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WU Zhi-hui, WANG Li-zhong, LIANG Jin, GONG Chun-yuan, ZHU Feng, CHANG Zhi-wen, XU Jian-ning. Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts[J]. Chinese Optics. doi: 10.37188/CO.2023-0225
Citation: WU Zhi-hui, WANG Li-zhong, LIANG Jin, GONG Chun-yuan, ZHU Feng, CHANG Zhi-wen, XU Jian-ning. Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts[J]. Chinese Optics. doi: 10.37188/CO.2023-0225

Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts

doi: 10.37188/CO.2023-0225
Funds:  Supported by the National Key R&D Program of China (No. 2022YFB4601802); National Natural Science Foundation of China (No. 52275543)
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  • Corresponding author: wanglz@mail.xjtu.edu.cn
  • Received Date: 18 Dec 2023
  • Accepted Date: 28 Feb 2024
  • Available Online: 17 May 2024
  • Triangular mesh model registration is an important part of industrial automation detection software. The registration accuracy has an important influence on the shape and position tolerance of mechanical parts. Aiming at the problems of low accuracy and poor robustness of automatic registration of triangular mesh models, this paper proposes a segmentation method for enhanced features in automatic registration of triangular mesh models for mechanical parts. Firstly, the K value of the feature segmentation of the triangular mesh model is determined, and the seed points are determined by the Laplacian matrix for iterative initialization. Secondly, this paper uses the appropriate region shape agent and cost function to accelerate the process, and performs multi-source iterative clustering to obtain the feature segmentation results. Finally, based on the feature segmentation results of the triangular mesh model, the coarse registration based on the singular value decomposition method is performed, and the fine registration is performed according to the EM-ICP. Compared with the traditional feature descriptor coarse registration and ICP fine registration method, the experimental results show that the registration error of the proposed method is reduced by 25.2 %, and the automatic registration time is shortened by 62.6 %, which effectively improves the accuracy and efficiency of the automatic registration of the triangular mesh model.

     

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  • [1]
    周晓东, 张雅超, 谭庆昌, 等. 基于结构光视觉技术的圆柱度测量新方法[J]. 吉林大学学报(工学版),2017,47(2):524-529.

    ZHOU X D, ZHANG Y CH, TAN Q CH, et al. New method of cylindricity measurement based on structured light vision technology[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(2): 524-529. (in Chinese).
    [2]
    RAFFAELI R, MENGONI M, GERMANI M, et al. Off-line view planning for the inspection of mechanical parts[J]. International Journal on Interactive Design and Manufacturing, 2013, 7(1): 1-12. doi: 10.1007/s12008-012-0160-1
    [3]
    PHAN N D M, QUINSAT Y, LAVERNHE S, et al. Scanner path planning with the control of overlap for part inspection with an industrial robot[J]. The International Journal of Advanced Manufacturing Technology, 2018, 98(1-4): 629-643. doi: 10.1007/s00170-018-2336-8
    [4]
    杨鹏程, 杨朝, 孟杰, 等. 基于法向量和面状指数特征的文物点云棱界配准方法[J]. 中国光学(中英文),2023,16(3):654-662. doi: 10.37188/CO.2022-0156

    YANG P CH, YANG ZH, MENG J, et al. Aligning method for point cloud prism boundaries of cultural relics based on normal vector and faceted index features[J]. Chinese Optics, 2023, 16(3): 654-662. (in Chinese). doi: 10.37188/CO.2022-0156
    [5]
    JUNIOR E M O, SANTOS D R, MIOLA G A R. A new variant of the ICP algorithm for pairwise 3D point cloud registration[J]. American Scientific Research Journal for Engineering, Technology, and Sciences, 2022, 85(1): 71-88.
    [6]
    YANG J L, LI H D, CAMPBELL D, et al. Go-ICP: a globally optimal solution to 3D ICP point-set registration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(11): 2241-2254. doi: 10.1109/TPAMI.2015.2513405
    [7]
    LIAN W, ZHANG L, YANG M H. An efficient globally optimal algorithm for asymmetric point matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(7): 1281-1293. doi: 10.1109/TPAMI.2016.2603988
    [8]
    LIU Y L, WANG CH, SONG ZH J, et al. Efficient global point cloud registration by matching rotation invariant features through translation search[C]. Proceedings of the 15th European Conference on Computer Vision (ECCV), Springer, 2018: 448-463.
    [9]
    刘跃生, 陈新度, 吴磊, 等. 混合稀疏迭代最近点配准[J]. 光学 精密工程,2021,29(9):2255-2267. doi: 10.37188/OPE.20212909.2255

