Abstract
The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses on edge areas for efficient feature extraction for complex geometry. A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method for pose estimation for geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.
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Acknowledgements
We thank Jiazhao Zhang and Yuqin Lan for helpful discussions.
Funding
This work was supported in part by the National Key R&D Program of China (2018AAA0102200), National Natural Science Foundation of China (62132021, 62102435, 61902419, 62002375, 62002376), Natural Science Foundation of Hunan Province of China (2021JJ40696), Huxiang Youth Talent Support Program (2021RC3071), and NUDT Research Grants (ZK19-30, ZK22-52).
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Contributions
Chenyi Liu: Methodology, Writing Draft, Visualization, Results Analysis; Fei Chen: Methodology, Supervision; Lu Deng: Supervision; Renjiao Yi: Supervision, Results Analysis; Lintao Zheng: Supervision; Chenyang Zhu: Supervision, Results Analysis; Jia Wang: Supervision; Kai Xu: Methodology, Supervision.
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The authors have no competing interests to declare that are relevant to the content of this article. The author Kai Xu is the Area Executive Editor of this journal.
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Chenyi Liu received her B.E. degree in software engineering from Tianjin Normal University, China, in 2020. She is now a master student in the National University of Defense Technology (NUDT), China. Her research interests cover 3D point cloud registration.
Fei Chen is a professor of spinal surgery in the Second Xiangya Hospital. His current interests lie in surgical robot perception and automatic navigation.
Lu Deng is a professor in the Surgery Department of the Second Xiangya Hospital. Her current interest is in automatic surgical navigation.
Renjiao Yi is an assistant professor in the School of Computing, NUDT. She received her Ph.D. degree from Simon Fraser University in 2019. She is interested in 3D vision problems such as inverse rendering and image-based relighting.
Lintao Zheng is an assistant professor in the College of Meteorology and Oceanography, NUDT. He earned his Ph.D. degree in computer science from NUDT. His research interests focus on 3D vision and robot perception.
Chenyang Zhu is an assistant professor in the School of Computing, NUDT. He received his Ph.D. degree from Simon Fraser University in 2019. His current directions of interest include 3D vision, and robot perception and navigation.
Jia Wang received her B.E. and M.E. degrees from NUDT. She is currently an assistant research fellow at Beijing Institute of Tracking and Communication Technology. Her research interests focus on launch informatics.
Kai Xu is a professor in the School of Computing, NUDT, where he received his Ph.D. degree in 2011. He serves on the editorial boards of ACM Transactions on Graphics, Computer Graphics Forum, Computers & Graphics, etc.
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Liu, C., Chen, F., Deng, L. et al. 6DOF pose estimation of a 3D rigid object based on edge-enhanced point pair features. Comp. Visual Media 10, 61–77 (2024). https://doi.org/10.1007/s41095-022-0308-2
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DOI: https://doi.org/10.1007/s41095-022-0308-2