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

基于曲率特征的文物点云分类降采样与配准方法

朱婧怡 杨鹏程 孟杰 张津京 崔嘉宝 代阳

朱婧怡, 杨鹏程, 孟杰, 张津京, 崔嘉宝, 代阳. 基于曲率特征的文物点云分类降采样与配准方法[J]. 中国光学(中英文), 2024, 17(3): 572-579. doi: 10.37188/CO.2023-0115
引用本文: 朱婧怡, 杨鹏程, 孟杰, 张津京, 崔嘉宝, 代阳. 基于曲率特征的文物点云分类降采样与配准方法[J]. 中国光学(中英文), 2024, 17(3): 572-579. doi: 10.37188/CO.2023-0115
ZHU Jing-yi, YANG Peng-cheng, MENG Jie, ZHANG Jin-jing, CUI Jia-bao, DAI Yang. A point cloud classification downsampling and registration method for cultural relics based on curvature features[J]. Chinese Optics, 2024, 17(3): 572-579. doi: 10.37188/CO.2023-0115
Citation: ZHU Jing-yi, YANG Peng-cheng, MENG Jie, ZHANG Jin-jing, CUI Jia-bao, DAI Yang. A point cloud classification downsampling and registration method for cultural relics based on curvature features[J]. Chinese Optics, 2024, 17(3): 572-579. doi: 10.37188/CO.2023-0115

基于曲率特征的文物点云分类降采样与配准方法

doi: 10.37188/CO.2023-0115
基金项目: 陕西省自然科学基础研究计划——面上项目(No. 2022JM-219);陕西省教育厅专项科研计划(No. 22JK0404)
详细信息
    作者简介:

    杨鹏程(1985—),河南南阳人,男,博士,副教授,2013年于西安交通大学获得工学博士学位,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。E-mail:yangpengcheng@xpu.edu.cn

  • 中图分类号: TP394.1;TH691.9

A point cloud classification downsampling and registration method for cultural relics based on curvature features

Funds: Supported by Basic Research Program of Shaanxi Province - Surface Project (No. 2022JM-219); Special Research Program of Shaanxi Education Department (No. 22JK0404)
More Information
  • 摘要:

    三维重构是文物数字化的关键技术,其中三维点云配准精度是评估重构质量优劣的重要指标之一。实际采样中,文物点云细节信息繁多,传统降采样后易出现细节缺失从而影响配准精度。为了解决这一问题,本文提出了一种基于曲率特征的文物点云分类降采样与配准方法。首先,通过线性矩阵激光测量获取文物的三维点云数据。其次,计算所有点的曲率值,并设置曲率阈值进行点云分类,不同点集按照其特征属性进行不同权重的降采样,从而最大限度地保留点云的形态特征和细节信息。最后,通过求解刚性变换模型实现点云配准。点云配准前的降采样处理后点云数据降至原始点云的1/3,与传统的整体降采样ICP方法相比,平均距离从0.89 mm约降至0.59 mm,标准偏差从0.29 mm约降至0.18 mm。在降低点云数据的同时也保证了配准的精度,适用于不同类型的文物点云数据。

     

  • 图 1  点云分类与降采样方法流程图

    Figure 1.  Flowchart of point cloud classification and downsampling methods

    图 2  文物雕像实物

    Figure 2.  Cultural relics and physical statues

    图 3  扫描系统实物图

    Figure 3.  Physical picture of scanning system

    图 4  原始点云图

    Figure 4.  Original point cloud diagrams

    图 5  特征点提取示意图

    Figure 5.  Schematic diagram of feature point extraction

    图 6  本文方法与传统ICP方法点云图对比

    Figure 6.  Comparison of point cloud maps of proposed method and the traditional ICP method

    表  1  本文分类降采样数据

    Table  1.   The classification downsampling data of this paper

    原始点云数量配准后点云数量平均距离/mm标准偏差/mm
    1950581422423152816575239
    下载: 导出CSV

    表  2  仿真铜像点云配准实验过程数据分析

    Table  2.   Experiment process data analysis of point cloud registration for simulated copper statue

    方法 原始点云
    数量
    配准后点云
    数量
    平均距离
    /mm
    标准偏差
    /mm
    传统整体
    降采样后
    3471705 985621 0.891086 0.296167
    本文分类
    降采样后
    3471705 981584 0.591977 0.180786
    下载: 导出CSV
  • [1] 阎春生, 黄晨, 韩松涛, 等. 古代纸质文物科学检测技术综述[J]. 中国光学,2020,13(5):936-964. doi: 10.37188/CO.2020-0010

    YAN CH SH, HUANG CH, HAN S T, et al. Review on scientific detection technologies for ancient paper relics[J]. Chinese Optics, 2020, 13(5): 936-964. (in Chinese). doi: 10.37188/CO.2020-0010
    [2] 张瑞, 骆岩林, 周明全, 等. 文物数字化的关键技术[J]. 北京师范大学学报(自然科学版),2007,43(2):150-153.

