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Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform (2404.11156v2)

Published 17 Apr 2024 in cs.CV

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 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.

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References (45)
  1. Shapenet: An information-rich 3d model repository. arXiv, 2015.
  2. Equivariant point network for 3d point cloud analysis. In CVPR, 2021.
  3. Learning 3d dense correspondence via canonical point autoencoder. In NeurIPS, 2021.
  4. Cats: Cost aggregation transformers for visual correspondence. In NeurIPS, 2021.
  5. Pointmixer: Mlp-mixer for point cloud understanding. In ECCV, 2022.
  6. Spherical cnns. In ICLR, 2018.
  7. Vector neurons: A general framework for so(3)-equivariant networks. In ICCV, 2021.
  8. Learning elementary structures for 3d shape generation and matching. In NeurIPS, 2019.
  9. Efficient 2d-to-3d correspondence filtering for scalable 3d object recognition. In CVPR, 2013.
  10. Multiway non-rigid point cloud registration via learned functional map synchronization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2038–2053, 2022a.
  11. Learning semantic correspondence with sparse annotations. In ECCV, 2022b.
  12. Shape-pose disentanglement using se (3)-equivariant vector neurons. In ECCV, 2022.
  13. Transformatcher: Match-to-match attention for semantic correspondence. In CVPR, 2022.
  14. Stable and consistent prediction of 3d characteristic orientation via invariant residual learning. In ICML, 2023.
  15. Efficient semantic matching with hypercolumn correlation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.  139–148, 2024.
  16. Adam: A method for stochastic optimization. In ICLR, 2015.
  17. DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction. In 3DV, 2021.
  18. A closer look at rotation-invariant deep point cloud analysis. In ICCV, 2021a.
  19. A rotation-invariant framework for deep point cloud analysis. IEEE TVCG, 28(12):4503–4514, 2021b.
  20. Feature pyramid networks for object detection. In CVPR, 2017.
  21. Learning implicit functions for topology-varying dense 3d shape correspondence. In NeurIPS, 2020.
  22. Automatic grasp planning using shape primitives. In ICRA, 2003.
  23. Masked autoencoders for point cloud self-supervised learning. In ECCV, 2022.
  24. Fast point transformer. In CVPR, 2022.
  25. Pointnet: Deep learning on point sets for 3d classification and segmentation. In CVPR, 2017a.
  26. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In NIPS, 2017b.
  27. Shot: Unique signatures of histograms for surface and texture description. CVIU, 125:251–264, 2014.
  28. Robotic grasping of novel objects. In NIPS, 2006.
  29. 3d-rotation-equivariant quaternion neural networks. In ECCV, 2020.
  30. Ken Shoemake. Uniform random rotations. In Graphics Gems III (IBM Version), pp.  124–132. Elsevier, 1992.
  31. Srinet: Learning strictly rotation-invariant representations for point cloud classification and segmentation. In ACM MM, 2019.
  32. Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds. arXiv, 2018.
  33. Unique shape context for 3d data description. In ACM workshop on 3D object retrieval, 2010.
  34. Probabilistic warp consistency for weakly-supervised semantic correspondences. In CVPR, 2022.
  35. Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In ICCV, 2019.
  36. Density-aware chamfer distance as a comprehensive metric for point cloud completion. In NeurIPS, 2021.
  37. Triangle-net: Towards robustness in point cloud learning. In WACV, 2021.
  38. Foldingnet: Point cloud auto-encoder via deep grid deformation. In CVPR, 2018.
  39. A scalable active framework for region annotation in 3d shape collections. ACM TOG, 35(6):1–12, 2016.
  40. Keypointnet: A large-scale 3d keypoint dataset aggregated from numerous human annotations. In CVPR, 2020.
  41. 3d human mesh regression with dense correspondence. In CVPR, 2020.
  42. Point transformer. In ICCV, 2021.
  43. 3d point capsule networks. In CVPR, 2019.
  44. Deep implicit templates for 3d shape representation. In CVPR, 2021.
  45. Sc3k: Self-supervised and coherent 3d keypoints estimation from rotated, noisy, and decimated point cloud data. In ICCV, 2023.

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