G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors

• URL: http://arxiv.org/abs/2212.02910v1
• Date: Tue, 6 Dec 2022 12:09:24 GMT
• Title: G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors
• Authors: Marvin Eisenberger, Aysim Toker, Laura Leal-Taix\'e, Daniel Cremers
• Abstract summary: G-MSM is a novel unsupervised learning approach for non-rigid shape correspondence. We construct an affinity graph on a given set of training shapes in a self-supervised manner. We demonstrate state-of-the-art performance on several recent shape correspondence benchmarks.
• Score: 52.646396621449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as an unordered set of samples, we explicitly model the underlying shape data manifold. To this end, we propose an adaptive multi-shape matching architecture that constructs an affinity graph on a given set of training shapes in a self-supervised manner. The key idea is to combine putative, pairwise correspondences by propagating maps along shortest paths in the underlying shape graph. During training, we enforce cycle-consistency between such optimal paths and the pairwise matches which enables our model to learn topology-aware shape priors. We explore different classes of shape graphs and recover specific settings, like template-based matching (star graph) or learnable ranking/sorting (TSP graph), as special cases in our framework. Finally, we demonstrate state-of-the-art performance on several recent shape correspondence benchmarks, including real-world 3D scan meshes with topological noise and challenging inter-class pairs.

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