留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于法向量和面状指数特征的文物点云棱界配准方法

杨鹏程 杨朝 孟杰 肖渊 崔嘉宝

杨鹏程, 杨朝, 孟杰, 肖渊, 崔嘉宝. 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
引用本文: 杨鹏程, 杨朝, 孟杰, 肖渊, 崔嘉宝. 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 Peng-cheng, YANG Zhao, MENG Jie, XIAO Yuan, CUI Jia-bao. 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. doi: 10.37188/CO.2022-0156
Citation: YANG Peng-cheng, YANG Zhao, MENG Jie, XIAO Yuan, CUI Jia-bao. 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. doi: 10.37188/CO.2022-0156

基于法向量和面状指数特征的文物点云棱界配准方法

doi: 10.37188/CO.2022-0156
基金项目: 陕西省自然科学基础研究计划——面上项目(No. 2022JM-219)
详细信息
    作者简介:

    杨鹏程(1985—),男,副教授,博士,2008年于长沙理工大学获得学士学位,2013年于西安交通大学获得博士学位,主要从事激光干涉测量、三维数据精确建模、数字图像处理的研究。Email:yangpengcheng@xpu.edu.cn

    杨 朝(1996—),男,硕士研究生,2019年于西安工程大学获得学士学位,主要研究方向:全息幻影成像,三维数据精确建模。Email:muyi_meng2021@163.com

  • 中图分类号: TP391.9

Aligning method for point cloud prism boundaries of cultural relics based on normal vector and faceted index features

Funds: Supported by Natural Science Basic Research Program of Shaanxi Province (No. 2022JM-219)
More Information
  • 摘要:

    三维重建是文物信息保护常用的方法,其主要通过点云配准技术重组文物空间的点云信息,配准精度对文物复现有重要影响。针对文物表面复杂点云纹理特征配准存在精度低、鲁棒性差的问题,本文提出一种基于法向量夹角和面状指数特征的局域点云配准方法。首先,根据点云平面特性设定法向量夹角和协方差矩阵阈值,提取同时满足这两个特征的点云特征点;其次,采用K近邻搜索方法提取点云局域特征点集,通过刚性变换使两组点云质心位置重合,完成粗配准;最终,在两点云粗配准的基础上,根据迭代最近点ICP进行精配准。与传统ICP方法进行对比分析,结果显示本文方法的点云配准误差下降了3%,匹配耗时降低了50%,有效地提高了配准精度和效率,增强了点云配准的鲁棒性。

     

  • 图 1  不同表面形状的法向量

    Figure 1.  Normal vectors of different surface shapes

    图 2  点云配准方法流程图

    Figure 2.  Flow chart of point cloud alignment method

    图 3  模型实测图

    Figure 3.  Model measurement diagram

    图 4  原始点云图

    Figure 4.  Primitive point cloud diagrams

    图 5  点云法向量特征图

    Figure 5.  Normal vector feature plots of point cloud

    图 6  点云面状指数特征图

    Figure 6.  Point cloud-like exponential characteristics

    图 7  点云特征图

    Figure 7.  Point cloud feature diagrams

    图 8  特征点云局部配准图

    Figure 8.  Feature point cloud local registration diagram

    图 9  兵马俑点云配准对比图

    Figure 9.  Comparison charts of point cloud registration for terracotta army

    表  1  兵马俑点云配准过程实验数据分析

    Table  1.   Experimental data analysis of terracotta army point cloud registration process

    方法点云数目特征点数配准时间/sRMSE/mm
    传统ICP74320/
    66778
    72322/6644011.737.88
    本文方法6083/57236.224.73
    下载: 导出CSV
  • [1] 肖宇强. 海外藏中国戏曲与民俗文物的“数字孪生”及数字化资源平台建设[J]. 文化遗产,2022(1):89-96. doi: 10.3969/j.issn.1674-0890.2022.01.012

    XIAO Y Q. Digital twin of Chinese opera and folklore relics in overseas collections and the construction of digital resource Platform[J]. Cultural Heritage, 2022(1): 89-96. (in Chinese) doi: 10.3969/j.issn.1674-0890.2022.01.012
    [2] ALTUNTAS C. Point cloud acquisition techniques by using scanning LiDAR for 3D modelling and mobile measurement[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2022, XLIII-B2-2022: 967-972. doi: 10.5194/isprs-archives-XLIII-B2-2022-967-2022
    [3] 史艳侠, 娄小平, 李伟仙. 线结构光点云粗拼接方法研究[J]. 电子测量与仪器学报,2018,32(6):12-16. doi: 10.13382/j.jemi.2018.06.003

