尖锐特征曲面散乱点云法向估计

袁小翠; 吴禄慎; 陈华伟

doi:10.3788/OPE.20162410.2581

您当前的位置：

首页 >

文章列表页 >

尖锐特征曲面散乱点云法向估计

信息科学 | 更新时间：2020-07-07

- 尖锐特征曲面散乱点云法向估计
- Normal estimation of scattered point cloud with sharp feature
- 光学精密工程 2016年24卷第10期页码：2581-2588
- 作者机构：
  
  1.南昌工程学院江西省精密驱动与控制重点实验室, 江西南昌 330099
  2.南昌大学机电工程学院, 江西南昌 330031
- 作者简介：
  
  袁小翠(1988-),女,江西抚州人,博士,讲师,主要研究方向为图像处理与逆向工程。E-mail:yuanxc2012@163.com
  [ "吴禄慎(1953-),男,江西乐平人,硕士,教授,博士生导师,1978年于北京航空航天大学获得学士学位,1990年于清华大学获得硕士学位,主要从事面外\"moire\"法、三维光学图像测量与逆向工程的研究。E-mail:wulushen@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(51365037;51065021)
- DOI：10.3788/OPE.20162410.2581
  中图分类号： TP391.7
- 收稿日期：2016-06-15，
  
  录用日期：2016-7-19，
  
  纸质出版日期：2016-10
- 稿件说明：
移动端阅览
袁小翠, 吴禄慎, 陈华伟. 尖锐特征曲面散乱点云法向估计[J]. 光学精密工程, 2016,24(10):2581-2588.

Xiao-cui YUAN, Lu-shen WU, Hua-wei CHEN. Normal estimation of scattered point cloud with sharp feature[J]. Optics and precision engineering, 2016, 24(10): 2581-2588.
袁小翠, 吴禄慎, 陈华伟. 尖锐特征曲面散乱点云法向估计[J]. 光学精密工程, 2016,24(10):2581-2588. DOI： 10.3788/OPE.20162410.2581.

Xiao-cui YUAN, Lu-shen WU, Hua-wei CHEN. Normal estimation of scattered point cloud with sharp feature[J]. Optics and precision engineering, 2016, 24(10): 2581-2588. DOI： 10.3788/OPE.20162410.2581.

摘要

针对现有算法对尖锐特征曲面点云法矢估计不准确，点云处理时容易丢失曲面细节特征等问题，提出一种尖锐特征曲面散乱点云法向估计法。该方法用主成分分析法粗估计点云法向；然后，根据各邻域点的空间欧氏距离和法向距离对各邻域法向加权，用加权邻域法向之和来更新当前点的法向；最后，测试估计法向与标准法向的误差，评价估计法矢的准确性，并且将估计的法向应用到点云数据处理中来比较特征保留效果。实验结果表明：本文方法能够准确地估计尖锐特征曲面的法向，最小误差接近0。另外，该方法对噪声有较好的鲁棒性，点云处理时能保留曲面的尖锐特征。相比于其他特征曲面法向估计法，所提出的方法估计的法向误差更小、速度更快、耗时更少。

Abstract

A novel method was proposed to estimate the normal for a scattered point cloud with sharp features to overcome the shortcomings that existing methods are unable to reliably estimate the normal of point cloud model and lead to the smoothed sharp features. With proposed method

the normal of point cloud was estimated with principal component analysis method. Then

different values were weighted on neighborhood normals according to spatial distance and normal distance of current points of the neighborhood

and the revised or current normals were updated by the sum of weighted neighborhood normal. Finally

the average deviation between standard normal and estimated normal was measured and the accuracy of estimated normal was evaluated. The estimated normal was applied to point cloud processing to verify the feature-preserving property. The proposed method was validated. The results demonstrate that proposed method accurately estimates the normal for data with noise and the least average deviation is close to 0. Moreover

the method has good robustness to the niose

and it keeps the original geometry well when the normal is used as input of the point cloud processing. Comparing with other sharp feature preserving normal estimation methods

the proposed method shows smaller average deviation

higher processing speeds and less computation time.

关键词

Keywords

references

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 cloud based on classfication of feature information[J]. Opt. Precision Eng., 2016, 24(6):1465-1473.(in Chinese)

WANG Y H, HAO W, NING X J, et al.. Automatic segmentation of urban point clouds based on the Gaussian map[J]. Photogrammetric Record, 2013, 28(144):342-361.

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)

TANG P, HUBER D, AKINCI B, et al.. Automatic reconstruction of as-built building information models from laser-scanned point clouds:A review of related techniques[J]. Automation in Construction, 2010, 19(7):829-843.

HOPPE H, De ROSE T, DUCHAMP T, et al.. Surface reconstruction from Unorganized Points[J]. Computer Graphics, 1992, 26(2):71-78.

GUENNEBAUD G, GROSS M,ZURICH E. Algebraic point set surfaces[J]. ACM Transactions on Graphics,2007, 26(3):23.

PAULY M, GROSS M, KOBBELT L P. Efficient simplification of point-sampled surfaces[C]. IEEE Visualization 2002, Washington DC:IEEE Computer Society Press, 2002:163-170.

YOON M, LEE Y, LEE S, et al.. Surface and normal ensembles for surface reconstruction[J]. Computer-Aided Design, 2007, 39(5):408-420.

MEDEROS B, VELHO L, FIGUIREDO L H. Robust smoothing of noisy point clouds[C]. Proceedings of the SIAM Conference on Geometric Design and Computing, Seattle:SIAM, 2003:405-416.

WANG Y, FENG H Y, DELORME F, et al.. An adaptive normal estimation method for scanned point clouds with sharp features[J]. Computer-Aided Design, 2013, 45(11):1333-1348.

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.

苏志勋, 栗志扬, 王小超. 基于法向修正及中值滤波的点云平滑[J]. 计算机辅助设计与图形学学报, 2010, 22(11):1892-1898.

SU ZH X, LI ZH Y, WANG X CH. Denoising of point-sampled model based on norm al mollification and median filtering[J]. Journal of Computer-Aided Design & Computer Graphics, 2010, 22(11):1892-1898.(in Chinese)

ZHANG J, CAO J, LIU X, et al.. Point cloud normal estimation via low-rank subspace clustering[J]. Computers & Graphics, 2013, 37(6):697-706.

AMENTA N, BERN M. Surface reconstruction by Voronoi filtering[J]. Discrete & Computational Geometry, 1999, 22(4):481-504.

DEY T K, GOSWAMI S. Provable surface reconstruction from noisy samples[C]. IEEE Symposium on Parallel and Large-Data Visualization and Graphics, Piscataway:IEEE, 2001:19-27.

ALLIEZ P, COHEN-STEINER D, TONG Y, et al.. Voronoi-based variational reconstruction of unoriented point sets[C]. Proceedings of the 5th Eurographics Symposium on Geometry processing, Geneve:Wiley, 2007:39-48.

HUBER P J. Robust Statistics[M]. Berlin:Springer, 2011.

ZHENG Y, FU H, AU O C, et al.. Bilateral normal filtering for mesh denoising[J]. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(10):1521-1530.

浏览量

364

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于Soft均值滤波的鲁棒主成分分析算法

应用邻域点信息描述与匹配的点云配准

光谱色差与主分量特征融合的石窟表面风化智能评估

凸显尖锐特征的点-线-面递进式曲面重建

用于静脉增强显示的图像融合算法