Intrinsic shape context descriptors for deformable shapes

Abstract : In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape contexts: for this purpose, we chart the surface by shooting geodesic outwards from the point being analyzed; `angle' is treated as tantamount to geodesic shooting direction, and radius as geodesic distance. To deal with orientation ambiguity, we exploit properties of the Fourier transform. Our charting method is intrinsic, i.e., invariant to isometric shape transformations. The resulting descriptor is a meta-descriptor that can be applied to any photometric or geometric property field defined on the shape, in particular, we can leverage recent developments in intrinsic shape analysis and construct ISC based on state-of-the-art dense shape descriptors such as heat kernel signatures. Our experiments demonstrate a notable improvement in shape matching on standard benchmarks.
Type de document :
Communication dans un congrès
CVPR 2012 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2012, Providence, United States. IEEE, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.159-166, 2012, 〈10.1109/CVPR.2012.6247671〉
Liste complète des métadonnées

https://hal.inria.fr/hal-00857572
Contributeur : Iasonas Kokkinos <>
Soumis le : mardi 3 septembre 2013 - 16:48:01
Dernière modification le : jeudi 29 mars 2018 - 13:36:02

Lien texte intégral

Identifiants

Collections

Citation

Iasonas Kokkinos, Michael M. Bronstein, Roee Litman, Alexander M. Bronstein. Intrinsic shape context descriptors for deformable shapes. CVPR 2012 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2012, Providence, United States. IEEE, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.159-166, 2012, 〈10.1109/CVPR.2012.6247671〉. 〈hal-00857572〉

Partager

Métriques

Consultations de la notice

356