    SHI Y X, LOU X P, LI W X. Coarse stitching of point structured cloud of line structured light research on unconstrained method of surface measurement[J]. Journal of Electronic Measurement and Instrumentation, 2018, 32(6): 12-16. (in Chinese) doi: 10.13382/j.jemi.2018.06.003
    [4] MEDDA D, ANOFFO Y M, PERRA C, et al. Automated point cloud acquisition system using multiple RGB-D cameras[J]. Proceedings of SPIE, 2020, 11353: 1135315.
    [5] TOSCHI I, FARELLA E M, WELPONER M, et al. Quality-based registration refinement of airborne LiDAR and photogrammetric point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 172: 160-170. doi: 10.1016/j.isprsjprs.2020.12.005
    [6] 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
    [7] 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. 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
    [8] FOTSING C, NZIENGAM N, BOBDA C. Large common plansets-4-points congruent sets for point cloud registration[J]. ISPRS International Journal of Geo-Information, 2020, 9(11): 647. doi: 10.3390/ijgi9110647
    [9] SUN L, MANABE Y, YATA N. [Paper] double sparse representation for point cloud registration[J]. ITE Transactions on Media Technology and Applications, 2019, 7(3): 148-158. doi: 10.3169/mta.7.148
    [10] 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 Academic Scientific Research Journal for Engineering,Technology,and Sciences, 2022, 85(1): 71-88.
    [11] KUÇAK R A, EROL S, EROL B. An experimental study of a new keypoint matching algorithm for automatic point cloud registration[J]. ISPRS International Journal of Geo-Information, 2021, 10(4): 204. doi: 10.3390/ijgi10040204
    [12] BAUER P, HECKLER L, WORACK M, et al. Registration strategy of point clouds based on region-specific projections and virtual structures for robot-based inspection systems[J]. Measurement, 2021, 185: 109963. doi: 10.1016/j.measurement.2021.109963
    [13] SUN W X, WANG J, JIN F X, et al. Datum feature extraction and deformation analysis method based on normal vector of point cloud[J]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2018, XLII-3: 1601-1606. doi: 10.5194/isprs-archives-XLII-3-1601-2018
    [14] FOORGINEJAD A, KHALILI K. Umbrella curvature: a new curvature estimation method for point clouds[J]. Procedia Technology, 2014, 12: 347-352. doi: 10.1016/j.protcy.2013.12.497
    [15] 宁浩, 董秀军, 刘谦, 等. 利用点云法向量实现岩体结构面识别[J/OL]. 武汉大学学报(信息科学版), 1-13 [2022-06-07]. http://kns.cnki.net/kcms/detail/42.1676.tn.20211028.1541.027.html.

    NING H, DONG X J, LIU Q, et al. . Using point cloud normal vector to realize rock mass structural plane recognition[J/OL]. Geomatics and Information Science of Wuhan University, 1-13 [2022-06-07]. http://kns.cnki.net/kcms/detail/42.1676.tn.20211028.1541.027.html. (in Chinese)
    [16] 张溪溪, 纪小刚, 胡海涛, 等. 微型复杂曲面零件散乱点云特征点提取[J]. 机械设计与研究,2019,35(5):1-5,10. doi: 10.13952/j.cnki.jofmdr.2019.0263

    ZHANG X X, JI X G, HU H T, et al. Feature point extraction of scattered point cloud for complex micro-surface parts[J]. Machine Design &Research, 2019, 35(5): 1-5,10. (in Chinese) doi: 10.13952/j.cnki.jofmdr.2019.0263
    [17] 赵夫群, 耿国华. 基于图像特征和奇异值分解的点云配准算法[J]. 激光与光电子学进展,2020,57(10):101101.

    ZHAO F Q, GENG G H. Point cloud registration algorithm based on image feature and singular value decomposition[J]. Laser &Optoelectronics Progress, 2020, 57(10): 101101. (in Chinese)
    [18] 谭国威, 伍吉仓. 基于FPFH特征的三维点云配准方法研究[J]. 工程勘察,2022,50(4):52-56. doi: 10.3969/j.issn.1000-1433.2022.4.gckc202204010

    TAN G W, WU J C. Research on 3D point cloud registration based on FPFH features[J]. Geotechnical Investigation &Surveying, 2022, 50(4): 52-56. (in Chinese) doi: 10.3969/j.issn.1000-1433.2022.4.gckc202204010
    [19] 秦红星, 刘镇涛, 谭博元. 深度学习刚性点云配准前沿进展[J]. 中国图象图形学报,2022,27(2):329-348. doi: 10.11834/jig.210556

    QIN H X, LIU ZH T, TAN B Y. Review on deep learning rigid point cloud registration[J]. Journal of Image and Graphics, 2022, 27(2): 329-348. (in Chinese) doi: 10.11834/jig.210556
    [20] ZHANG R ZH, LIU K. Research introduction of 3D measurement technology based on grating projection[J]. Proceedings of SPIE, 2021, 11887: 1188722.
    [21] MURTIYOSO A, GRUSSENMEYER P. Automatic point cloud noise masking in close range photogrammetry for buildings using AI-based semantic labelling[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2022, XLVI-2/W1-2022: 389-393. doi: 10.5194/isprs-archives-XLVI-2-W1-2022-389-2022
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  290
  • HTML全文浏览量:  172
  • PDF下载量:  218
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-07-09
  • 录用日期:  2022-11-03
  • 修回日期:  2022-09-06
  • 网络出版日期:  2022-11-22

目录

    /

    返回文章
    返回