mlsanityData to 7 Oct 2026

Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud

arXiv 2112.02713 ↗First posted 5 Dec 2021cs.CV cs.AI cs.GRcited by 0

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Abstract (arXiv)

Despite the success of deep functional maps in non-rigid 3D shape matching, there exists no learning framework that models both self-symmetry and shape matching simultaneously. This is despite the fact that errors due to symmetry mismatch are a major challenge in non-rigid shape matching. In this paper, we propose a novel framework that simultaneously learns both self symmetry as well as a pairwise map between a pair of shapes. Our key idea is to couple a self symmetry map and a pairwise map through a regularization term that provides a joint constraint on both of them, thereby, leading to more accurate maps. We validate our method on several benchmarks where it outperforms many competitive baselines on both tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 50 references that are in the map. First posted on arXiv 5 Dec 2021.

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28 of 50 references.

References · 28

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16 more
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  16. 2018Foreground Clustering for Joint Segmentation and Localization in Videos and Imagescited by 1

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