    ZHANG R, LUO Y L, ZHOU M Q, et al. The key technology in digital cultural relics[J]. Journal of Beijing Normal University (Natural Science), 2007, 43(2): 150-153. (in Chinese).
    [3] 陈辉, 马世伟, NUECHTER A. 基于激光扫描和SFM的非同步点云三维重构方法[J]. 仪器仪表学报,2016,37(5):1148-1157.

    CHEN H, MA SH W, NUECHTER A. Non-synchronous point cloud algorithm for 3D reconstruction based on laser scanning and SFM[J]. Chinese Journal of Scientific Instrument, 2016, 37(5): 1148-1157. (in Chinese).
    [4] 王蕊, 李俊山, 刘玲霞, 等. 基于几何特征的点云配准算法[J]. 华东理工大学学报(自然科学版),2009,35(5):768-773.

    WANG R, LI J SH, LIU L X, et al. Registration of point clouds based on geometric properties[J]. Journal of East China University of Science and Technology (Natural Science Edition), 2009, 35(5): 768-773. (in Chinese).
    [5] 张新荣, 王鑫, 王瑶, 等. 基于转动式二维激光扫描仪和多传感器的三维重建方法[J]. 中国光学(中英文),2023,16(3):663-672. doi: 10.37188/CO.2022-0159

    ZHANG X R, WANG X, WANG Y, et al. 3D reconstruction method based on a rotating 2D laser scanner and multi-sensor[J]. Chinese Optics, 2023, 16(3): 663-672. (in Chinese). doi: 10.37188/CO.2022-0159
    [6] 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
    [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] ZHAO H, ZHANG Y J, ZHANG L, et al. Fast color point cloud registration based on virtual viewpoint image[J]. Frontiers in Physics, 2022, 10: 1026517. doi: 10.3389/fphy.2022.1026517
    [9] 毕勇, 潘鸣奇, 张硕, 等. 三维点云数据超分辨率技术[J]. 中国光学,2022,15(2):210-223. doi: 10.37188/CO.2021-0176

    BI Y, PAN M Q, ZHANG SH, et al. Overview of 3D point cloud super-resolution technology[J]. Chinese Optics, 2022, 15(2): 210-223. (in Chinese). doi: 10.37188/CO.2021-0176
    [10] 伍济钢, 马佳康, 杨康, 等. 基于改进ICP的复杂机械零件测量点云配准方法[J]. 光电子·激光,2023,34(6):620-627. doi: 10.16136/j.joel.2023.06.0337

    WU J G, MA J K, YANG K, et al. Measurement point cloud registration method for complex mechanical parts based on improved ICP[J]. Journal of Optoelectronics·Laser, 2023, 34(6): 620-627. (in Chinese). doi: 10.16136/j.joel.2023.06.0337
    [11] QIN H X, ZHANG Y CH, LIU ZH T, et al. Rigid registration of point clouds based on partial optimal transport[J]. Computer Graphics Forum, 2022, 41(6): 365-378. doi: 10.1111/cgf.14614
    [12] ZHANG K X, CHEN H, WU H, et al. Point cloud registration method for maize plants based on conical surface fitting—ICP[J]. Scientific Reports, 2022, 12(1): 6852. doi: 10.1038/s41598-022-10921-6
    [13] 张彬, 熊传兵. 基于体素下采样和关键点提取的点云自动配准[J]. 激光与光电子学进展,2020,57(4):041008.

    ZHANG B, XIONG CH B. Automatic point cloud registration based on voxel downsampling and key point extraction[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041008. (in Chinese).
    [14] GARLAND M, HECKBERT P S. Surface simplification using quadric error metrics[C]. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, ACM, 1997: 209-216.
    [15] SU H, JAMPANI V, SUN D Q, et al. SPLATNet: sparse lattice networks for point cloud processing[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 2530-2539.
    [16] 汪千金, 崔海华, 张益华, 等. 面向光学测量跨源点云的多尺度采样配准方法[J]. 光学学报,2022,42(10):1015002. doi: 10.3788/AOS202242.1015002

