University of Oulu

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.

OVE6D : object viewpoint encoding for depth-based 6D object pose estimation

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Author: Cai, Dingding1; Heikkilä, Janne2; Rahtu, Esa1
Organizations: 1Tampere University
2University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 20.1 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-01-25


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

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Series: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN: 1063-6919
ISSN-E: 2575-7075
ISSN-L: 1063-6919
ISBN: 978-1-6654-6946-3
ISBN Print: 978-1-6654-6947-0
Pages: 6793 - 6803
DOI: 10.1109/cvpr52688.2022.00668
Host publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Conference: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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