G-MSM: Unsupervised Multi-Shape Matching With Graph-Based Affinity Priors

Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 22762-22772

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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[bibtex]
@InProceedings{Eisenberger_2023_CVPR, author = {Eisenberger, Marvin and Toker, Aysim and Leal-Taix\'e, Laura and Cremers, Daniel}, title = {G-MSM: Unsupervised Multi-Shape Matching With Graph-Based Affinity Priors}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {22762-22772} }