基于面提取的三维岩体点云孔洞检测与修复方法

doi:10.7523/j.ucas.2020.0032

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (5): 668-676.DOI: 10.7523/j.ucas.2020.0032

• 电子信息与计算机科学 • 上一篇下一篇

基于面提取的三维岩体点云孔洞检测与修复方法

马曌月, 肖俊, 王颖

中国科学院大学人工智能学院, 北京 100049

收稿日期:2020-04-10 修回日期:2020-05-11 发布日期:2021-06-01
通讯作者: 肖俊
基金资助:
中国科学院前沿科学重点研究项目（QYZDY-SSW-SYS004）、北京市科技计划课题（Z181100003818019）和中国科学院战略性先导科技专项（A类）（XDA23090304）资助

3D rock mass point cloud holes detection and filling method based on plane extraction

MA Zhaoyue, XIAO Jun, WANG Ying

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-04-10 Revised:2020-05-11 Published:2021-06-01

摘要/Abstract

摘要： 在岩体工程中，由于扫描测量角度、障碍物的阴影和遮挡等因素，使用激光扫描仪扫描得到的岩体点云数据往往包含孔洞，影响后续三维重建的效果。现有的修复方法主要针对规则的点云数据，依据孔洞邻域信息对点云孔洞进行修复，对岩体点云孔洞的检测与修复效果欠佳，且效率低。从岩体点云数据特征出发，提出一种基于平面提取的岩体点云孔洞检测与修复算法。首先，应用一种优化的区域生长算法对岩体点云进行平面提取，然后遍历所有点云并检索其k邻域点集，将其映射至对应平面，计算邻域夹角，实现孔洞检测；最后将点云孔洞根据边界点集的对应平面数量进行分类，在对应平面上新增采样点实现点云孔洞修复。本算法通过平面提取实现了点云数据的去噪和平面拟合过程，简化后续的孔洞修复流程，降低时间复杂度。实验结果表明，与已有算法相比，本算法对大型不规则岩体点云孔洞的检测、修复准确率和运行效率更高，修复效果更佳。

关键词: 岩体点云, 孔洞检测, 孔洞修复, 平面提取

Abstract: In rock engineering, the rock point cloud data scanned by a laser scanner always contains holes because of the scanning measurement angle, shadow, occlusion of obstacles, and other factors, which will affect the result of subsequent 3D reconstruction. The existing filling methods mainly focus on the regular point cloud data, and the point cloud hole is filled according to the neighborhood information of the holes, and the experiment result of rock point cloud holes detection and filling are not effective and low efficiency. In this paper, we propose an algorithm for detecting and filling rock point cloud holes based on the plane extraction leverage the characteristics of rock point cloud data. Firstly, an optimized region growing algorithm is applied to extract the plane of the rock point cloud. Then, we traverse all point clouds and retrieve their K-neighborhood point sets. These points are mapped to the corresponding plane, and we calculate the neighborhood angle to detect holes. Finally, we classify the point cloud holes according to the number of corresponding planes of the boundary point set, and the point cloud holes are filled by adding sampling points on the corresponding planes. Our algorithm realizes the process of denoising and plane fitting of point cloud data by plane extraction, simplifys the subsequent hole filling process and reduces the time complexity. Experimental results demonstrate that our algorithm has a higher accuracy of detection and filling, higher operation efficiency, and better filling result for rock-mass point cloud compared to the state-of-the-art approaches.

Key words: rock mass point cloud, holes detection, holes filling, plane extraction

中图分类号:

TP391.41

马曌月, 肖俊, 王颖. 基于面提取的三维岩体点云孔洞检测与修复方法[J]. 中国科学院大学学报, 2022, 39(5): 668-676.

MA Zhaoyue, XIAO Jun, WANG Ying. 3D rock mass point cloud holes detection and filling method based on plane extraction[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(5): 668-676.

