Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching

• URL: http://arxiv.org/abs/2204.13453v1
• Date: Thu, 28 Apr 2022 12:36:09 GMT
• Title: Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching
• Authors: Nicolas Donati and Etienne Corman and Maks Ovsjanikov
• Abstract summary: We propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on top of DiffusionNet, making it robust to discretization changes.
• Score: 32.03608983026839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art fully intrinsic networks for non-rigid shape matching often struggle to disambiguate the symmetries of the shapes leading to unstable correspondence predictions. Meanwhile, recent advances in the functional map framework allow to enforce orientation preservation using a functional representation for tangent vector field transfer, through so-called complex functional maps. Using this representation, we propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on top of DiffusionNet, making it robust to discretization changes. Additionally, we introduce a vector field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors.

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