Feature preserving point cloud simplification

YUAN Xiao-cui; WU Lu-shen; CHEN Hua-wei

doi:10.3788/OPE.20152309.2666

您当前的位置：

首页 >

文章列表页 >

Feature preserving point cloud simplification

更新时间：2020-08-12

- Feature preserving point cloud simplification
- Optics and Precision Engineering Vol. 23, Issue 9, Pages: 2666-2676(2015)
- 作者机构：
  
  南昌大学机电工程学院
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152309.2666
  CLC： TP391.7
- Received：30 March 2015，
  
  Revised：03 June 2015，
  
  Published：25 September 2015
- 稿件说明：
移动端阅览
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 Xiao-cui, WU Lu-shen, CHEN Hua-wei. Feature preserving point cloud simplification[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2666-2676
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 DOI： 10.3788/OPE.20152309.2666.

YUAN Xiao-cui, WU Lu-shen, CHEN Hua-wei. Feature preserving point cloud simplification[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2666-2676 DOI： 10.3788/OPE.20152309.2666.

摘要

由于三维扫描设备采集的点云数据庞大

本文提出了一种特征保持的点云精简方法以在减少冗余数据的同时更好地保持原始曲面的几何特征。首先

利用

均值聚类法在空间域对点云全局聚类

对点云构建

-d树并以

-d树的部分节点作为初始化聚类中心。然后

用主成分分析法估计点云法矢和候选特征点

遍历每个聚类

若类中包含特征点则将该类细分为多个子类

细分时将聚类映射到高斯球。最后

基于自适应均值漂移法对高斯球上的数据进行分类

高斯球上的聚类结果对应为空间聚类细分结果

各聚类中心的集合为精简结果。以多个实物模型为例验证了算法的有效性。结果表明

本文方法精简的点云在平坦区域保留少数点

在高曲率区域保留更多的点。相比于非均匀网格、层次聚类、

均值点云精简法

该方法对包含尖锐特征的曲面精简误差最小

更好地保留了原始曲面的几何特征。

Abstract

3D scanning devices generally produce a large amount of dense points. This paper presents a feature preserving point cloud simplification method to reduce redundant points while preserving original geometric features well. Firstly

-mean clustering algorithm was employed to globally gather similar points in a spatial domain. By constructing a

K-d

tree structure for the point cloud

some nodes of the

K-d

tree were used as initial clustering centroids. Then

normal vector of point cloud and candidate feature points were estimated with principal component analysis method. Traversing every cluster

if feature points were contained in the cluster

the cluster was subdivided into a series of sub-clusters and the cluster was mapped to a Gaussian sphere. Finally

adaptive mean shift algorithm was employed to classify the data in Gaussian sphere and the clusters in Gaussian sphere were corresponded to the sub-clusters in the spatial domain. The cluster centroids present the simplification data. Several real object models were used to verify the effectiveness of the proposed method. The experiment results demonstrate that the proposed method generates sparse sampling points in flat areas and high density points in high curvature regions. As comparing with the non-uniform grid

hierarchical agglomerative

and

-means methods

the proposed method obtains the smallest simplification error and preserves original geometric features.

关键词

Keywords

references

李磊刚,梁晋,唐正宗,等. 用于工业三维点测量的接触式光学探针[J]. 光学精密工程, 2014, 22(6):1477-1485. LI L G, LIAN J, TANG ZH Z, et al..Optical and contact probe for industrial measurment of 3-D points [J]. Opt. Precision Eng., 2014, 22(6):1477-1485. (in Chinese)

XIE F, ZHAO J, JU F.The point cloud collection of the incisor teeth of beaver and re-construction of its curved surface [J]. Advanced Materials Research, 2012, 426:387-390.

DEFISHER S, BECHTOLD M, MOHRING D. A non-contact surface measurement system for freeform and conformal optics [C]. SPIE Defense, Security, and Sensing, 2011, 8016:80160W-1-80160W-6.

