Xiaolong Li, Yijia Weng, Li Yi, Leonidas J. Guibas, A. Lynn Abbott, Shuran Song, He Wang. Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation. Neural Information Processing Systems (NeurIPS) 2021

Abstract:

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. The key to our method is to disentangle shape and pose through an invariant shape reconstruction module and an equivariant pose estimation module, empowered by SE(3) equivariant point cloud networks. The invariant shape reconstruction module learns to perform aligned reconstructions, yielding a category-level reference frame without using any annotations. In addition,the equivariant pose estimation module achieves category-level pose estimation accuracy that is comparable to some fully supervised methods. Extensive experiments demonstrate the effectiveness of our approach on both complete and partialdepth point clouds from the ModelNet40 benchmark, and on real depth point cloudsfrom the NOCS-REAL 275 dataset.

Bibtex: 

@article{lwygasw-lsessclope-21,
  Author    = {Li, Xiaolong and Weng, Yijia and Yi, Li and Guibas, Leonidas and Abbott, A Lynn and Song, Shuran and Wang, He},
  Title     = {Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation},
  journal   = {Thirty-Fifth Conference on Neural Information Processing Systems},
  year      = {2021}
}