OVE6D : object viewpoint encoding for depth-based 6D object pose estimation
Cai, Dingding; Heikkilä, Janne; Rahtu, Esa (2022-09-27)
D. Cai, J. Heikkiä and E. Rahtu, "OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 6793-6803, doi: 10.1109/CVPR52688.2022.00668.
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https://urn.fi/URN:NBN:fi-fe202301255542
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Abstract
This paper proposes a universal framework, called OVE6D, for model-based 6D object pose estimation from a single depth image and a target object mask. Our model is trained using purely synthetic data rendered from ShapeNet, and, unlike most of the existing methods, it generalizes well on new real-world objects without any fine-tuning. We achieve this by decomposing the 6D pose into viewpoint, in-plane rotation around the camera optical axis and translation, and introducing novel lightweight modules for estimating each component in a cascaded manner. The resulting network contains less than 4M parameters while demon-strating excellent performance on the challenging T-LESS and Occluded LINEMOD datasets without any dataset-specific training. We show that OVE6D outperforms some contemporary deep learning-based pose estimation methods specifically trained for individual objects or datasets with real-world training data. The implementation is available at https://github.com/dingdingcai/OVE6D-pose.
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