A Point Cloud Segmentation Algorithm Using Local Convexity and Octree

您当前的位置：

首页 >

文章列表页 >

A Point Cloud Segmentation Algorithm Using Local Convexity and Octree

更新时间：2025-07-09

- A Point Cloud Segmentation Algorithm Using Local Convexity and Octree
- Vol. 46, Issue 10, Pages: 60-65(2012)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Online First：10 October 2012，
  
  Published：2012
- 稿件说明：
移动端阅览
A Point Cloud Segmentation Algorithm Using Local Convexity and Octree[J]. 2012, 46(10): 60-65.
DOI：

A Point Cloud Segmentation Algorithm Using Local Convexity and Octree[J]. 2012, 46(10): 60-65. DOI：

摘要

针对粗糙点云分割效果差的问题

提出了一种采用八叉树和局部凸性的点云分割算法.该算法首先通过仪器扫描得到仅包含坐标信息的点云数据

然后对点云进行法向量估算

并根据点云的法向量信息进行八叉树初始分割得到面片

最后根据面片之间的局部凸性特征进行融合

得到最终的分割结果.与其他同类算法相比

采用八叉树和局部凸性的点云分割算法不仅能有效地减少曲面数量

而且在曲面质量上也优于同类算法.采用塔身震落石块的点云数据进行的实验表明

该算法在处理分布较均匀的闭合点云数据时

能够有效减少最终的曲面个数

且面片的质量与手工分割拟合度达到90%以上.

Abstract

An algorithm of point cloud segmentation using octree and local convexity is proposed to deal with the fact that normal segmentation methods have poor effect on coarse point clouds. The proposed algorithm is composed of three steps. Firstly

point clouds with 3D coordinates are obtained using special instruments. Then

normal estimation approaches are used to get the normal vector and to segment the point clouds into patches by octree. Finally

the neighboring patches are merged if local convexity is held. Compared with other works

the proposed algorithm can reduce the number of surface effectively and produce surfaces with better quality. Experimental results show that the proposed algorithm can better segment the nearly and equally distributed data into several meaningful surfaces and 90% of the surfaces is the same as that produced by human operation.

关键词

Keywords

references

费文龙,魏志辉. Mumford-Shah模型在卫星图象分割中的应用[J]. 中国图象图形学报, 2009, 14(4): 598-603.

FEI Wenlong, WEI Zhihui, An application of Mumford-Shah model in segmentation of satellite cloud image [J]. Journal of Image and Graphics, 2009, 14(4): 598-603.

MOOSMANN F, PINK O, STILLER C. Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion[C]∥Proceedings of IEEE Intelligent Vehicles Symposium. Piscataway, NJ, USA: IEEE, 2009: 215-220.

TSENG Yi-Hsing, WANG Miao. Automatic plane extraction from LIDAR data based on octree splitting and merging segmentation [C]∥ Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Piscataway, NJ, USA: IEEE, 2005: 3281-3284.

WANG Miao, TSENG Yi-Hsing. Automatic segmentation of Lidar data into coplanar point clusters using an octree-based split-and-merge algorithm [J]. Photogramme Thrice Engineering and Remote Sensing, 2010, 76(4): 407-420.

DOUILARD B, UNDERWOOD J, KUNTZ N, et al. On the segmentation of 3D LIDAR point clouds [C]∥ Proceedings of IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2011: 2798-2805.

DAVID S, JIRI Z. Graph cut based point-cloud segmentation for polygonal reconstruction [C]∥ Proceedings of the 5th International Symposium on Advances in Visual Computing. Heidelberg, Germang: Springer Verlag, 2009: 218-227.

MARIO R, MARKUS V. Point cloud segmentation based on radial reflection [C]∥Proceedings of 13th International Conference on Computer Analysis of Images and Patterns. Heidelberg, Germany: Springer Verlag, 2009: 955-962.

JAGANNATHAN A,MILLIER E L. Three-dimensional surface mesh segmentation using curvedness-based region growing approach [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(12): 2195-2204.

STEINHAUSER D, RUEPP O, BURSCHKA D. Motion segmentation and scene classification from 3D LIDAR data [C]∥ Proceedings of IEEE Intelligent Vehicles Symposium. Piscataway, NJ, USA: IEEE, 2008: 398-403.

ZHU Xiaolong, ZHAO Huijing, LIU Yiming, et al. Segmentation and classification of range image from an intelligent vehicle in urban environment [C]∥Proceedings of 23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems. Piscataway, NJ, USA: IEEE Computer Society, 2010: 1457-1462.

PANG Xufang, PANG Mingyong. An algorithm for extracting geometric features from point cloud[C]∥Proceedings of 2009 International Conference on Information Management, Innovation Management and Industrial Engineering. Piscataway, NJ, USA: IEEE Computer Society, 2009: 78-83.

张连伟,李炎,刘肖林,等. 基于局部曲面拟合的散乱点云简化方法[J]. 计算机工程与科学,2010,32(12): 65-68.

ZHANG Lianwei, LI Yan, LIU Xiaolin, et al. A simplification method for cloud points based on local surface fitting[J]. Computer Engineering Science, 2010, 32(12): 65-68.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Mutil-Octree Searching Algorithm for Different Size Particle Collision in Polydisperse Systems

An Accelerating Algorithm for 3D Texture Volume Rendering with Octree Encoding

Lossless Geometry Coding Method for Dynamic Point Clouds under Octree Structure

Turbulent Kinetic Energy Dissipation Mechanism of Supercritical Carbon Dioxide Leakage Flow in a Scallop Bionic Seal Cavity

Electrothermal Coupling Research on the Evolutionary Law of Anti-Corona Performance for High-Altitude Air-Cooled Generators

Related Author

鲁录义 1

周逢森 1

冯诗愚 1

顾兆林 2

邹华

高新波

吕新荣

WANG Zhecheng

Related Institution

School of Energy and Power Engineering, Xi'an Jiaotong University

School of Electronic Engineering, Xidian University

School of Electronics and Information, Northwestern Polytechnical University

School of Engineering, Royal Melbourne Institute of Technology

School of Telecommunication Engineering, Xidian University

⁰