保持特征的散乱点云数据去噪

崔鑫; 李世鹏

doi:10.3788/OPE.20172512.3169

您当前的位置：

首页 >

文章列表页 >

保持特征的散乱点云数据去噪

信息科学 | 更新时间：2020-07-07

- 保持特征的散乱点云数据去噪
- Feature-preserving scattered point cloud denoising
- 光学精密工程 2017年25卷第12期页码：3169-3178
- 作者机构：
  
  北京宇航系统工程研究所, 北京 100076
- 作者简介：
  
  [ "崔鑫(1993-), 男, 山东菏泽人, 硕士, 助理工程师, 2015年于西北工业大学获得学士学位, 主要从事三维重建与逆向工程方面的研究。E-mail:npucuixin@163.com" ]
- 基金信息：
  
  国家国际科技合作专项资金资助项目(2013DFA51360)
- DOI：10.3788/OPE.20172512.3169
  中图分类号： TP391.41
- 收稿日期：2017-06-23，
  
  录用日期：2017-8-15，
  
  纸质出版日期：2017-12-25
- 稿件说明：
移动端阅览
崔鑫, 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.

Xin CUI, Shi-peng LI. Feature-preserving scattered point cloud denoising[J]. Optics and precision engineering, 2017, 25(12): 3169-3178.
崔鑫, 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. DOI： 10.3788/OPE.20172512.3169.

Xin CUI, Shi-peng LI. Feature-preserving scattered point cloud denoising[J]. Optics and precision engineering, 2017, 25(12): 3169-3178. DOI： 10.3788/OPE.20172512.3169.

摘要

为保证在去除点云数据噪声的同时不损失模型的细节特征，提出了一种基于特征信息的加权模糊C均值聚类去噪算法。首先，构建点云K-D树拓扑结构，根据点的

半径球邻域点统计特性去除大尺度离群噪声点。然后，利用主元分析法估算点云的曲率和法向量，根据曲率特征标识点云数据的特征区域，并采用特征加权模糊C均值聚类算法对特征区域去噪，采用加权模糊C均值聚类算法对非特征区域去噪。最后，使用双边滤波器对点云模型进行平滑。对提出的算法进行了验证实验，结果显示：去噪后点云模型的最大偏差保持在模型尺寸的0.15%以内；标准偏差保持在模型尺寸的0.03%以内。本文算法能够在有效去除不同尺度和强度的噪声的同时不损失点云模型的细节特征，去噪精度高，且对不同的噪声模型具有较强的鲁棒性。

Abstract

To move the outliers and noisy points away from 3D point cloud data and to maintain the sharp features of the model simultaneously

a feature-based weighted fuzzy C-means point cloud denoising algorithm was proposed. Firstly

the point cloud was organized by K-D tree data structure and the large-scale outliers were removed by the statistics of r radius neighboring points. Then

the principal component analysis method was adopted to estimate the curvature and normal vector of point cloud data and the patches with distinguished features were identified according to the curvature feature weight. Pursuant to different feature regions

the feature-preserving weighted fuzzy C-means clustering algorithm was adopted to denoise for the patch with rich feature information and the fuzzy C-means clustering algorithm was adapted to denoise for the patch with less feature information

respectively. Finally

a bilateral filter was used to smooth the data set. The algorithm was verified and the experimental results show that the max denoising error is limited to 0.15% of the model size and the min denoising error is limited to 0.03% of the model size. In conclusion

this approach moves efficiently and precisely the noise with different scales and intensities in point cloud

meanwhile performing a feature-preserving nature. Moreover

it is robust enough to different noise models.

关键词

Keywords

references

MOORFIELD B, HAEUSLER R, KLETTE R. Bilateral filtering of 3D point clouds for refined 3D roadside reconstructions[C]. International Conference on Computer Analysis of Images and Patterns , Springer , 2015:394-402. http://link.springer.com/content/pdf/10.1007/978-3-319-23117-4_34.pdf

张雨禾, 耿国华, 魏萧然.散乱点云谷脊特征提取[J].光学精密工程, 2015, 23(1):310-318.

ZHANG Y H, GENG G H, WEI X R. Valley-ridge feature extraction from point clouds[J]. Opt. Precision Eng., 2015, 23(1):310-318. (in Chinese)

JONES T R, DURAND F, DESBURN M. Non-iterative, feature-preserving mesh smoothing[J]. ACM Transactions on Graphics, 2003, 22(3):943-949.

