Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (36): 213-215.DOI: 10.3778/j.issn.1002-8331.2010.36.059

• 图形、图像、模式识别 • Previous Articles     Next Articles

Novel algorithm for fast extracting edges from massive point clouds

WANG Zong-yue1,2,MA Hong-chao2,XU Hong-gen3,YANG Zhi-wei1   

  1. 1.Computer Engineering College,Jimei University,Xiamen,Fujian 361021,China
    2.School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430072,China
    3.China Aero Geophysical Survey and Remote Sensing Center for Land and Resources,Beijing 100083,China
  • Received:2010-06-28 Revised:2010-10-22 Online:2010-12-21 Published:2010-12-21
  • Contact: WANG Zong-yue



  1. 1.集美大学 计算机工程学院,福建 厦门 361021
    2.武汉大学 遥感信息工程学院,武汉 430072
    3.中国国土资源航空物探遥感中心,北京 100083
  • 通讯作者: 王宗跃

Abstract: A novel algorithm is proposed for fast extracting edges from massive point clouds in this paper.First,organize point cloud data in grid form,and then exclude non-edge discrete points,at last extract edges in the condition of Alpha Shapes.The algorithm sacrifices a small amount time on grid data organization,while saving a lot of Alpha Shapes conditional time,thereby significantly improves the efficiency.This algorithm has been realized in the VC environment,and experimental results show that the algorithm can extract outer boundary,holes and other functions,and is high efficiency.

Key words: Light Detection And Ranging(LiDAR), point clouds, edges

摘要: 提出一种海量点云边缘快速提取算法。该算法先对点云数据进行格网组织,然后排除非边缘的离散点,最后采用Alpha Shapes判断条件提取边缘。该算法牺牲少量格网数据组织时间,节约大量的Alpha Shapes条件判断时间,从而显著提高算法效率。在VC环境下实现了该算法,实验结果表明该算法不仅具有提取外边界、空洞等功能,而且效率高。

关键词: 机载激光雷达, 点云, 边缘

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