首页 > 论文 > 激光与光电子学进展 > 57卷 > 20期(pp:202802--1)

基于三维激光点云特征线提取的溶洞多分辨率三维重建方法研究

Multi-Resolution 3D Reconstruction of Karst Caves Based on the Feature Line Extraction of 3D Laser Point Cloud

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

溶洞表面具有复杂、不规则性,现有的诸多的建模方法都是按照统一分辨率来进行三维重建,但是在三维重建过程中效率很低,且模型文件过大,对后续专业应用造成了很大困难,因此提出了一种基于三维激光点云特征线提取的技术,并针对溶洞进行了多分辨率三维重建。首先,采用改进邻近点几何特征提取溶洞特征值,增加法向量角作为检测特征点的参数;其次,用社会粒子群(SPSO)算法与模糊C-均值(FCM)聚类算法实现点云分类;再次,采用折线生长方法将特征点连接成特征线,并将其投影到三维点云上;最后,利用分类后的点云按照不同分辨率建模,实现高精度、高质量、高效率溶洞三维重建。实验结果表明,该方法可以按照不同分辨率进行建模,减少了三维重建后模型的数据量,提高了三维重建效率,在溶洞三维重建方面具有较高的实用价值。

Abstract

The surfaces of caves are complex and irregular. Many existing modeling methods are based on an overall-resolution 3D reconstruction. Although the overall model resolution is guaranteed, the efficiency of 3D reconstruction is substantially low, and the model file is too large, which makes it considerably difficult for the follow-up professional application. Therefore, a multi-resolution 3D reconstruction technology based on 3D laser point cloud feature line extraction was proposed for the karst cave. First, the enhanced geometric features of neighboring points were used to extract the eigenvalues and increased the normal vector angle as a basis for detecting the feature points. Second, the standard particle swarm optimization(SPSO) and fuzzy C-means clustering algorithms were used to realize the point cloud classification. Third, the broken line growth method was used to connect the feature points into the feature lines and project them onto 3D point cloud; finally, the classified point cloud was used for modeling as per different resolutions. The 3D reconstruction of the karst cave was realized with high precision, high quality, and high efficiency. The experimental results show that this method can improve the efficiency of the 3D reconstruction of karst caves, realize modeling according to different resolutions, reduce the amount of data after the 3D reconstruction, improve the efficiency of 3D reconstruction, and has high practical value.

广告组1 - 空间光调制器+DMD
补充资料

中图分类号:TP79

DOI:10.3788/LOP57.202802

所属栏目:遥感与传感器

基金项目:国家自然科学基金、宜良九乡风景区三角洞洞内三维建模测绘、三维激光扫描和室内定位技术在地下空间信息化中的应用研究;

收稿日期:2020-01-03

修改稿日期:2020-02-10

网络出版日期:2020-10-01

作者单位    点击查看

柏宏强:昆明理工大学国土资源工程学院, 云南 昆明 650093云南省高校高原山区空间信息测绘技术应用工程研究中心, 云南 昆明 650093
夏永华:昆明理工大学国土资源工程学院, 云南 昆明 650093云南省高校高原山区空间信息测绘技术应用工程研究中心, 云南 昆明 650093
杨明龙:昆明理工大学国土资源工程学院, 云南 昆明 650093云南省高校高原山区空间信息测绘技术应用工程研究中心, 云南 昆明 650093
李照永:昆明市城市地下空间规划管理办公室, 云南 昆明 650011
黄德:昆明理工大学国土资源工程学院, 云南 昆明 650093云南省高校高原山区空间信息测绘技术应用工程研究中心, 云南 昆明 650093

联系人作者:夏永华(617073761@qq.com); 杨明龙(617073761@qq.com);

备注:国家自然科学基金、宜良九乡风景区三角洞洞内三维建模测绘、三维激光扫描和室内定位技术在地下空间信息化中的应用研究;

【1】Zhang Y H, Geng G H, Wei X R, et al. A statistical approach for extraction of feature lines from point clouds [J]. Computers & Graphics. 2016, 56: 31-45.Zhang Y H, Geng G H, Wei X R, et al. A statistical approach for extraction of feature lines from point clouds [J]. Computers & Graphics. 2016, 56: 31-45.

【2】Altantsetseg E, Muraki Y, Matsuyama K, et al. Feature line extraction from unorganized noisy point clouds using truncated Fourier series [J]. The Visual Computer. 2013, 29: 617-626.Altantsetseg E, Muraki Y, Matsuyama K, et al. Feature line extraction from unorganized noisy point clouds using truncated Fourier series [J]. The Visual Computer. 2013, 29: 617-626.

【3】Fu S Y, Wu L S. Feature line extraction from point clouds based on geometric structure of point space [J]. 3D Research. 2019, 10(2): 16.Fu S Y, Wu L S. Feature line extraction from point clouds based on geometric structure of point space [J]. 3D Research. 2019, 10(2): 16.

【4】Zhou W, Peng R C, Dong J, et al. Automated extraction of 3D vector topographic feature line from terrain point cloud [J]. Geocarto International. 2018, 33(10): 1036-1047.

【5】He T, Xiong F G, Han X, et al. A feature curve extraction algorithm for point cloud based on covariance matrix [J]. Computer Engineering. 2018, 44(3): 275-280, 286.
贺彤, 熊风光, 韩燮, 等. 一种基于协方差矩阵的点云特征曲线提取算法 [J]. 计算机工程. 2018, 44(3): 275-280, 286.

