Learning Canonical Embedding for Non-rigid Shape Matching

• URL: http://arxiv.org/abs/2110.02994v1
• Date: Wed, 6 Oct 2021 18:09:13 GMT
• Title: Learning Canonical Embedding for Non-rigid Shape Matching
• Authors: Abhishek Sharma, Maks Ovsjanikov
• Abstract summary: This paper provides a novel framework that learns canonical embeddings for non-rigid shape matching. Our framework is trained end-to-end and thus avoids instabilities and constraints associated with the commonly-used Laplace-Beltrami basis.
• Score: 36.85782408336389
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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