AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (7 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

6DOF pose estimation of a 3D rigid object based on edge-enhanced point pair features

College of Computing, National University of Defense Technology, Changsha 410073, China
Department of Spine Surgery, the Second Xiangya Hospital, Central South University, Changsha 410011, China
Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital, Changsha 410011, China
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Beijing Institute of Tracking and Communication Technology, Beijing 100094, China
Show Author Information

Graphical Abstract

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.

Keywords

point pair feature (PPF)pose estimationobject recognition3D point cloud

References

[1]
Li, R. T.; Si, W. X.; Liao, X. Y.; Wang, Q.; Klein, R.; Heng, P. A. Mixed reality based respiratory liver tumor puncture navigation. Computational Visual Media Vol. 5, No. 4, 363–374, 2019.
Crossref Google Scholar
[2]
Wang, Y.; Cao, D.; Chen, S. L.; Li, Y. M.; Zheng, Y. W.; Ohkohchi, N. Current trends in three-dimensional visualization and real-time navigation as well as robot-assisted technologies in hepatobiliary surgery. World Journal of Gastrointestinal Surgery Vol. 13, No. 9, 904–922, 2021.
Crossref Google Scholar
[3]
Kim, K.; Lee, S. Vertebrae localization in CT using both local and global symmetry features. Computerized Medical Imaging and Graphics Vol. 58, 45–55, 2017.
Crossref Google Scholar
[4]
Rusu, R. B.; Bradski, G.; Thibaux, R.; Hsu, J. Fast 3D recognition and pose using the Viewpoint Feature Histogram. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2155–2162, 2010.
Crossref
[5]
Marton, Z. C.; Pangercic, D.; Blodow, N.; Beetz, M. Combined 2D–3D categorization and classification for multimodal perception systems. The International Journal of Robotics Research Vol. 30, No. 11, 1378–1402, 2011.
Crossref Google Scholar
[6]
Madry, M.; Ek, C. H.; Detry, R.; Hang, K. Y.; Kragic, D. Improving generalization for 3D object categorization with Global Structure Histograms. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 1379–1386, 2012.
Crossref
[7]
Johnson, A. E.; Hebert, M. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 21, No. 5, 433–449, 1999.
Crossref Google Scholar
[8]
Rusu, R. B.; Blodow, N.; Beetz, M. Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of the IEEE International Conference on Robotics and Automation, 3212–3217, 2009.
Crossref
[9]
Tombari, F.; Salti, S.; Di Stefano, L. Unique signatures of histograms for local surface description. In: Computer Vision – ECCV 2010. Lecture Notes in Computer Science, Vol. 6313. Daniilidis, K.; Maragos, P.; Paragios, N. Eds. Springer Berlin Heidelberg, 356–369, 2010.
Crossref
[10]
Rusu, R. B.; Holzbach, A.; Beetz, M.; Bradski, G. Detecting and segmenting objects for mobile manipulation. In: Proceedings of the IEEE 12th International Conference on Computer Vision Workshops, 47–54, 2009.
Crossref
[11]
Hinterstoisser, S.; Holzer, S.; Cagniart, C.; Ilic, S.; Konolige, K.; Navab, N.; Lepetit, V. Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: Proceedings of the International Conference on Computer Vision, 858–865, 2011.
Crossref
[12]
Besl, P. J.; McKay, N. D. Method for registration of 3-D shapes. In: Proceedings of the SPIE Volume 1611, Sensor Fusion IV: Control Paradigms and Data Structures, 586–606, 1992.
[13]
Chen, Y.; Medioni, G. Object modelling by registration of multiple range images. Image and Vision Computing Vol. 10, No. 3, 145–155, 1992.
Crossref Google Scholar
[14]
Rusinkiewicz, S.; Levoy, M. Efficient variants of the ICP algorithm. In: Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling, 145–152, 2001.
[15]
Park, K.; Patten, T.; Vincze, M. Pix2Pose: Pixel-wise coordinate regression of objects for 6D pose estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 7667–7676, 2019.
Crossref
[16]
Hodaň T.; Baráth, D.; Matas, J. EPOS: Estimating 6D pose of objects with symmetries. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11700–11709, 2020.
Crossref
[17]
Liang, H. Z.; Ma, X. J.; Li, S.; Görner, M.; Tang, S.; Fang, B.; Sun, F. C.; Zhang, J. W. PointNetGPD: Detecting grasp configurations from point sets. In: Proceedings of the International Conference on Robotics and Automation, 3629–3635, 2019.
Crossref
[18]
Lan, Y. Q.; Duan, Y.; Liu, C. Y.; Zhu, C. Y.; Xiong, Y. S.; Huang, H.; Xu, K. ARM3D: Attention-based relation module for indoor 3D object detection. Computational Visual Media Vol. 8, No. 3, 395–414, 2022.