参考文献

[1] Pu S, Vosselman G. Automatic extraction of building features from terrestrial laser scanning[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2006, 36(5):25-27.
[2] Kotthäuser T, Mertsching B. Triangulation-based plane extraction for 3D point clouds[C]//Intelligent Robotics and Applications (ICIRA 2012). Springer, Berlin, Heidelberg, 2012:217-228.DOI:10.1007/978-3-642-33509-9_21.
[3] Biosca J M, Lerma J L. Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 63(1):84-98.DOI:10.1016/j.isprsjprs.2007.07.010.
[4] 胡志胜. 三维激光扫描点云边界检测和孔洞修补技术研究[D].徐州:中国矿业大学,2016.
[5] Guo X Y, Xiao J, Wang Y. A survey on algorithms of hole filling in 3D surface reconstruction[J]. The Visual Computer, 2018, 34(1):93-103.DOI:10.1007/s00371-016-1316-y.
[6] 陈飞舟, 陈志杨, 丁展,等. 基于径向基函数的残缺点云数据修复[J]. 计算机辅助设计与图形学学报, 2006,18(9):130-135.DOI:10.3321/j.issn:1003-9775.2006.09.023.
[7] 陈义仁, 王一宾, 彭张节,等.一种改进的散乱点云边界特征点提取算法[J]. 计算机工程与应用, 2012, 48(23):177-180,190.DOI:10.3778/j.issn.1002-8331.2012.23.040.
[8] Bendels G H, Schnabel R, Klein R. Detecting holes in point set surfaces[J]. Journal of WSCG, 2006, 14(1-3):89-96.
[9] Ztireli A C, Guennebaud G, Gross M. Feature preserving point set surfaces based on non-linear kernel regression[J]. Computer Graphics Forum, 2009, 28(2):493-501.DOI:10.1111/j.1467-8659.2009.01388.x.
[10] Ju T. Robust repair of polygonal models[J]. ACM Transactions on Graphics, 2004, 23(3):888-895.DOI:10.1145/1015706.1015815.
[11] Kallis C, Deliparaschos K M, Moustris G P, et al. Incremental 2D Delaunay triangulation core implementation on FPGA for surface reconstruction via high-level synthesis[C]//201722^nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). September 12-15, 2017, Limassol, Cyprus. IEEE, 2017:1-4. DOI:10.1109/ETFA.2017.8247736.
[12] Boltcheva D, Lévy B.Surface reconstruction by computing restricted Voronoi cells in parallel[J]. Computer-Aided Design, 2017,90(1):123-134.DOI:10.1016/j.cad.2017.05.011.
[13] Foks N L,Li Y G. Automatic boundary extraction from magnetic field data using triangular meshes[J]. GEOPHYSICS, 2016,81(3):147-160.DOI:10.1190/geo2015-0112.1.
[14] 刘震,王艳宾,白丽丽,等. 曲面细节特征保持的三维模型孔洞修复方法[J]. 计算机辅助设计与图形学学报, 2016,28(12):2052-2059.
[15] Jun Y. A piecewise hole filling algorithm in reverse engineering[J]. Computer-Aided Design, 2005, 37(2):263-270.DOI:10.1016/j.cad.2004.06.012.
[16] Altantsetseg E, Khorloo O, Matsuyama K, et al. Complex hole-filling algorithm for 3D models[C]//Proceedings of the Computer Graphics International Conference(CGI'17). ACM,Yokohama,Japan.2017:1-6.DOI:10.1145/3095140.3095150.
[17] 张琦, 蔺素珍, 白佳璐,等. 基于改进曲线收缩流方法的点云张开孔洞的虚拟修补[J]. 激光与光电子学进展, 2018, 55(9):119-125.DOI:10.3788/LOP55.091002.
[18] 孟庆年, 郑德华, 夏佳毅. 一种基于曲线拟合方法在建筑物垂直面点云孔洞修复中的应用[J]. 勘察科学技术, 2015(1):40-43.DOI:10.3969/j.issn.1001-3946.2015.01.009.
[19] Hai T T. Filling holes in an elevation surface structured by a sparse 3D volume[D]. Ho Chi Ming City, Vietnam:International University HCMC, 2015.
[20] Xie H, McDonnell K T, Qin H. Surface reconstruction of noisy and defective data sets[C]//IEEE Visualization 2004. October 10-15,2004,Austin, TX, USA. IEEE, 2004:259-266.DOI:10.1109/VISUAL.2004.101.
[21] 王春香, 梁亮, 王耀,等. 基于优化BP神经网络的点云孔洞修补效果比较分析[J]. 机床与液压, 2019,47(16):30-33,69.DOI:10.3969/j.issn.1001-3881.2019.16.007.
[22] Hu L, Xiao J, Wang Y. Efficient and automatic plane detection approach for 3-D rock mass point clouds[J]. Multimedia Tools and Applications, 2020,79(1/2):839-864.DOI:10.1007/s11042-019-08189-6.

基于面提取的三维岩体点云孔洞检测与修复方法

3D rock mass point cloud holes detection and filling method based on plane extraction

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

访问统计

联系我们

[1]	张韬, 肖俊, 王颖. 基于匹配点逐层过滤的岩体点云配准方法[J]. 中国科学院大学学报, 2022, 39(3): 352-359.
[2]	胡亮, 肖俊, 王颖. 基于多维度特征和MLP的岩体点云植被滤波方法[J]. 中国科学院大学学报, 2020, 37(3): 345-351.