Simultaneous Pose, Correspondence and Non-Rigid Shape

Abstract : Recent works have shown that 3D shape of non-rigid surfaces can be accurately retrieved from a single image given a set of 3D-to-2D correspondences between that image and another one for which the shape is known. However, existing approaches assume that such correspondences can be readily established, which is not necessarily true when large deformations produce significant appearance changes between the input and the reference images. Furthermore, it is either assumed that the pose of the camera is known, or the estimated solution is pose-ambiguous. In this paper we relax all these assumptions and, given a set of 3D and 2D unmatched points, we present an approach to simultaneously solve their correspondences, compute the camera pose and retrieve the shape of the surface in the input image. This is achieved by introducing weak priors on the pose and shape that we model as Gaussian Mixtures. By combining them into a Kalman filter we can progressively reduce the number of 2D candidates that can be potentially matched to each 3D point, while pose and shape are refined. This lets us to perform a complete and efficient exploration of the solution space and retain the best solution.
Type de document :
Communication dans un congrès
CVPR 2010 - 23rd IEEE Conference on Computer Vision and Pattern Recognition, Jun 2010, San Francisco, United States. IEEE Computer Society, pp.1189-1196, 2010, 〈10.1109/CVPR.2010.5539831〉
Liste complète des métadonnées

Littérature citée [22 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00590264
Contributeur : Team Perception <>
Soumis le : mardi 3 mai 2011 - 09:52:28
Dernière modification le : lundi 2 octobre 2017 - 16:06:02
Document(s) archivé(s) le : jeudi 4 août 2011 - 03:09:21

Fichier

Sanchez-RieraOFM10.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Jordi Sanchez-Riera, Jonas Östlund, Pascal Fua, Francesc Moreno-Noguer. Simultaneous Pose, Correspondence and Non-Rigid Shape. CVPR 2010 - 23rd IEEE Conference on Computer Vision and Pattern Recognition, Jun 2010, San Francisco, United States. IEEE Computer Society, pp.1189-1196, 2010, 〈10.1109/CVPR.2010.5539831〉. 〈inria-00590264〉

Partager

Métriques

Consultations de la notice

111

Téléchargements de fichiers

131