestner, Matthias, et al. "Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space." Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA Institute of Electrical and Electronics Engineers Inc., 2017. 6681-6690.
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