FLEISHMAN S, DRORI I, COHEN-OR D. Bilateral mesh denoising[J]. ACM Transactions on Graphics, 2003, 22(3):950-953.

王丽辉. 三维点云数据处理的技术研究[D]. 北京: 北京交通大学, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10004-1011198797.htm

WANG L H. Study on data processing technology of 3D cloud points [D]. Beijing:Beijing Jiaotong University, 2011. (in Chinese)

袁小翠, 吴禄慎, 陈华伟.特征保持点云数据精简[J].光学精密工程, 2015, 23(9):2666-2676.

YUAN X C, WU L SH, CHEN H W. Feature preserving point cloud simplification[J]. Opt. Precision Eng., 2015, 23(9):2666-2676. (in Chinese)

WANG X Z, LI Z K, MAI Y Q, et al .. Robust denoising of unorganized point clouds[C]. Proceedings of the 2011 International Conference on Intelligent Computing and Integrated Systems , IEEE , 2013:1-3. http://www.researchgate.net/publication/269311500_Robust_denoising_of_unorganized_point_clouds

GU X Y, LIU Y S, WU Q. A filtering algorithm for scattered point cloud based on curvature features classification[J]. Journal of Information and Computational Science, 2015, 12(2):525-532.

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 SH, SHI H L, CHEN H W. Denoising of three-dimensional point data based on classification of feature information[J]. Opt. Precision Eng., 2016, 24(6):1465-1473.

曹爽, 岳建平, 马文.基于特征选择的双边滤波点云去噪算法[J].东南大学学报(自然科学版), 2013, 43(S2):351-354.

CAO SH, YUE J P, MA W. Bilateral filtering denoise algorithm for point cloud based on feature selection[J]. Journal of Southeast University (Natural Science Edition), 2013, 43(S2):351-354. (in Chinese)

LIU C, YAN D, ZHAO H W. 3D point cloud denoising and normal estimation for 3D surface reconstruction[C]. Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics , IEEE , 2015:820-825. http://www.researchgate.net/publication/304414478_3D_point_cloud_denoising_and_normal_estimation_for_3D_surface_reconstruction

HAQUE S M, GOVINDU V M. Robust feature-preserving denoising of 3D point clouds[C]. Proceedings of the 2016 4 th International Conference on 3 D Vision , IEEE , 2016:83-91. http://www.researchgate.net/publication/311756195_Robust_Feature-Preserving_Denoising_of_3D_Point_Clouds

JUN S. Two-stage point-sampled model denoising by robust ellipsoid criterion and mean shift[C]. Proceedings of the 2013 3 rd International Conference on Intelligent System Design and Engineering Applications . IEEE , 2013:1581-1584. http://www.researchgate.net/publication/261320657_Two-stage_Point-sampled_Model_Denoising_by_Robust_Ellipsoid_Criterion_and_Mean_Shift

SCHOENENBERGER Y, PARATTE J, VANDERGHEYNST P. Graph-based denoising for time-varying point clouds[C]. 3 DTV-Conference : The True Vision-Capture , Transmission and Display of 3 D Video , IEEE , 2015:1-4. http://arxiv.org/abs/1511.04902

ROSMAN G, DUBROVINA A, KIMMEL R. Patch-collaborative spectral surface denoising[J]. Computer Graphics Forum, 2013, 32(8):1-12.

ZAMAN F, WONG Y P, NG B Y. Density-based denoising of point cloud[C]. Proceedings of the 9 th International Conference on Robotic , Vision , Signal Processing and Power Applications , Springer , 2017:287-295. http://www.researchgate.net/publication/311998465_Density-Based_Denoising_of_Point_Cloud

程效军, 贾东峰, 程小龙.海量点云数据处理理论与技术[M].上海:同济大学出版社, 2014:5.

CHENG X J, JIA D F, CHENG X L. Theory and Technology of Mass Point Cloud Data Pretreatment[M]. Shanghai:Tongji University Press, 2014:5. (in Chinese)

CHAUDHURY K N. Acceleration of the shiftable O(1) algorithm for bilateral filtering and nonlocal means[J]. IEEE Transactions on Image Processing, 2013, 22(4):1291-1300.

浏览量

646

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

非合作航天器的立体视觉位姿测量

用于静脉增强显示的图像融合算法

光学合成孔径复原图像的振铃探测与消除

基于广义线性运算和双边滤波的红外图像增强

应用属性距离加权平均滤波提高CCD光斑的亚像素定位精度