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| Point Cloud Segmentation De-noising Method Based on Curvature Constraint |
| ZHANG Yu-cun1,LI Ya-bin1,FU Xian-bin2 |
1. Institute of Electrical Engineer, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Institute of Information Engineer, Hebei University of Environmental Engineering, Qinhuangdao, Hebei 066102, China |
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Abstract In the process of point cloud de-noising, point cloud segmentation de-noising method based on curvature constraint was proposed in order to improve the de-noising effect of the region with larger curvature change. In the mentioned method, curvature constrained point cloud data was used to keep the model features effectively, and noise smoothing mapping function was constructed to make the noise points regress smoothly, which could avoid over-smoothing the point cloud data model by traditional filtering and lay a foundation for subsequent processing. Experiments showed that the above method could effectively preserve the characteristics of the model, retain the edge information of the model, and the de-noising effect was more obvious than bilateral filtering.
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Received: 16 September 2018
Published: 10 October 2020
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| Fund:;Science and Technology Research Projects of Hebei Higher Education Institutions |
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［1］曹文意,陈继民,袁艳萍,等. 基于多视图的三维模型采集系统的研制［J］. 计量学报, 2019, 40(6): 1000-1005.
Cao W Y, Chen J M, Yuan Y P, et al. Research on 3D Model Acquisition SystemBased on Multiple View ［J］. Acta Metrologica Sinica, 2019, 40(6): 1000-1005.
［2］Kim H J, BickelI B, Gross M, et al. Subsurface scattering using splat-based diffusion in point-based rendering［J］. Science China-Information Sciences, 2010, 53(5): 911-919.
［3］Fleishman S, Drori I, Cohen-Or D. Bilateral mesh denoising［J］. Acm Transactions on Graphics, 2003, 22(3): 950-953.
［4］Zheng Y Y, Fu H B, Au O K C, et al. Bilateral Normal Filtering for Mesh Denoising［J］. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(10): 1521-1530.
［5］江亮亮, 杨付正, 任光亮. 用于网格去噪的自适应双边滤波器［J］. 华南理工大学学报:自然科学版, 2015, 43(11): 54-60+74.
Jiang L L, Yang F Z, Fu G L. Adaptive Bilateral Filter for Mesh Denoising ［J］. Journal of South China University of Technology (Natural Science Edition), 2015, 43(11): 54-60 +74.
［6］Lu X Q, Liu X H, Deng Z G, et al. An efficient approach for feature-preserving mesh denoising［J］. Optics and Lasers in Engineering, 2017, 90: 186-195.
［7］崔鑫, 闫秀天, 李世鹏. 保持特征的散乱点云数据去噪［J］. 光学精密工程, 2017, 25(12): 3169-3178.
Cui X, Yan X T, Li S P. Feature-preserving scattered point cloud denoising［J］. Optics and Precision Engineering, 2017, 25(12): 3169-3178.
［8］Zhang Y, Liu X Q. A Three-Dimensional Diffusion Filtering Model Establishment and Analysis for Point Cloud Intensity Noise［J］. Journal of Computing and Information Science in Engineering, 2017, 17(1): 011010.
［9］吴禄慎, 史皓良, 陈华伟. 基于特征信息分类的三维点数据去噪［J］. 光学精密工程, 2016, 24(6): 1465-1473.
Wu L S, Shi H L, Chen H W. Denoising of three-dimensional point data based on classification of feature information［J］. Optics and Precision Engineering, 2016, 24(6): 1465-1473.
［10］Zheng Y L, Li G Q, Wu S H, et al. Guided point cloud denoising via sharp feature skeletons［J］. Visual Computer, 2017, 33(6): 857-867.
［11］Mattei E, Castrodad A. Point Cloud Denoising via Moving RPCA［J］. Computer Graphics Forum, 2017, 36(8): 123-137.
［12］Wang P S, Fu X M, Liu Y, et al. Rolling Guidance Normal Filter for Geometric Processing［J］. Acm Transactions on Graphics, 2015, 34(6): 1-9.
［13］樊宇, 王宇楠, 王俊杰, 等. 一种基于几何关系的数据点云去噪算法［J］. 价值工程, 2011, 30(20): 217-218.
Fan Y, Wang Y N, Wang J J, et al. A Triangle Filter Method for Reducing Noise Error of Points Clouds Data Based on Geometric Relations［J］. Value Engineering, 2011, 30(20): 217-218.
［14］张巧英, 陈浩, 朱爽. 密度聚类算法在连续分布点云去噪中的应用［J］. 地理空间信息, 2011, 9(6): 101-104.
Zhang Q Y, Chen H, Zhu S. Application of Density-based Clustering Algorithms in Noise Removing of Continuous Distributed Point Clouds ［J］. Geospatial Information, 2011, 9(6): 101-104. |
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2020, Vol. 41 
