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3D symmetry detection by a single image and geometric transformation

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Abstract

Three dimensional symmetry plane detection is a hot research topic in the field of computer vision. When detecting the symmetry plane, the integrity of the three-dimensional point cloud is often ignored, and it is often defaulted to be complete and absolutely symmetrical, which makes the mirror key points relatively easy to be found.This proposes a method for 3D symmetry plane detection based on 2D image and transformation. In detail, the proposed method firstly detects the mirror key points in a single 2D image of the target, then selects the corresponding points in the 2D image and the 3D point cloud to construct a transformation matrix, and finally obtains the mirror key points in the 3D point cloud based on the 2D mirror key points and the transformation matrix to detect the symmetry plane. Experimental evaluations are performed on both synthetic and real point cloud datasets. The results show that the proposed approach is effective for the complete point cloud as well as the incomplete point cloud. Compared with the other two methods, it is proved that the symmetry plane detected by the proposed method is more accurate. The experimental results show that the symmetry plane detected by the proposed method is more accurate than the other two comparison methods. Then the experimental results on incomplete point clouds show that the proposed method could detect symmetry plane effective.

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References

  1. Atadjanov IR, Lee S (2016) Reflection symmetry detection via appearance of structure descriptor. ECCV:3–18

  2. Cheng L, Wu Y, Chen S, Zong W, Yuan Y, Sun Y, Zhuang Q, Li M (2018) A symmetry-based method for LiDAR point registration. IEEE J Select Topics Appl Earth Observ Remote Sensing 11(1):285–299

    Article  Google Scholar 

  3. Dang Q, Mouysset S, Morin G (2014) Symmetry-based alignment for 3D model retrieval. In: 2014 12th International workshop on content-based multimedia indexing (CBMI), pp 1–6

  4. Daras P, Axenopoulos A, Litos G (2011) Investigating the effects of multiple factors towards more accurate 3-D object retrieval. IEEE Trans Multimed 14(52):374–388

    Google Scholar 

  5. Elawady M, Ducottet C, Alata O, Barat C, Colantoni P (2017) Wavelet-based reflection symmetry detection via textural and color histograms: algorithm and results. IEEE ICCV Workshops:1734–1738

  6. Funk C, Lee S, Oswald MR, Tsogkas S, Shen W, Cohen A, Dickinson S, Liu Y (2017) 2017 ICCV challenge: detecting symmetry in the wild,. In: 2017 IEEE international conference on computer vision workshops (ICCVW), pp 692–1701

  7. Gao Z, Xue KX, Zhang H (2019) Multi-view and multivariate gaussian descriptor for 3d object retrieval. Multimed Tools Appl 78(1):555–572

    Article  Google Scholar 

  8. Gao L, Zhang LX, Meng HY, Ren YH, Lai YK, Kobbelt L (2021) PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models. IEEE Trans Visual Comput Graph 27(6):3007–3018

    Article  Google Scholar 

  9. Ge Y, Xiong Y, From PJ (2020) Symmetry-based 3D shape completion for fruit localisation for harvesting robots. Biosyst Eng 197:188–202

    Article  Google Scholar 

  10. Hildebrand MC, Elliott H (2017) Finding mirror symmetry via registration and optimal symmetric pairwise assignment of curves: algorithm and results. In: 2017 IEEE international conference on computer vision workshops (ICCVW), pp 1759–1763

  11. Hosoki D, Lu H, Kim H, Kimura N, Okawachi T, Nozoe E, Nakamura N (2019) Detection of facial symmetric plane based on registration of 3D point cloud. In: 2019 19th International conference on control automation and systems (ICCAS), pp 1037–1041

  12. Ji PL, Liu XG (2019) A fast and efficient 3D reflection symmetry detector based on neural networks. Multimed Tools Appl 78:35471–35492

    Article  Google Scholar 

  13. L Cheng, Wu Y, Chen S, Zong W, Yuan Y, Sun Y, Zhuang Q, Li M (2018) A Symmetry-Based method for liDAR point registration. IEEE J Select Topics Appl Earth Observ Remote Sensing 11:285–299

