Self-Supervised Category-Level 6D Object Pose Estimation with Deep Implicit Shape Representation
Wanli Peng, Jianhang Yan, Hongtao Wen, Yi Sun
[AAAI-22] Main Track
Abstract:
Category-level 6D pose estimation can be better generalized to unseen objects in a category compared with instancelevel 6D pose estimation. However, existing category-level 6D pose estimation methods usually require supervised training with a sufficient number of 6D pose annotations of objects which makes them difficult to be applied in real scenarios. To address this problem, we propose a self-supervised framework for category-level 6D pose estimation in this paper.We leverage DeepSDF as a 3D object representation and design several novel loss functions based on DeepSDF to help the self-supervised model predict unseen object poses without any 6D object pose labels and explicit 3D models in real scenarios.Experiments demonstrate that our method achieves comparable performance with the state-of-the-art fully supervised methods on the category-level NOCS benchmark.
Introduction Video
Sessions where this paper appears
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Poster Session 3
Red 2
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Poster Session 8
Red 2
