• Corpus ID: 240354495

Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation

@inproceedings{Li2021LeveragingSE,
  title={Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation},
  author={Xiaolong Li and Yijia Weng and Li Yi and Leonidas J. Guibas and A. Lynn Abbott and Shuran Song and He Wang},
  booktitle={Neural Information Processing Systems},
  year={2021}
}
Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models. To reduce the huge amount of pose annotations needed for category-level learning, we propose for the first time a self-supervised learning framework to estimate category-level 6D object pose from single 3D point clouds. During training, our method assumes no ground-truth pose annotations, no CAD models, and no multi-view supervision… 

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