Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform
<|reference_start|>Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform: 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 an SO(3)-invariant local shape transform for each point, which maps the SO(3)-equivariant global shape descriptor of the input shape to a local shape descriptor. These local shape descriptors are provided as inputs to our decoder to facilitate point cloud self- and cross-reconstruction. Our proposed self-supervised training pipeline encourages semantically corresponding points from different shapes to be mapped to similar local shape descriptors, enabling RIST to establish dense point-wise correspondences. RIST demonstrates state-of-the-art performances on 3D part label transfer and semantic keypoint transfer given arbitrarily rotated point cloud pairs, outperforming existing methods by significant margins.<|reference_end|>
@article{park2024learning,
    title={Learning SO(3)-Invariant Semantic Correspondence via Local Shape
  Transform},
    author={Chunghyun Park, Seungwook Kim, Jaesik Park, Minsu Cho},
    journal={arXiv preprint arXiv:2404.11156},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11156},
    primaryClass={cs.CV}
}