南京林业大学学报(自然科学版) ›› 2016, Vol. 40 ›› Issue (02): 160-166.doi: 10.3969/j.issn.1000-2006.2016.02.027

• 研究论文 • 上一篇    下一篇

基于力场的点云树木骨架提取方法

张 冬1,2,云 挺1,2,薛联凤1*,阮宏华2   

  1. 1.南京林业大学信息科学技术学院,江苏 南京 210037;
    2.南京林业大学生物与环境学院, 南京林业大学南方现代林业协同创新中心,江苏 南京 210037
  • 出版日期:2016-04-18 发布日期:2016-04-18
  • 基金资助:
    收稿日期:2015-04-27 修回日期:2015-08-06
    基金项目:国家自然科学基金项目(31300472); 江苏省自然科学基金项目(BK2012418); 江苏高校优势学科建设工程资助项目(PAPD); 国家重点基础研究发展计划(2012CB416904)
    第一作者:张冬(shang_jia927@163.com)。*通信作者:薛联凤(285201972@qq.com),副教授。
    引文格式:张冬,云挺,薛联凤,等. 基于力场的点云树木骨架提取方法[J]. 南京林业大学学报(自然科学版),2016,40(2):160-166.

Skeleton extraction for point cloud trees based on force field

ZHANG Dong1,2, YUN Ting1,2, XUE Lianfeng1*, RUAN Honghua2   

  1. 1. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037,China;
    2. Co-Innovation Center Sustainable Forestry Studies in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjin
  • Online:2016-04-18 Published:2016-04-18

摘要: 树木骨架是树木仿真及建模的基础,笔者根据树木的拓扑原理,直接利用地面激光雷达扫描获得的单木点云数据,提出了一种基于物理学中力场概念的点云数据树木骨架提取方法:首先对点云树木模型运用空间层次剖分的方法进行分层,根据点云的邻域关系建立基于树木特征点的简化表示,然后根据计算点的测地距离对树木特征点进行连接,再运用力场将位于树木表面的骨架连线压缩至树木内部,最后根据骨架夹角阈值对骨架进行顺滑得到最终的树木骨架。研究显示,将该方法分别应用在含笑树和樱花树的骨架提取中效果较好,相比同类算法效率较高。

Abstract: Skeleton extraction is a fundamental part in simulation and modeling of point cloud trees. According to topology, a novel skeleton extraction method, which based on physical force field, is first proposed in this paper. First, the point cloud tree is layered by space subdivision method, and a simplified representation of the feature points is established under the neighbor relationships. Next, the feature points are connected by calculating the geodesic distance. Then, the surface skeleton is compressed into trees by applying force field. Finally, the final skeleton will be acquired by polishing it according to a threshold setting. The experimental results indicate that the algorithm can provide an satisfactory result, and the algorithm performance is verified using datasets ofmichelia and cherry trees.

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