【6】He T, Xiong F G, Han X, et al. tp . 20170719.1047.002.html. 1289.
贺彤, 熊风光, 韩燮, 等. tp . 20170719.1047.002.html. 1289.

【7】Dong W. Feature extraction of the building point cloud by using geometrical characteristics of adjacent points [J]. Laser & Optoelectronics Progress. 2018, 55(7): 071006.
董伟. 利用邻近点几何特征实现建筑物点云特征提取 [J]. 激光与光电子学进展. 2018, 55(7): 071006.

【8】Chen P, Tan Y W, Li L. Extraction of building''''s feature lines based on 3-D terrestrial laser scanning [J]. Laser Journal. 2016, 37(3): 9-11.
陈朋, 谭晔汶, 李亮. 地面三维激光扫描建筑物点云特征线提取 [J]. 激光杂志. 2016, 37(3): 9-11.

【9】Wang X H, Wu L S, Chen H W, et al. Feature line extraction from a point cloud based on region clustering segmentation [J]. Acta Optica Sinica. 2018, 38(11): 1110001.
王晓辉, 吴禄慎, 陈华伟, 等. 基于区域聚类分割的点云特征线提取 [J]. 光学学报. 2018, 38(11): 1110001.

【10】Wang X H, Wu L S, Chen H W, et al. Region segmentation of point cloud data based on improved particle swarm optimization fuzzy clustering [J]. Optics and Precision Engineering. 2017, 25(4): 1095-1105.
王晓辉, 吴禄慎, 陈华伟, 等. 应用改进的粒子群优化模糊聚类实现点云数据的区域分割 [J]. 光学精密工程. 2017, 25(4): 1095-1105.

【11】Huang M, Ma C S, Yang F, et al. Theory and method of surface laser point cloud processing and fine construction[D]. Beijing: Science Press, 2016.
黄明, 马朝帅, 杨芳, 等. 地面激光点云处理与精细构建理论与方法[D]. 北京: 科学出版社, 2016.

【12】Daniels J, Ha L K, Ochotta T, et al. Robust smooth feature extraction from point clouds . [C]∥IEEE International Conference on Shape Modeling and Applications 2007 (SMI''''07) , June 13-15, 2007, Lyon, France. New York: IEEE. 2007, 9855058.

【13】Liu Q, Geng G H, Zhou M Q, et al. Algorithm for feature line extraction based on 3D point cloud models [J]. Application Research of Computers. 2013, 30(3): 933-937.
刘倩, 耿国华, 周明全, 等. 基于三维点云模型的特征线提取算法 [J]. 计算机应用研究. 2013, 30(3): 933-937.
Liu Q, Geng G H, Zhou M Q, et al. Algorithm for feature line extraction based on 3D point cloud models [J]. Application Research of Computers. 2013, 30(3): 933-937.
刘倩, 耿国华, 周明全, 等. 基于三维点云模型的特征线提取算法 [J]. 计算机应用研究. 2013, 30(3): 933-937.

【14】Bai H Q, Xia Y H, Yang M L, et al. 3D modeling and mapping technology and application in the development of large karst cave tourism [J]. Software Guide. 2019, 18(6): 138-142.
柏宏强, 夏永华, 杨明龙, 等. 大型溶洞旅游开发中三维建模测绘技术及应用 [J]. 软件导刊. 2019, 18(6): 138-142.

【15】Huang D, Xia Y H, Bai H Q, et al. Urban tree feature extraction based on 3D laser scanning technology Urban Geotechnical Investigation & Surveying[J]. 0, 2019(3): 92-95, 99.
黄德, 夏永华, 柏宏强, 等. 基于三维激光扫描技术的城市树木特征提取 城市勘测[J]. 0, 2019(3): 92-95,99.

【16】Nie J H, Liu Y, Gao H, et al. Feature line detection from point cloud based on signed surface variation and region segmentation [J]. Journal of Computer-Aided Design & Computer Graphics. 2015, 27(12): 2332-2339.
聂建辉, 刘烨, 高浩, 等. 基于符号曲面变化度与特征分区的点云特征线提取算法 [J]. 计算机辅助设计与图形学学报. 2015, 27(12): 2332-2339.

【17】Yang B S, Wei Z, Li Q Q, et al. A classification-oriented method of feature image generation for vehicle-borne laser scanning point clouds [J]. Acta Geodaetica et Cartographica Sinica. 2010, 39(5): 540-545.
杨必胜, 魏征, 李清泉, 等. 面向车载激光扫描点云快速分类的点云特征图像生成方法 [J]. 测绘学报. 2010, 39(5): 540-545.

引用该论文

Bai Hongqiang,Xia Yonghua,Yang Minglong,Li Zhaoyong,Huang De. Multi-Resolution 3D Reconstruction of Karst Caves Based on the Feature Line Extraction of 3D Laser Point Cloud[J]. Laser & Optoelectronics Progress, 2020, 57(20): 202802

柏宏强,夏永华,杨明龙,李照永,黄德. 基于三维激光点云特征线提取的溶洞多分辨率三维重建方法研究[J]. 激光与光电子学进展, 2020, 57(20): 202802

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF