Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New DatasetDownload PDF

26 Sept 2022, 12:09 (modified: 09 Nov 2022, 02:12)NeurReps 2022 PosterReaders: Everyone
Abstract: 6D object pose estimation is one of the fundamental problems in computer vision and robotics research. While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environments given the limited number of annotated data. In this paper, we collect Wild6D, a new unlabeled RGBD object video dataset with diverse instances and backgrounds. We utilize this data to generalize category-level 6D object pose estimation in the wild with semi-supervised learning. We propose a new model, called \textbf{Re}ndering for \textbf{Po}se estimation network (\textbf{RePoNet}), that is jointly trained using the free ground-truths with the synthetic data, and a silhouette matching objective function on the real-world data. Without using any 3D annotations on real data, our method outperforms state-of-the-art methods on the previous dataset and our Wild6D test set (with manual annotations for evaluation) by a large margin. Our code and dataset will be made publicly available.
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