    LIU Y SH, CHEN X D, WU L, et al. Sparse mixture iterative closest point registration[J]. Optics and Precision Engineering, 2021, 29(9): 2255-2267. (in Chinese). doi: 10.37188/OPE.20212909.2255
    [10]
    林森, 张强. 应用邻域点信息描述与匹配的点云配准[J]. 光学 精密工程,2022,30(8):984-997. doi: 10.37188/OPE.20223008.0984

    LIN S, ZHANG Q, et al. Point cloud registration using neighborhood point information description and matching[J]. Optics and Precision Engineering, 2022, 30(8): 984-997. (in Chinese). doi: 10.37188/OPE.20223008.0984
    [11]
    GARLAND M, WILLMOTT A, HECKBERT P S. Hierarchical face clustering on polygonal surfaces[C]. Proceedings of the 2001 Symposium on Interactive 3D Graphics, ACM, 2001: 49-58.
    [12]
    THEOLOGOU P, PRATIKAKIS I, THEOHARIS T. Unsupervised spectral mesh segmentation driven by heterogeneous graphs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2): 397-410. doi: 10.1109/TPAMI.2016.2544311
    [13]
    SHI J B, MALIK J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905. doi: 10.1109/34.868688
    [14]
    CHAHHOU M, MOUMOUN L, EL FAR M, et al. Segmentation of 3D meshes usingp-spectral clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8): 1687-1693. doi: 10.1109/TPAMI.2013.2297314
    [15]
    DONG Q J, WANG Z X, LI M Y, et al. Laplacian2Mesh: Laplacian-based mesh understanding[J]. IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2023.3259044.
    [16]
    LEI H, AKHTAR N, SHAH M, et al. Mesh convolution with continuous filters for 3-D surface parsing[J]. IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3281871.
    [17]
    LAVOUÉ G, DUPONT F, BASKURT A. A new CAD mesh segmentation method, based on curvature tensor analysis[J]. Computer-Aided Design, 2005, 37(10): 975-987. doi: 10.1016/j.cad.2004.09.001
    [18]
    BHOLOWALIA P, KUMAR A. EBK-means: A clustering technique based on elbow method and K-means in WSN[J]. International Journal of Computer Applications, 2014, 105(9): 17-24.
    [19]
    COHEN-STEINER D, ALLIEZ P, DESBRUN M. Variational shape approximation[C]. Proceedings of ACM SIGGRAPH 2004 Papers, ACM, 2004: 905-914.
    [20]
    LIANG Y Q, HE F ZH, ZENG X T. 3D mesh simplification with feature preservation based on whale optimization algorithm and differential evolution[J]. Integrated Computer-Aided Engineering, 2020, 27(4): 417-435. doi: 10.3233/ICA-200641
    [21]
    DU T, INALA J P, PU Y W, et al. InverseCSG: Automatic conversion of 3D models to CSG trees[J]. ACM Transactions on Graphics, 2018, 37(6): 213.
    [22]
    BORJI A, CHENG M M, HOU Q B, et al. Salient object detection: a survey[J]. Computational Visual Media, 2019, 5(2): 117-150. doi: 10.1007/s41095-019-0149-9
    [23]
    ATTENE M, FALCIDIENO B, SPAGNUOLO M. Hierarchical mesh segmentation based on fitting primitives[J]. The Visual Computer, 2006, 22(3): 181-193. doi: 10.1007/s00371-006-0375-x
    [24]
    SHAPIRA L, SHAMIR A, COHEN-OR D. Consistent mesh partitioning and skeletonisation using the shape diameter function[J]. The Visual Computer, 2008, 24(4): 249-259. doi: 10.1007/s00371-007-0197-5
    [25]
    KATZ S, TAL A. Hierarchical mesh decomposition using fuzzy clustering and cuts[J]. ACM Transactions on Graphics, 2003, 22(3): 954-961. doi: 10.1145/882262.882369
    [26]
    LAI Y K, HU S M, MARTIN R R, et al. Rapid and effective segmentation of 3D models using random walks[J]. Computer Aided Geometric Design, 2009, 26(6): 665-679. doi: 10.1016/j.cagd.2008.09.007
    [27]
    YANG H, SHI J N, CARLONE L. TEASER: fast and certifiable point cloud registration[J]. IEEE Transactions on Robotics, 2021, 37(2): 314-333. doi: 10.1109/TRO.2020.3033695
    [28]
    RODRIGUES R S V, MORGADO J F M, GOMES A J P. A contour-based segmentation algorithm for triangle meshes in 3D space[J]. Computers & Graphics, 2015, 49: 24-35.
    [29]
    CHEN X B, GOLOVINSKIY A, FUNKHOUSER T. A benchmark for 3D mesh segmentation[J]. ACM Transactions on Graphics, 2009, 28(3): 73.
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