引入曲面变分实现点云法矢一致性调整

doi:10.11947/j.AGCS.2018.20170494

测绘学报 ›› 2018, Vol. 47 ›› Issue (2): 275-280.doi: 10.11947/j.AGCS.2018.20170494

引入曲面变分实现点云法矢一致性调整

何华¹, 李宗春¹, 闫荣鑫², 杨再华², 阮焕立¹, 付永健¹

1. 信息工程大学地理空间信息学院, 河南郑州 450001;
2. 北京卫星环境工程研究所, 北京 100094

收稿日期:2017-09-01 修回日期:2017-11-27 出版日期:2018-02-20 发布日期:2018-03-02
通讯作者: 李宗春 E-mail:13838092876@139.com
作者简介:何华(1992-),男,硕士生,研究方向为点云数据曲面重建与精密工程测量。E-mail:805648221@qq.com
基金资助:
航天器高精度测量联合实验室基金（201501）

On the Consistent Normal Vector Adjustment of Point Cloud Using Surface Variation

HE Hua¹, LI Zongchun¹, YAN Rongxin², YANG Zaihua², RUAN Huanli¹, FU Yongjian¹

1. Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China;
2. Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China

Received:2017-09-01 Revised:2017-11-27 Online:2018-02-20 Published:2018-03-02
Supported by:
The Spacecraft High-precision Measuring Association Laboratory Foundation (No. 201501)

摘要/Abstract

摘要： 针对现有法矢一致性调整算法效率不高、准确度低的问题，引入曲面变分改善该类算法的性能。首先通过主成分分析法估算点云的法矢和曲面变分，然后用曲面变分区分平缓点与非平缓点。调整法矢时，采用缩小待调整法矢的搜索范围到k-邻域内和增加每次搜索时法矢传播个数的策略来提高效率；采用约束法矢传播方向的方法保证准确性。试验结果表明，该算法在平缓区域、尖锐特征区域和高曲率区域均能得到正确的法矢调整结果，且算法效率较已有方法更高。

关键词: 法矢一致性调整, 曲面变分, 主成分分析, k-邻域, 点云

Abstract: In order to improve the efficiency and accuracy of existing normal vector adjustment algorithms,a consistent normal vector adjustment algorithm using surface variation is proposed.Firstly,the normal vector and surface variation of point cloud are calculated using principal component analysis.Then,the points on flat or uneven area are distinguished based on surface variation.In the process of adjusting normal vector,the search scope is narrowed to k-nearest neighbors and the number of adjusted normal vector is increased to improve efficiency.The propagating direction of normal vector is restrained to insure accuracy.The experiments show that the proposed algorithm can always receive accurate result on flat region,feature condition and high curvature area,meanwhile,the proposed algorithm is more efficient than the existing algorithms.

Key words: consistent normal vector adjustment, surface variation, principal component analysis, k-nearest neighbors, point cloud

中图分类号:

P237

何华, 李宗春, 闫荣鑫, 杨再华, 阮焕立, 付永健. 引入曲面变分实现点云法矢一致性调整[J]. 测绘学报, 2018, 47(2): 275-280.

HE Hua, LI Zongchun, YAN Rongxin, YANG Zaihua, RUAN Huanli, FU Yongjian. On the Consistent Normal Vector Adjustment of Point Cloud Using Surface Variation[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(2): 275-280.

