3D Point Cloud Denoising Method Based on Curvature and Normal Information Segmentation

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3D Point Cloud Denoising Method Based on Curvature and Normal Information Segmentation

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[1]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]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 3 Page number: 92-99 Column: Public date: 2026-05-27

Title:: 3D Point Cloud Denoising Method Based on Curvature and Normal Information Segmentation

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

Keywords:: point cloud denoising; point cloud segmentation; curvature information; statistical filtering; bilateral filtering

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2025.03.014

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.

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Last Update: 2026-05-27