    WANG Q J, CUI H H, ZHANG Y H, et al. Multi-scale sampling registration method for optical measurement of cross-source point clouds[J]. Acta Optica Sinica, 2022, 42(10): 1015002. (in Chinese). doi: 10.3788/AOS202242.1015002
    [17] LU J, WANG ZH, HUA B W, et al. Automatic point cloud registration algorithm based on the feature histogram of local surface[J]. PLoS One, 2020, 15(9): e0238802. doi: 10.1371/journal.pone.0238802
    [18] CHEN Y W, ZHOU L D, TANG Y, et al. Fast neighbor search by using revised k-d tree[J]. Information Sciences, 2019, 472: 145-162. doi: 10.1016/j.ins.2018.09.012
    [19] 金泽芬芬, 侯志强, 余旺盛, 等. 基于协方差矩阵的多特征融合跟踪算法[J]. 光学学报,2017,37(9):0915005. doi: 10.3788/AOS201737.0915005

    JIN Z F F, HOU ZH Q, YU W SH, et al. Multi-feature fusion tracking algorithm based on the covariance matrix[J]. Acta Optica Sinica, 2017, 37(9): 0915005. (in Chinese). doi: 10.3788/AOS201737.0915005
    [20] ILEA I, BOMBRUN L, TEREBES R, et al. An M-estimator for robust centroid estimation on the manifold of covariance matrices[J]. IEEE Signal Processing Letters, 2016, 23(9): 1255-1259. doi: 10.1109/LSP.2016.2594149
    [21] FU Y J, LI Z CH, DENG Y, et al. Pairwise registration for terrestrial laser scanner point clouds based on the covariance matrix[J]. Remote Sensing Letters, 2021, 12(8): 788-798. doi: 10.1080/2150704X.2021.1938734
    [22] WANG X H, CHEN H W, WU L SH. Feature extraction of point clouds based on region clustering segmentation[J]. Multimedia Tools and Applications, 2020, 79(17-18): 11861-11889. doi: 10.1007/s11042-019-08512-1
    [23] 李韦童, 邓念武. 一种预拼装钢构件的点云自动分割算法[J]. 武汉大学学报(工学版),2022,55(3):247-252. doi: 10.14188/j.1671-8844.2022-03-005

    LI W T, DENG N W. An automatic point cloud data segmentation algorithm for pre-assembled steel structures[J]. Engineering Journal of Wuhan University, 2022, 55(3): 247-252. (in Chinese). doi: 10.14188/j.1671-8844.2022-03-005
    [24] 魏磊, 万帅, 王哲诚, 等. 面向点云无损压缩的快速细节层次优化方法[J]. 西安交通大学学报,2021,55(9):88-96.

    WEI L, WAN SH, WANG ZH CH, et al. Optimization method for level of detail of lossless point cloud compression[J]. Journal of Xi'an Jiaotong University, 2021, 55(9): 88-96. (in Chinese).
    [25] 郭培闪, 杜黎明. 运用Geomagic Studio实现点云数据的曲面重建及误差分析[J]. 地理信息世界,2015,22(1):57-60. doi: 10.3969/j.issn.1672-1586.2015.01.016

    GUO P SH, DU L M. Realized the surface reconstruction of point clouds and error analysis by using the Geomagic Studio[J]. Geomatics World, 2015, 22(1): 57-60. (in Chinese). doi: 10.3969/j.issn.1672-1586.2015.01.016
    [26] 戴静兰, 陈志杨, 叶修梓. ICP算法在点云配准中的应用[J]. 中国图象图形学报,2007,12(3):517-521.

    DAI J L, CHEN ZH Y, YE X Z. The application of ICP algorithm in point cloud alignment[J]. Journal of Image and Graphics, 2007, 12(3): 517-521. (in Chinese).
    [27] SOUZA NETO P, MARQUES SOARES J, PEREIRA THÉ G A. Uniaxial partitioning strategy for efficient point cloud registration[J]. Sensors, 2022, 22(8): 2887. doi: 10.3390/s22082887
  • 加载中
图(6) / 表(2)
计量
  • 文章访问数:  217
  • HTML全文浏览量:  77
  • PDF下载量:  33
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-07-11
  • 修回日期:  2023-08-22
  • 网络出版日期:  2023-11-07

目录

    /

    返回文章
    返回

    重要通知

    2024年2月16日科睿唯安通过Blog宣布,2024年将要发布的JCR2023中,229个自然科学和社会科学学科将SCI/SSCI和ESCI期刊一起进行排名!《中国光学(中英文)》作为ESCI期刊将与全球SCI期刊共同排名!