Deformation-Guided Unsupervised Non-Rigid Shape Matching - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Deformation-Guided Unsupervised Non-Rigid Shape Matching

Résumé

We present an unsupervised data-driven approach for non-rigid shape matching. Shape matching identifies correspondences between two shapes and is a fundamental step in many computer vision and graphics applications. Our approach is designed to be particularly robust when matching shapes digitized using 3D scanners that contain fine geometric detail and suffer from different types of noise including topological noise caused by the coalescence of spatially close surface regions. We build on two strategies. First, using a hierarchical patch based shape representation we match shapes consistently in a coarse to fine manner, allowing for robustness to noise. This multi-scale representation drastically reduces the dimensionality of the problem when matching at the coarsest scale, rendering unsupervised learning feasible. Second, we constrain this hierarchical matching to be reflected in 3D by fitting a patch-wise near-rigid deformation model. Using this constraint, we leverage spatial continuity at different scales to capture global shape properties, resulting in matchings that generalize well to data with different deformations and noise characteristics. Experiments demonstrate that our approach obtains significantly better results on raw 3D scans than state-of-the-art methods, while performing on-par on standard test scenarios.
Fichier principal
Vignette du fichier
main.pdf (4.69 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Licence : CC BY NC - Paternité - Pas d'utilisation commerciale

Dates et versions

hal-04249563 , version 1 (09-11-2023)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Aymen Merrouche, João Regateiro, Stefanie Wuhrer, Edmond Boyer. Deformation-Guided Unsupervised Non-Rigid Shape Matching. BMVC 2023 - 34th British Machine Vision Conference, Nov 2023, Aberdeen, United Kingdom. pp.1-22. ⟨hal-04249563⟩
90 Consultations
90 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More