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Sca-pose: category-level 6D pose estimation with adaptive shape prior based on CNN and graph convolution

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Abstract

Category-level 6D pose estimation aims to accurately predict the spatial position, orientation and scale of unseen objects belonging to a specific category. Existing methods often fall into two categories: prior-based approaches, which typically utilize the Umeyama algorithm and achieve high accuracy but suffer from training limitations and computational overhead, and end-to-end methods, which offer efficient training but often underperform due to a lack of category-specific prior knowledge. To bridge this gap, we propose a novel framework SCA-Pose to leverage the advantages of both approaches. SCA-Pose consists of one main network for efficient inference and an auxiliary network for enhanced accuracy. The main network, featuring local and global feature fusion modules (CNN and HS-Net) and a trainable pose regressor, enables end-to-end learning for real-time applications. The auxiliary network further refines the pose prediction by incorporating intrinsic geometric consistency constraints between Normalized Object Coordinate Space (NOCS) coordinates and object pose and size, while adaptively accounting for intra-class shape variations. Experimental results on the REAL275 and CAMERA25 datasets show that SCA-Pose has significant performance improvement compared to the existing baseline method (RBP-Pose) and can achieve real-time operation (30FPS).

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Acknowledgements

This work was supported by the National Nature Science Foundation of China (62373016) and the Open Projects Program of State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS-2023-22).

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Authors and Affiliations

  1. School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China

    Guoyu Zuo, Shan Yu, Shuangyue Yu, Hong Liu & Min Zhao

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China

    Guoyu Zuo, Shan Yu, Shuangyue Yu, Hong Liu & Min Zhao

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  1. Guoyu Zuo
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  2. Shan Yu
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  3. Shuangyue Yu
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Correspondence to Guoyu Zuo.

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Zuo, G., Yu, S., Yu, S. et al. Sca-pose: category-level 6D pose estimation with adaptive shape prior based on CNN and graph convolution. Intel Serv Robotics 18, 351–361 (2025). https://doi.org/10.1007/s11370-025-00587-0

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  • DOI: https://doi.org/10.1007/s11370-025-00587-0

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