Learning Canonical Embedding for Non-rigid Shape Matching

Abhishek Sharma (1) , Maks Ovsjanikov (1)

1 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]

This paper provides a novel framework that learns canonical embeddings for non-rigid shape matching. In contrast to prior work in this direction, our framework is trained end-to-end and thus avoids instabilities and constraints associated with the commonly-used Laplace-Beltrami basis or sequential optimization schemes. On multiple datasets, we demonstrate that learning self symmetry maps with a deep functional map projects 3D shapes into a low dimensional canonical embedding that facilitates non-rigid shape correspondence via a simple nearest neighbor search. Our framework outperforms multiple recent learning based methods on FAUST and SHREC benchmarks while being computationally cheaper, data efficient, and robust.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Apprentissage [cs.LG]; Synthèse d'image et réalité virtuelle [cs.GR]

Abhishek Sharma, Maks Ovsjanikov. Learning Canonical Embedding for Non-rigid Shape Matching. 2021. ⟨hal-03251317v2⟩