Self-Supervised Category-Level 6D Object Pose Estimation with Deep Implicit Shape Representation

No. 2: AAAI-22 Technical Tracks 2

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 36

AAAI Technical Track on Computer Vision II

Category-level 6D pose estimation can be better generalized to unseen objects in a category compared with instance-level 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.

10.1609/aaai.v36i2.20104