[1]刘永生,甘鑫斌,杨豪强,等.基于曲率与法线信息分割的三维点云数据去噪方法[J].郑州大学学报(工学版),2026,47(3):92-99.[doi:10.13705/j.issn.1671-6833.2025.03.014]
　LIU Yongsheng,GAN Xinbin,YANG Haoqiang,et al.3D Point Cloud Denoising Method Based on Curvature and Normal Information Segmentation[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):92-99.[doi:10.13705/j.issn.1671-6833.2025.03.014]
点击复制

基于曲率与法线信息分割的三维点云数据去噪方法()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年3期
页码:
92-99
栏目:
出版日期:
2026-05-27

文章信息/Info

Title:
3D Point Cloud Denoising Method Based on Curvature and Normal Information Segmentation
文章编号:
1671-6833(2026)03-0092-08
作者:
刘永生1,2, 甘鑫斌1, 杨豪强1, 谭佳敏1, 王瑞富1
1.长安大学 道路施工技术与装备教育部重点实验室,陕西 西安 710064;2.中航光电科技股份有限公司,河南 洛阳 471003
Author(s):
LIU Yongsheng 1,2 , GAN Xinbin1, YANG Haoqiang1, TAN Jiamin1, WANG Ruifu1
1.Key Laboratory of Road Construction Technology and Equipment of the Ministry of Education, Chang’an University, Xi’an 710064, China; 2.AVIC Jonhon Optronic Technology Co., Ltd., Luoyang 471003, China
关键词:
点云去噪;  点云分割;  曲率信息;  统计滤波;  双边滤波
Keywords:
point cloud denoising;  point cloud segmentation;  curvature information;  statistical filtering;  bilateral filtering
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2025.03.014
文献标志码:
A
摘要:
在三维点云数据采集过程中,由于三维激光扫描设备精度的局限性和外部环境的干扰等因素,获取的点云数据通常混杂着噪声。为了有效去除三维点云数据中的噪声,同时准确保留其几何特征,提出了一种基于三维点云曲率与法线信息分割的去噪方法。首先,利用奇异值分解(SVD)和距离加权分别估算点云的曲率与法线信息,并将点云划分为平坦区域和非平坦区域;其次,针对平坦区域采用改进的统计滤波处理,利用动态调整邻域大小和曲率加权距离以优化离群点检测;对非平坦区域则结合改进的双边滤波方法,通过增强空间距离和法线差异的权重函数,有效保留局部几何特征。对斯坦福兔子点云处理的实验结果表明:所提算法的去噪率达到了97.83%,优于统计滤波、双边滤波和DBSCAN等传统方法;对实测直齿轮点云进行实验,去噪后的点云与标准模型的偏差分析显示,直齿轮整体点云中90%以上的点与标准模型的距离偏差小于0.2 mm,轮齿部分90%以上的点与标准直齿轮模型的距离偏差小于0.25 mm,在去除噪声的同时,能够有效保留点云模型的几何细节和边缘特征,证明了所提算法的有效性。
Abstract:
In the process of 3D point cloud data acquisition, due to the factors such as the limitations of the accuracy of 3D laser scanning equipment and the interference from external environmental, the collected point cloud data is often contaminated with noise. To effectively remove noise while accurately preserving the geometric features of the 3D point cloud, in this study a denoising method based on 3D point cloud curvature and normal information segmentation was proposed. Firstly, the curvature and normal information of the point cloud were estimated using Singular Value Decomposition (SVD) and distance-weighted method, respectively, to divide the point cloud into flat and non-flat regions. Subsequently, improved statistical filtering approach was applied to flat regions, utilizing dynamic neighborhood size adjustment and curvature-weighted distance to optimize outlier detection. For non-flat regions, an improved bilateral filtering method was combined to enhance the weight function of spatial distance and normal difference, effectively preserving local geometric features. Experimental results on Stanford Bunny point cloud demonstrated that the denoising rate of the proposed algorithm reached by 97.83%, outperforming traditional methods such as statistical filtering, bilateral filtering, and DBSCAN. Experiments on spur gear point clouds showed that the deviation analysis between the denoised point cloud and the standard model indicated that more than 90% of the points in the overall spur gear point cloud had a distance deviation of less than 0.2 mm from the standard model, and more than 90% of the points on the gear teeth had a distance deviation within 0.25 mm. This confirmed that the algorithm could effectively preserve the geometric details and edge features of the point cloud model while removing noise, demonstrating its effectiveness.

参考文献/References:

[1]丁少闻, 张小虎, 于起峰, 等. 非接触式三维重建测量方法综述[J]. 激光与光电子学进展, 2017, 54(7): 70003.

