Unified algorithm for scattered point cloud denoising and simplification

doi:10.11772/j.issn.1001-9081.2017.10.2879

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (10): 2879-2883.DOI: 10.11772/j.issn.1001-9081.2017.10.2879

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Unified algorithm for scattered point cloud denoising and simplification

ZHAO Jingdong, YANG Fenghua, GUO Yingxin

School of Mathematical Sciences, Qufu Normal University, Qufu Shandong 273165, China

Received:2017-05-02 Revised:2017-07-22 Online:2017-10-10 Published:2017-10-16
Supported by:
This work is partially supported by the Postdoctoral Science Foundation of China (2014M551738).

散乱点云去噪与简化的统一算法

赵京东, 杨凤华, 郭英新

曲阜师范大学数学科学学院, 山东曲阜 273165

通讯作者: 赵京东(1962-),男,山东莱州人,教授,主要研究方向:CAD、数字图像处理,E-mail:zhaojd@mail.qfnu.edu.cn
作者简介:赵京东(1962-),男,山东莱州人,教授,主要研究方向:CAD、数字图像处理;杨凤华(1962-),女,山东新泰人,副教授,硕士,主要研究方向:非线性泛函分析;郭英新(1969-),男,山东新泰人,副教授,博士,主要研究方向:控制理论、控制工程、神经网络.
基金资助:
中国博士后科学基金资助项目（2014M551738）。

Abstract

Abstract: Since it is difficult to denoise and simplify a three dimensional point cloud data by a same parameter, a new unified algorithm based on the Extended Surface Variation based Local Outlier Factor (ESVLOF) for denoising and simplification of scattered point cloud was proposed. Through the analysis of the definition of ESVLOF, its properties were given. With the help of the surface variability computed in denoising process and the default similarity coefficient, the parameter γ which decreased with the increase of surface variation was constructed. Then the parameter γ was used as local threshold for denoising and simplifying point cloud. The simulation results show that this method can preserve the geometric characteristics of the original data. Compared with traditional 3D point-cloud preprocessing, the efficiency of this method is nearly doubled.

Key words: scattered point cloud, surface variation, denoising, point cloud simplification

摘要： 针对三维点云去噪和简化很难用同一参数的问题，提出一种基于扩展的曲面变化度局部离群系数（ESVLOF）的散乱点云去噪与简化的统一算法。通过对ESVLOF定义的分析，给出了其性质。利用ESVLOF去噪过程中计算的曲面变化度和预设的相似度系数，构造出随曲面变化度增大而减小的参数γ，并将其作为点云简化的局部阈值，在点云去噪的同时进行点云简化。仿真结果显示，该方法能够保留原始数据的几何特征，与传统的三维点云预处理相比，效率提高近一倍。

关键词: 散乱点云, 曲面变化度, 去噪, 点云简化

CLC Number:

TP391.72

ZHAO Jingdong, YANG Fenghua, GUO Yingxin. Unified algorithm for scattered point cloud denoising and simplification[J]. Journal of Computer Applications, 2017, 37(10): 2879-2883.

赵京东, 杨凤华, 郭英新. 散乱点云去噪与简化的统一算法[J]. 计算机应用, 2017, 37(10): 2879-2883.