Crossref Google Scholar
[19]
Zeng, L.; Lv, W. J.; Dong, Z. K.; Liu, Y. J. PPR-net: Accurate 6-D pose estimation in stacked scenarios. IEEE Transactions on Automation Science and Engineering Vol. 19, No. 4, 3139–3151, 2022.
Crossref Google Scholar
[20]
Drost, B.; Ulrich, M.; Navab, N.; Ilic, S. Model globally, match locally: Efficient and robust 3D object recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 998–1005, 2010.
Crossref
[21]
Choi, C.; Christensen, H. I. RGB-D object pose estimation in unstructured environments. Robotics and Autonomous Systems Vol. 75, 595–613, 2016.
Crossref Google Scholar
[22]
Drost, B.; Ilic, S. 3D object detection and localization using multimodal point pair features. In: Proceedings of the 2nd International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission, 9–16, 2012.
Crossref
[23]
Liu, D. Y.; Arai, S.; Miao, J. Q.; Kinugawa, J.; Wang, Z.; Kosuge, K. Point pair feature-based pose estimation with multiple edge appearance models (PPF-MEAM) for robotic Bin picking. Sensors Vol. 18, No. 8, 2719, 2018.
Crossref Google Scholar
[24]
Vock, R.; Dieckmann, A.; Ochmann, S.; Klein, R. Fast template matching and pose estimation in 3D point clouds. Computers & Graphics Vol. 79, 36–45, 2019.
Crossref Google Scholar
[25]
Lu, R. R.; Zhu, F.; Wu, Q. X.; Chen, F. J.; Cui, Y. G.; Kong, Y. Z. Three-dimensional object recognition based on enhanced point pair features. Acta Optica Sinica Vol. 39, No. 8, 0815006, 2019.
Crossref Google Scholar
[26]
Vidal, J.; Lin, C. Y.; Lladó, X.; Martí, R. A method for 6D pose estimation of free-form rigid objects using point pair features on range data. Sensors Vol. 18, No. 8, 2678, 2018.
Crossref Google Scholar
[27]
Guo, J. W.; Xing, X. J.; Quan, W. Z.; Yan, D. M.; Gu, Q. Y.; Liu, Y.; Zhang, X. P. Efficient center voting for object detection and 6D pose estimation in 3D point cloud. IEEE Transactions on Image Processing Vol. 30, 5072–5084, 2021.
Crossref Google Scholar
[28]
Hinterstoisser, S.; Lepetit, V.; Rajkumar, N.; Konolige, K. Going further with point pair features. In: Computer Vision – ECCV 2016. Lecture Notes in Computer Science, Vol. 9907. Leibe, B.; Matas, J.; Sebe, N.; Welling, M. Eds. Springer Cham, 834–848, 2016.
Crossref
[29]
Papazov, C.; Burschka, D. An efficient RANSAC for 3D object recognition in noisy and occluded scenes. In: Computer Vision – ACCV 2010. Lecture Notes in Computer Science, Vol. 6492. Kimmel, R.; Klette, R.; Sugimoto, A. Eds. Springer Berlin Heidelberg, 135–148, 2011.
Crossref
[30]
Mian, A. S.; Bennamoun, M.; Owens, R. Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 28, No. 10, 1584–1601, 2006.
Crossref Google Scholar
[31]
Sølund, T.; Buch, A. G.; Krüger, N.; Aanæs, H. A large-scale 3D object recognition dataset. In: Proceedings of the 4th International Conference on 3D Vision, 73–82, 2016.
Crossref
[32]
Hodaň T.; Matas, J.; Obdržálek, Š. On evaluation of 6D object pose estimation. In: Computer Vision – ECCV 2016 Workshops. Lecture Notes in Computer Science, Vol. 9915. Hua, G.; Jégou, H. Eds. Springer Cham, 606–619, 2016.
Crossref
[33]
Buch, A. G.; Kiforenko, L.; Kraft, D. Rotational subgroup voting and pose clustering for robust 3D object recognition. In: Proceedings of the IEEE International Conference on Computer Vision, 4137–4145, 2017.
Crossref
[34]
Jørgensen, T. B.; Buch, A. G.; Kraft, D. Geometric edge description and classification in point cloud data with application to 3D object recognition. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications, Vol. 2, 333–340, 2015.
Crossref
[35]
Buch, A. G.; Petersen, H. G.; Krüger, N. Local shape feature fusion for improved matching, pose estimation and 3D object recognition. SpringerPlus Vol. 5, No. 1, 1–33, 2016.
Crossref Google Scholar
[36]
Salti, S.; Tombari, F.; Di Stefano, L. SHOT: Unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding Vol. 125, 251–264, 2014.
Crossref Google Scholar
Computational Visual Media
Volume 10 Issue 1,
February 2024
Pages 61-77
DOI: 10.1007/s41095-022-0308-2
Cite this article:
Liu C, Chen F, Deng L, et al. 6DOF pose estimation of a 3D rigid object based on edge-enhanced point pair features. Computational Visual Media, 2024, 10(1): 61-77. https://doi.org/10.1007/s41095-022-0308-2

440

Views

27

Downloads

4

Crossref

2

Web of Science

4

Scopus

0

CSCD

Altmetrics
Received: 12 May 2022
Accepted: 16 August 2022
Published: 30 November 2023
© The Author(s) 2023.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.
Return