参考文献

[1] 陶海跻, 达飞鹏. 一种基于法向量的点云自动配准方法[J]. 中国激光, 2013, 40(8):179-184. TAO Haiji, DA Feipeng. Automatic Registration Algorithm for the Point Clouds Based on the Normal Vector[J]. Chinese Journal of Lasers, 2013, 40(8):179-184.
[2] 董震, 杨必胜. 车载激光扫描数据中多类目标的层次化提取方法[J]. 测绘学报, 2015, 44(9):980-987. DOI:10.11947/j.AGCS.2015.20140339. DONG Zhen, YANG Bisheng. Hierarchical Extraction of Multiple Objects from Mobile Laser Scanning Data[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9):980-987. DOI:10.11947/j.AGCS.2015.20140339.
[3] 张强, 李朝奎, 李俊晓, 等. 一种改进的基于法矢方向调整的平面点云分割方法[J]. 地理与地理信息科学, 2015, 31(1):45-48. ZHANG Qiang, LI Chaokui, LI Junxiao, et al. Planar Point Cloud Segmentation Based on the Weighted Average of Adjusted Normal Vector[J]. Geography and Geo-Information Science, 2015, 31(1):45-48.
[4] 方悄云, 吕东辉, 孙九爱. 改进的物体表面重建的三角网格法[J]. 应用科学学报, 2016, 34(2):145-153. FANG Qiaoyun, LV Donghui, SUN Jiuai. Improved Triangle Mesh Method for Surface Reconstruction from Normal Field[J]. Journal of Applied Sciences, 2016, 34(2):145-153.
[5] 朱德海, 郭浩, 苏伟. 点云库PCL学习教程[M]. 北京:北京航空航天大学出版社, 2012. ZHU Dehai, GUO Hao, SU Wei. The Learning Tutorial for Point Cloud Library PCL[M]. Beijing:Beihang University Press, 2012.
[6] HOPPE H, DEROSE T, DUCHAMP T, et al. Surface Reconstruction from Unorganized Points[J]. ACM SIGGRAPH Computer Graphics, 1992, 26(2):71-78.
[7] 贺美芳. 基于散乱点云数据的曲面重建关键技术研究[D]. 南京:南京航空航天大学, 2006. HE Meifang. Research on Key Technologies of Surfaces Reconstruction Based on Scattered Point Cloud Data[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2006.
[8] 肖天姿. 散乱点云的数据预处理及曲面重建研究[D]. 大连:大连海事大学, 2012. XIAO Tianzi. Research on Data Preprocessing and Surface Reconstruction of Scattered Point Cloud[D]. Dalian:Dalian Maritime University, 2012.
[9] 孟祥林, 何万涛, 赵灿, 等. 逆向工程中点云邻域搜索及法矢估算相关算法研究[J]. 制造技术与机床, 2009(2):44-47. MENG Xianglin, HE Wantao, ZHAO Can, et al. Point Cloud-nearest-neighbor Search and Normal Vector Estimation Relative Algorithm in Reverse Engineering[J]. Manufacturing Technology & Machine Tool, 2009(2):44-47.
[10] 何学铭. 点云模型的孔洞修补技术研究[D]. 南京:南京师范大学, 2013. HE Xueming. Research on Hole Filling Technology of Point Cloud Model[D]. Nanjing:Nanjing Normal University, 2013.
[11] 孙金虎. 点云模型分割与融合关键技术研究[D]. 南京:南京航空航天大学, 2013. SUN Jinhu. Research on Key Technologies of Point Cloud Segmentation and Fusion[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2013.
[12] 孙金虎, 周来水, 安鲁陵. 点云模型法矢调整优化算法[J]. 中国图象图形学报, 2013, 18(7):844-851. SUN Jinhu, ZHOU Laishui, AN Luling. Optimal Algorithm for Normal Adjustment of Point Clouds[J]. Journal of Image and Graphics, 2013, 18(7):844-851.
[13] 易倩宇. 高质量鲁棒曲面重建[D]. 杭州:浙江大学, 2014. YI Qianyu. High-quality and Robust Surface Reconstruction[D]. Hangzhou:Zhejiang University, 2014.
[14] 张剑清, 李彩林, 郭宝云. 基于切平面投影的散乱数据点快速曲面重建算法[J]. 武汉大学学报(信息科学版), 2011, 36(7):757-762. ZHANG Jianqing, LI Cailin, GUO Baoyun. A Fast Surface Reconstruction Algorithm for Unorganized Points Based on Tangent Plane Projection[J]. Geomatics and Information Science of Wuhan University, 2011, 36(7):757-762.
[15] 刘健. 可修正的点云一致定向[D]. 大连:大连理工大学, 2014. LIU Jian. Mendable Consistent Orientation of Point Clouds[D]. Dalian:Dalian University of Technology, 2014.
[16] SUN Jiuai, SMITH M, SMITH L, et al. Examining the Uncertainty of the Recovered Surface Normal in Three Light Photometric Stereo[J]. Image and Vision Computing, 2007, 25(7):1073-1079.
[17] LIU Shengjun, WANG C C L. Orienting Unorganized Points for Surface Reconstruction[J]. Computers & Graphics, 2010, 34(3):209-218.
[18] 曾峰, 钟治初, 杨通, 等. 基于SOM的散乱点云法矢计算[J]. 计算机工程, 2012, 38(8):287-290. ZENG Feng, ZHONG Zhichu, YANG Tong, et al. Scattered Point Cloud Normal Vector Calculation Based on SOM[J]. Computer Engineering, 2012, 38(8):287-290.
[19] MULLEN P, GOES F D, DESBRUN M, et al. Signing the Unsigned:Robust Surface Reconstruction from Raw Pointsets[J]. Computer Graphics Forum, 2010, 29(5):1733-1741.
[20] PAULY M, GROSS M, KOBBELT L P. Efficient Simplification of Point-sampled Surfaces[C]//Proceedings of the IEEE Visualization Conference. Boston, MA, USA:IEEE, 2002.
[21] PAULY M, KEISER R, KOBBELT L P, et al. Shape Modeling with Point-sampled Geometry[J]. ACM Transactions on Graphics (TOG), 2003, 22(3):641-650.
[22] LEVIN D. The Approximation Power of Moving Least-squares[J]. Mathematics of Computation, 1998, 67(224):1517-1531.
[23] AMENTA N, BERN M. Surface Reconstruction by Voronoi Filtering[J]. Discrete & Computational Geometry, 1999, 22(4):481-504.
[24] DEY T K, GOSWAMI S. Provable Surface Reconstruction from Noisy Samples[C]//Proceedings of the 12th Annual Symposium on Computational Geometry. New York:ACM, 2004:330-339.
[25] HUANG Hui, LI Dan, ZHANG Hao, et al. Consolidation of Unorganized Point Clouds for Surface Reconstruction[J]. ACM Transactions on Graphics, 2009, 28(5):1-7.

