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Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform
April 18, 2024, 4:44 a.m. | Chunghyun Park, Seungwook Sim, Jaesik Park, Minsu Cho
cs.CV updates on arXiv.org arxiv.org
Abstract: Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics. However, existing self-supervised methods for this problem assume perfect input shape alignment, restricting their real-world applicability. In this work, we introduce a novel self-supervised Rotation-Invariant 3D correspondence learner with Local Shape Transform, dubbed RIST, that learns to establish dense correspondences between shapes even under challenging intra-class variations and arbitrary orientations. Specifically, RIST learns to dynamically formulate …
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