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Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds
Xiaolong Li · Yijia Weng · Li Yi · Leonidas Guibas · A. Abbott · Shuran Song · He Wang

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @ None #None

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 partial depth point clouds from the ModelNet40 benchmark, and on real depth point clouds from the NOCS-REAL 275 dataset. The project page with code and visualizations can be found at: dragonlong.github.io/equi-pose.

Author Information

Xiaolong Li (Virginia Tech)

I'm second-year master student in computer engineering at Virginia Tech. I'm currently working in CESCA , and I am advised by Professor A. Lynn Abbott. My primary research direction is computer vision and deep learning.

Yijia Weng (Peking University)
Li Yi (Stanford University)
Leonidas Guibas (stanford.edu)
A. Abbott (Virginia Tech)
Shuran Song (Columbia University)
He Wang (Peking University)

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