一种新的散乱点云尖锐特征提取方法

吾守尔·斯拉木 1; 2; 曹巨明 1

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一种新的散乱点云尖锐特征提取方法

更新时间：2025-07-09

- 一种新的散乱点云尖锐特征提取方法
- An Extraction Algorithm for Sharp Feature Points from Point Clouds
- 西安交通大学学报 2012年46卷第12期页码：1-5+73
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 新疆大学信息科学与工程学院,乌鲁木齐,830046
- 作者简介：
- 基金信息：
  
  : 2012-04-22. 作者简介: 吾守尔·斯拉木(1942-),男,教授,博士生导师,中国工程院院士. 基金项目: 国家自然科学基金资助项目(61063024)
- DOI：
  中图分类号： TP393
- 网络首发：2012-12-10，
  
  纸质出版：2012
- 稿件说明：
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吾守尔·斯拉木 1, 2, 曹巨明 1. 一种新的散乱点云尖锐特征提取方法[J]. 西安交通大学学报, 2012,46(12):1-5+73.

An Extraction Algorithm for Sharp Feature Points from Point Clouds[J]. 2012, 46(12): 1-5+73.
吾守尔·斯拉木 1, 2, 曹巨明 1. 一种新的散乱点云尖锐特征提取方法[J]. 西安交通大学学报, 2012,46(12):1-5+73. DOI：

An Extraction Algorithm for Sharp Feature Points from Point Clouds[J]. 2012, 46(12): 1-5+73. DOI：

摘要

针对散乱点云的尖锐特征识别与提取问题

提出一种基于平均曲率运动的散乱点云尖锐特征提取算法.该算法利用采样点的加权邻域重心近似表示离散Laplacian算子; 利用采样点邻域的主成分分析估算散乱点云法向量

通过张量投票的方法平滑估算得到的点云法向场

进一步提高了该算法识别细微尖锐特征的能力; 将采样点和其对应加权邻域重心之间的距离投影到法向方向

消除了因为采样密度不均匀以及边界点所引起的尖锐特征点误判.该算法直接对散乱点云进行操作

不需要维护采样点之间的连接关系和任何全局的拓扑信息

简捷且易于实现

对点云中的噪声和局外点保持鲁棒.该算法应用于合成点云和实际扫描点云的实验结果表明了方法的有效性.

Abstract

A novel algorithm for extraction of sharp feature points from scattered point cloud is proposed. The proposed method bases on the concept of mean curvature motion.The weighted local barycenter is used as an approximation of discrete Laplacian operator. If the distance between a sample point and its weighted local barycenter is greater than a given threshold

then the sample point is labeled as a candidate sharp feature point. Then

the normal directions of these candidate points are estimated using the local PCA method. In order to extract finer sharp feature points

the estimated normal field is further smoothed via tensor voting frame. The distance between a point and its weighted local barycenter is projected along the direction of normal

so that the fault sharp feature points due to uneven sample ratio and point cloud boundary can be eliminated successfully. Experiments on both real scanner point clouds and synthesized point clouds show that the proposed method is easy to implement

efficient for both space and time overhead

and is robust to noise

outlier and uneven sample ratio inherent in point clouds.

关键词

Keywords

references

GROSS M, PFISTER H. Point-based graphics[M]. San Francisco, CA, USA: Morgan Kaufmann, 2007.

胡事民,杨永亮,来煜坤. 数字几何处理研究进展[J]. 计算机学报,2009,32(8):1-18.

HU Shimin, YANG Yongliang, LAI Yukun. Research progress of digital geometry processing [J]. Chinese Journal of Computers,2009,32(8):1-18.

GUMHOLD S, WANG X, MACLEOD R. Feature extraction from point clouds [C]∥Proceedings of the 10th International Meshing Roundtable Conference. Berlin, Germany: Springer, 2001: 293-305.

PAULY M, KEISER R, GROSS M. Multi-scale feature extraction on point-sampled surfaces[J]. Computer Graphics Forum, 2003,22(3): 281-289.

DANIELS J, HA L K, OCHOTTA T, et al. Robust smooth feature extraction from point clouds [C]∥Proceedings of the IEEE International Conference on Shape Modeling and Applications. Piscataway,NJ,USA:IEEE,2007: 123-136.

FLEISHMAN S, COHEN-OR D, SILVA C T. Robust moving least-squares fitting with sharp features [J]. ACM Transactions on Graphics, 2005,24(3): 544-552.

MERIGOT Q, OVSJANIKOV M, GUIBAS L J. Robust Voronoi-based curvature and feature estimation [C]∥Proceedings of the SIAM/ACM Joint Conference on Geometric and Physical Modeling. New York, USA: ACM, 2009:1-12.

DEMARSIN K, VANDERSTRAETEN D, VOLODINE T, et al. Detection of closed sharp edges in point clouds using normal estimation and graph theory [J]. Computer-Aided Design, 2007, 39(4): 276-283.

WEBER C, HAHMANN S, HAGEN H. Sharp feature detection in point clouds [C]∥Proceedings of the IEEE International Conference on Shape Modeling and Applications. Piscataway,NJ,USA:IEEE,2010:175-186.

KALOGERAKIS E, NOWROUZEZAHRAI D, SIMARI P, et al. Extracting lines of curvature from noisy point clouds [J]. Computer Aided Design, 2009, 41(4): 282-292.

CANNY J. A computational approach to edge detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(6): 679-698.

KOBBELT L P, BOTSCH M, SCHWANECKE U, et al. Feature sensitive surface extraction from volume data [C]∥Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. New York, USA: ACM,2001: 57-66.

YOSHIZAWA S, BELYAEV A, YOKOTA H, et al. Fast and faithful geometric algorithm for detecting crest lines on meshes[J]. Computer Aided Geometric Design, 2008,25(8): 231-237.

LEE Yunjin, LEE Seungyong. Geometric snakes for triangular meshes [J]. Computer Graphics Forum, 2002, 21(3): 229-238.

JIA Jiaya, TANG Chi-Keung. Tensor voting for image correction by global and local intensity alignment [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(1): 36-50.

TANG Chi-Keung, MEDIONI G. Inference of integrated surface, curve and junction descriptions from sparse 3D data [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1206-1223.

GRIMM C, SMART W D. Shape classification and normal estimation for non-uniformly sampled, noisy point data [J]. Computers Graphics, 2011, 35(4): 904-915.

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.

DEY T K, LI Gang, SUN Jian. Normal estimation for point clouds: a comparison study for a Voronoi based method [C]∥Proceedings of the IEEE VGTC Symposium. Piscataway, NJ, USA: IEEE Computer Society,2005: 39-46.

MHATRE A N. Normal estimation and surface reconstruction of large point clouds [D]. Tallahassee FL, USA: Florida State University,2006.

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

FALCIDIENO B. AIM@SHAPE project presentation [C]∥Proceedings of the Shape Modeling Applications. Piscataway,NJ,USA:IEEE, 2004: 329.

EDELSBRUNNER H. 180 wrapped tubes[J]. Journal of Universal Computer Science, 2001, 7(5): 379-399.

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