DING S W, ZHANG X H, YU Q F, et al. Overview of non-contact 3D reconstruction measurement methods[J]. Laser & Optoelectronics Progress, 2017, 54(7): 70003.
[2]段红娟. 简述三维点云处理技术的研究[J]. 电子技术与软件工程, 2013(14): 137-138.
DUAN H J. A brief introduction to the research of 3D point cloud processing technology[J]. Electronic Technology & Software Engineering, 2013(14): 137-138.
[3]陈义飞, 郭胜, 潘文安, 等. 基于多源传感器数据融合的三维场景重建[J]. 郑州大学学报(工学版), 2021, 42(2): 80-86.
CHEN Y F, GUO S,PAN W A , et al. 3D scene reconstruction based on multi-source sensor data fusion[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(2): 80-86.
[4]杜超, 向亚琪, 樊国政. 基于激光雷达的点云数据处理研究[J]. 信息技术与信息化, 2024(3): 83-86.
DU C, XIANG Y Q, FAN G Z. Research on point cloud data processing based on lidar[J]. Information Technology and Informatization, 2024(3): 83-86.
[5]杨必胜, 梁福逊, 黄荣刚. 三维激光扫描点云数据处理研究进展、挑战与趋势[J]. 测绘学报, 2017, 46(10): 1509-1516.
YANG B S, LIANG F X, HUANG R G. Progress, challenges and perspectives of 3D LiDAR point cloud processing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1509-1516.
[6]FLEISHMAN S, DRORI I, COHEN-OR D. Bilateral mesh denoising[J]. ACM Transactions on Graphics, 2003, 22(3): 950-953.
[7]任彬, 崔健源, 李刚, 等. 基于自适应阈值的三维点云分段式去噪方法[J]. 光子学报, 2022, 51(2): 319-332.
REN B, CUI J Y, LI G, et al. A three-dimensional point cloud denoising method based on adaptive threshold[J]. Acta Photonica Sinica, 2022, 51(2): 319-332.
[8]ZHOU S T, LIU X L, WANG C Y, et al. Non-iterative denoising algorithm based on a dual threshold for a 3D point cloud[J]. Optics and Lasers in Engineering, 2020, 126: 105921.
[9]焦亚男, 马杰, 钟斌斌. 一种基于尺度变化的点云并行去噪方法[J]. 武汉大学学报(工学版), 2021, 54(3): 277-282.
JIAO Y N, MA J, ZHONG B B. Point cloud parallel denoising algorithms based on scale change[J]. Engineering Journal of Wuhan University, 2021, 54 (3): 277-282.
[10]陈亚超, 樊彦国, 禹定峰, 等. 考虑法向离群的自适应双边滤波点云平滑及IMLS评价方法[J]. 图学学报, 2023, 44(1): 131-138.
CHEN Y C, FAN Y G, YU D F, et al. Adaptive bilateral filtering point cloud smoothing and IMLS evaluation method considering normal outliers[J]. Journal of Graphics, 2023, 44(1): 131-138.
[11] LI B, SCHNABEL R, KLEIN R, et al. Robust normal estimation for point clouds with sharp features[J]. Computers & Graphics, 2010, 34(2): 94-106.
[12]袁华, 庞建铿, 莫建文. 基于噪声分类的双边滤波点云去噪算法[J]. 计算机应用, 2015, 35(8): 2305-2310.
YUAN H, PANG J K, MO J W. Denoising algorithm for bilateral filtered point cloud based on noise classification[J]. Journal of Computer Applications, 2015, 35(8):2305-2310.
[13]鲁冬冬, 邹进贵. 三维激光点云的降噪算法对比研究[J]. 测绘通报, 2019(增刊2): 102-105.
LU D D, ZOU J G. Comparative research on denoising algorithms of 3D laser point cloud[J]. Bulletin of Surveying and Mapping, 2019(S2): 102-105.
[14]赵尔平, 刘炜, 党红恩. 海量3D点云数据压缩与空间索引技术[J]. 计算机应用, 2018, 38(1): 146151, 193.
ZHAO E P, LIU W, DANG H E. Data compression and spatial indexing technology for massive 3D point cloud[J]. Journal of Computer Applications, 2018, 38(1): 146-151, 193.
[15] LUO N, JIANG Y Y, WANG Q. Supervoxel-based region growing segmentation for point cloud data[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2021, 35(3): 2154007.
[16]朱广堂, 叶珉吕. 基于曲率特征的点云去噪及定量评价方法研究[J]. 测绘通报, 2019(6): 105-108.
ZHU G T, YE M L. Research on the method of point cloud denoising based on curvature characteristics and quantitative evaluation[J]. Bulletin of Surveying and Mapping, 2019(6): 105-108.
[17] LITTLE A V, MAGGIONI M, ROSASCO L. Multiscale geometric methods for data sets I: multiscale SVD, noise and curvature[J]. Applied and Computational Harmonic Analysis, 2017, 43(3): 504-567.
[18] GUO X Y, SHI S H, ZHOU M, et al. Application of normal vectors and color features in semantic segmentation of colored point clouds[C]∥2023 China Automation Congress (CAC). Piscataway: IEEE, 2023: 8547-8552.
[19]焦晨, 王宝锋, 易耀华. 点云数据滤波算法研究[J]. 国外电子测量技术, 2019, 38(11): 18-22.
JIAO C, WANG B F, YI Y H. Research on point cloud data filtering algorithms[J]. Foreign Electronic Measurement Technology, 2019, 38(11): 18-22.
[20]魏硕, 赵楠翔, 李敏乐, 等. 结合改进DBSCAN和统计滤波的单光子去噪算法[J]. 激光技术, 2021, 45(5): 601-606.
WEI S, ZHAO N X, LI M L, et al. Single photon denoising algorithm combined with improved DBSCAN and statistical filtering[J]. Laser Technology, 2021, 45(5): 601-606.
[21]张志强, 王万玉. 一种改进的双边滤波算法[J]. 中国图象图形学报, 2009, 14(3): 443-447.
ZHANG Z Q, WANG W Y. A modified bilateral filtering algorithm[J]. Journal of Image and Graphics, 2009, 14(3): 443-447.
[22] DIGNE J, DE FRANCHIS C. The bilateral filter for point clouds[J]. Image Processing on Line, 2017, 7: 278-287.

相似文献/References:

[1]李文举,姬倩倩,沙利业,等.基于图游走和图注意力的点云分类与分割[J].郑州大学学报(工学版),2024,45(2):33.[doi:10.13705/j.issn.1671-6833.2024.02.006]
　LI Wenju,JI Qianqian,SHA Liye,et al.Point Cloud Classification and Segmentation Based on Graph Walk and Graph Attention[J].Journal of Zhengzhou University (Engineering Science),2024,45(3):33.[doi:10.13705/j.issn.1671-6833.2024.02.006]

更新日期/Last Update: 2026-05-27