Weakly Supervised Deep Functional Map for Shape Matching - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Weakly Supervised Deep Functional Map for Shape Matching

Résumé

A variety of deep functional maps have been proposed recently, from fully supervised to totally unsupervised, with a range of loss functions as well as different regularization terms. However, it is still not clear what are minimum ingredients of a deep functional map pipeline and whether such ingredients unify or generalize all recent work on deep functional maps. We show empirically minimum components for obtaining state of the art results with different loss functions, supervised as well as unsupervised. Furthermore, we propose a novel framework designed for both full-to-full as well as partial to full shape matching that achieves state of the art results on all benchmark datasets outperforming even the fully supervised methods by a significant margin.
Fichier principal
Vignette du fichier
view.pdf (188.49 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02872053 , version 1 (18-06-2020)
hal-02872053 , version 2 (28-09-2020)

Identifiants

  • HAL Id : hal-02872053 , version 2

Citer

Abhishek Sharma, Maks Ovsjanikov. Weakly Supervised Deep Functional Map for Shape Matching. Neurips 2020, Dec 2020, Vancouver (Virtual Conference), Canada. ⟨hal-02872053v2⟩
535 Consultations
482 Téléchargements

Partager

Gmail Facebook X LinkedIn More