引入曲面变分实现点云法矢一致性调整

On the Consistent Normal Vector Adjustment of Point Cloud Using Surface Variation

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	杨必胜, 陈驰, 董震. 面向智能化测绘的城市地物三维提取[J]. 测绘学报, 2022, 51(7): 1476-1484.
[2]	刘春, 贾守军, 吴杭彬, 黄炜, 郑宁, 艾克然木·艾克拜尔. 点云场景认知模式——泛化点云[J]. 测绘学报, 2022, 51(4): 556-567.
[3]	方莉娜, 王爽, 赵志远, 付化胜, 陈崇成. 车载激光点云中交通标线自动分类与矢量化[J]. 测绘学报, 2021, 50(9): 1251-1265.
[4]	杨必胜, 韩旭, 董震. 适用于城市场景大规模点云语义标识的深度学习网络[J]. 测绘学报, 2021, 50(8): 1059-1067.
[5]	赵传, 郭海涛, 卢俊, 余东行, 林雨准, 姜怀刚. 结合区域增长与RANSAC的机载LiDAR点云屋顶面分割[J]. 测绘学报, 2021, 50(5): 621-633.
[6]	黄远程, 薛园园, 李朋飞. 高光谱影像子空间分析孤立森林异常目标探测方法[J]. 测绘学报, 2021, 50(3): 416-425.
[7]	蒋腾平, 王永君, 张林淇, 梁冲, 孙剑. 融合CNN和MRF的激光点云层次化语义分割方法[J]. 测绘学报, 2021, 50(2): 215-225.
[8]	李帅鑫, 李九人, 田滨, 陈龙, 王力, 李广云. 面向点云退化的隧道环境的无人车激光SLAM方法[J]. 测绘学报, 2021, 50(11): 1487-1499.
[9]	方莉娜, 沈贵熙, 游志龙, 郭迎亚, 付化胜, 赵志远, 陈崇成. 融合点云和多视图的车载激光点云路侧多目标识别[J]. 测绘学报, 2021, 50(11): 1558-1573.
[10]	孙喜亮, 关宏灿, 苏艳军, 徐光彩, 郭庆华. 面向高精度城市测绘的激光紧耦合SLAM方法[J]. 测绘学报, 2021, 50(11): 1585-1593.
[11]	史鹏程, 叶勤, 张绍明, 邓海峰. 顾及室内场景特征的多线激光雷达初始定位[J]. 测绘学报, 2021, 50(11): 1594-1604.
[12]	李彩林, 王志勇, 俞路路, 郭宝云. 最邻近曲面约束的近景光学影像与地面激光点云几何配准[J]. 测绘学报, 2020, 49(8): 1014-1022.
[13]	麻卫峰, 王成, 王金亮, 周京春, 麻源源. 激光点云输电线精细提取的残差聚类法[J]. 测绘学报, 2020, 49(7): 883-892.
[14]	詹总谦, 胡孟琦, 满益云. 多尺度区域生长点云滤波地表拟合法[J]. 测绘学报, 2020, 49(6): 757-766.
[15]	姚连璧, 秦长才, 张邵华, 陈启超, 阮东旭, 聂顺根. 车载激光点云的道路标线提取及语义关联[J]. 测绘学报, 2020, 49(4): 480-488.