References

[1] WANG J, HAN J, PEI J. CLOSET+: searching for the best strategies for mining frequent closed item sets [C]//KDD 2003: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2003: 236-245.
[2] 陈西江, 章光, 花向红. 于法向量夹角信息熵的点云简化算法[J]. 中国激光, 2015, 42(8): 336-344. (CHEN X J, ZHANG G, HUA X H. Point cloud simplification based on the information entropy of normal vector angle[J]. Chinese Journal of Lasers, 2015, 42(8): 336-344.)
[3] HUR S M, KIM H C, LEE S H. STL file generation with data reduction by the delaunay triangulation method in reverse engineering[J]. The International Journal of Advanced Manufacturing Technology, 2002, 19(9): 669-678.
[4] 李国俊, 李宗春, 侯东兴. 基于Delaunay三角化的噪声点云非均匀采样[J]. 计算机应用, 2014, 34(10): 2922-2924, 2929. (LI G J, LI Z C, HOU D X. Delaunay-based non-uniform sampling for noisy point cloud[J]. Journal of Computer Applications, 2014, 34(10): 2922-2924, 2929.)
[5] LICHTI D D, GORDON S J. Error propagation in directly georeferenced terrestrial laser scanner point clouds for cultural heritage recording[EB/OL]. [2017-01-10]. https://www.fig.net/resources/proceedings/fig_proceedings/athens/papers/wsa2/WSA2_6_Lichti_Gordon.pdf.
[6] 张有亮, 刘建永, 付成群, 等. 新的点云数据简化存储方法[J]. 计算机应用, 2011, 31(5): 1255-1257. (ZHANG Y L, LIU J Y, FU C Q, et al. New method for point cloud data reduction[J]. Journal of Computer Applications, 2011, 31(5): 1255-1257.)
[7] OSHIMA T. Modern survey of large bridge and tunnel project for their construction control[EB/OL]. [2017-01-10]. https://www.fig.net/resources/proceedings/fig_proceedings/korea/abstracts/pdf/session17/oshima-abs.pdf.
[8] 郑德华. 点云数据直接缩减方法及缩减效果研究[J]. 测绘工程, 2006, 15(4): 27-30. (ZHENG D H. The data reduction of point cloud and analysis of reduction effect[J]. Engineering of Surveying and Mapping, 2006, 15(4): 27-30.)
[9] 黄国珍, 卢章平. 面向逆向工程的点云数据简化方法[J]. 机械设计与研究, 2005, 21(31): 59-61. (HUANG G Z, LU Z P. Method of point cloud data reduction for reverse engineering[J]. Machine Design and Research, 2005, 21(31): 59-61.)
[10] SIHVO T, NⅡTTYLAHTI J. A low cost solution for 2D memory access[C]//MWSCAS 2006: Proceedings of the 49th IEEE International Midwest Symposium on Circuits and Systems. Piscataway, NJ: IEEE, 2006: 123-127.
[11] WENTZLAFF D, GRIFFIN P. On-chip interconnection architecture of the tile processor[J]. IEEE Micro, 2007, 27(5): 15-31.
[12] SAREEN K K, KNOPF G K, CANAS R. Contour-based 3D point cloud simplification for modeling freeform surfaces[C]//Proceedings of the 2009 IEEE Toronto International Conference on Science and Technology for Humanity. Piscataway, NJ: IEEE, 2009: 381-386.
[13] 杜晓晖. 一种无记忆点云迭代简化算法[J]. 计算机工程与应用, 2012, 48(3): 182-184. (DU X H. Memoryless iterative point cloud simplification algorithm[J]. Computer Engineering and Applications, 2012, 48(3): 182-184.)
[14] KIM S-J, KIM C-H, LEVIN D. Surface simplification using a discrete curvature norm [J]. Computer & Graphics, 2002, 26(5): 657-663.
[15] 黄文明, 肖朝霞, 温佩芝,等. 保留边界的点云简化方法[J]. 计算机应用, 2010, 30(2): 348-384. (HUANG W M, XIAO Z X, WEN P Z, et al. Point cloud simplification with boundary points reservation[J]. Journal of Computer Applications, 2010, 30(2): 348-384.)
[16] PAULY M, GROSS M, KOBBELT L P. Efficient simplification of point-sampled surfaces [C]//VIS 2002: Proceedings of the 2002 IEEE Conference on Visualization. Washington, DC: IEEE Computer Society, 2002: 163-170.
[17] 聂建辉, 胡英, 马孜.散乱点云离群点的分类识别算法[J]. 计算机辅助设计与图形学学报, 2011, 23(9): 1526-1532. (NIE J H, HU Y, MA Z. Outlier detection of scattered point cloud by classification[J]. Journal of Computer-Aided Design & Computer Graphics, 2011, 23(9): 1526-1532.)
[18] 赵京东, 杨凤华, 刘爱晶.散乱点云近离群点识别算法[J]. 计算机应用, 2015, 35(4): 1089-1092, 1128. (ZHAO J D, YANG F H, LIU A J. Near outlier detection of scattered point cloud [J]. Journal of Computer Applications, 2015, 35(4): 1089-1092, 1128.)
[19] BERNARDINI F, MITTLEMAN J, RUSHMEIER H, et al. The ball pivoting algorithm for surface reconstruction [J]. IEEE Transactions on Visualization and Computer Graphics, 1999, 5(4): 349-359.
[20] HOPPE H, DE ROSE T, DUCHAMP T, et al. Surface reconstruction from unorganized points[J]. Computer Graphics, 1992, 26(2): 71-78.
[21] 周儒荣, 张丽艳, 苏旭, 等.海量散乱点的曲面重建算法研究[J]. 软件学报, 2001, 12(2): 249-255. (ZHOU R R, ZHANG L Y, SU X, et al. Algorithmic research on surface reconstruct ion from dense scattered points [J]. Journal of Software, 2001, 12(2): 249-255.)

Unified algorithm for scattered point cloud denoising and simplification

散乱点云去噪与简化的统一算法

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