MIAN S H, MANNAN M, AL-AHMARI A. Accuracy of a reverse-engineered mould using contact and non-contact measurement techniques [J]. International Journal of Computer Integrated Manufacturing, 2015,28(5):419-436.

史金龙, 钱强, 庞林斌,等. 大尺度钢板的三维测量和拼接[J]. 光学精密工程, 2014, 22(5):1165-1170. SHI J L, QIAN Q, PANG L B, et al.. Three-dimentional measurement and registration for large scale plates [J] Opt. Precision Eng., 2014, 22(5):1165-1170. (in Chinese)

MAGLO A, COURBET C, ALLIEZ P, et al.. Progressive compression of manifold polygon meshes[J]. Computers & Graphics, 2012, 36(5):349-359.

MARTIN R, STROUD I, MARSHALL A. Data reduction for reverse engineering[J]. RECCAD. Deliverable document 1 COPERUNICUS project. No. 1068. Computer and Automation Institute of Hungarian Academy of Science. January, 1996.

LEE K, WOO H, SUK T. Point data reduction using 3D grids[J]. The International Journal of Advanced Manufacturing Technology, 2001, 18(3):201-210.

周煜, 张万兵, 杜发荣,等. 散乱点云数据的曲率精简算法[J]. 北京理工大学学报, 2010, 30(7):785-789. ZHOU Y, ZHANG W B, DU F R, et al.. Algorithm for reduction of scattered point cloud data based on curvature [J]. Transactions of Beijing Institution Technology, 2010,30(7):785-789. (in Chinese)

ALEXA M, BEHR J, COHEN-OR D, et al.. Computing and rendering point set surfaces[J]. IEEE Transactions on Visualization and Computer Graphics, 2003, 9(1):3-15.

SONG H, FENG H Y. A progressive point cloud simplification algorithm with preserved sharp edge data[J]. The International Journal of Advanced Manufacturing Technology, 2009, 45(5-6):583-592.

PAULY M, GROSS M, KOBBELT L P.Efficient simplification of point-sampled surfaces[C]. Proceedings of the conference on Visualization'02,2002:163-170.

SONG H, FENG H Y. A global clustering approach to point cloud simplification with a specified data reduction ratio[J]. Computer-Aided Design, 2008, 40(3):281-292.

MIAO Y, PAJAROLA R, FENG J. Curvature-aware adaptive re-sampling for point-sampled geometry[J]. Computer-Aided Design, 2009, 41(6):395-403.

SHI B Q, LIANG J, LIU Q. Adaptive simplification of point cloud using k-means clustering[J]. Computer-Aided Design, 2011, 43(8):910-922.

JAIN A K. Data clustering:50 years beyond K-means[J]. Pattern Recognition Letters, 2010, 31(8):651-666.

HOPPE H, DEROSE T, DUCHAMP T, et al.. Surface reconstruction from unorganized points[M]. ACM, 1992.

NURUNNABI A, WEST G, BELTON D. Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data[J]. Pattern Recognition, 2015, 48(4):1404-1419.

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

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

ANAND S, MITTAL S, TUZEL O, et al.. Semi-supervised kernel mean shift clustering[J]. 2013, 36(6):1201-1215.

MAYER A, GREENSPAN H. An adaptive mean-shift framework for MRI brain segmentation[J]. IEEE Transactions on Medical Imaging, 2009, 28(8):1238-1250.

Views

574

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Point cloud adaptive simplification of feature extraction

Visual tracking of moving objects based on piecewise fusion weight and multi-strategy

Moving object tracking with multi-degree-of-freedom based on particle filters

Related Author

Ying LIU

Chao-yang WANG

Nan GAO

Zong-hua ZHANG

SU Yan-zhao

LI Ai-hua

WANG Tao

ZHANG Wei

Related Institution

College of Mechanical Engineering, Hebei University of Technology

Faculty, The Second Artillery Engineering University

Changchun Institute of Optics,Fine Mechanics and Physics, Chinese Academy of Sciences

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