    Article  Google Scholar 

  14. Li B, Johan H, Ye Y, Lu Y (2016) Efficient 3D reflection symmetry detection: a view-based approach. Graph Model 83:2–14

    Article  MathSciNet  Google Scholar 

  15. Liu ZB, Bu SH, Zhou K et al (2013) A survey on partial retrieval of 3D shapes. J Comput Sci Technol 28(5):836–851

    Article  Google Scholar 

  16. Lowe DG (2004) Distinctive image features from scaleinvariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  17. Loy G, Eklundh JO (2006) Detecting symmetry and symmetric constellations of features. ECCV:508–521

  18. Mitra NJ, Pauly M, Wand M, Ceylan D (2013) Symmetry in 3D geometry: extraction and applications. Comput Graph Forum 32(6):1–23

    Article  Google Scholar 

  19. Nagar R, Raman S (2019) Detecting approximate reflection symmetry in a point set using optimization on manifold. IEEE Trans Signal Process 67:1582–1595

    Article  MathSciNet  MATH  Google Scholar 

  20. Nagar R, Raman S (2020) 3DSymm: robust and accurate 3D reflection symmetry detection. Pattern Recogn 107:107483

    Article  Google Scholar 

  21. Noori SMR, Farnia P, Bayat M, Bahrami N, Shakourirad A, Ahmadian A (2020) Automatic detection of symmetry plane for computer-aided surgical simulation in craniomaxillofacial surgery. Phys Eng Sci Med 43:1087–1099

    Article  Google Scholar 

  22. Sawada T (2010) Visual detection of symmetry of 3D shapes. J Vis 10(6):1–22

    Article  Google Scholar 

  23. Sawada T, Pizlo Z (2008) Detecting mirror-symmetry of a volumetric shape from its single 2D image. In: 2008 IEEE computer society conference on computer vision and pattern recognition workshops, pp 1–8

  24. Shi YF, Huang JW, Zhang HJ, Xu X, Szymon R, Xu K (2020) SymmetryNet: learning to predict reflectional and rotational symmetries of 3D shapes from single-View RGB-D Images. ACM Trans Graph 39(6):1–14

    Google Scholar 

  25. Simon K, Roee L, Shai A, Alex B (2015) Probably approximately symmetric: fast rigid symmetry detection with global guarantees. Comput Graph Forum 34(1):2–13

    Article  Google Scholar 

  26. Tao D, Song M, Li X et al (2008) Bayesian ttensor approach for 3-D face modeling. IEEE Trans Circuits Syst Video Technol 18(10):1397–1410

    Article  Google Scholar 

  27. Wu ZL, Jiang HY, He SY (2021) Symmetry detection of occluded point cloud using deep learning. Proced Comput Sci 183:32–39

    Article  Google Scholar 

  28. Wu ZL, Jiang HY, He SY (2021) Symmetry detection of occluded point cloud using deep learning. Proced Comput Sci 183:32–39

    Article  Google Scholar 

  29. Yang AY, Huang K, Rao S et al (2005) Symmetry-based 3-D reconstruction from perspective images. Comput Vis Image Underst 99(2):210–240

    Article  Google Scholar 

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 51705304, in part by the Natural Science Foundation of Shanghai General Program under Grant 20ZR1421300, and in part by the Shanghai Pujiang Program under Grant 21PJD025.

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  1. College of Automation Engineering, Shanghai University of Electric Power, Shanghai, 200090, China

    Hui Chen & Fangyong Xu

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  1. Hui Chen
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  2. Fangyong Xu
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Correspondence to Hui Chen.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “3D Symmetry Detection by a single image and Geometric Transformation”.

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Chen, H., Xu, F. 3D symmetry detection by a single image and geometric transformation. Multimed Tools Appl 82, 41005–41020 (2023). https://doi.org/10.1007/s11042-023-14955-4

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