| 激光雷达点云树木建模研究进展与展望 |
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| 引用本文： | 曹伟,陈动,史玉峰,曹震,夏少波.激光雷达点云树木建模研究进展与展望[J].武汉大学学报(信息科学版),2021(2). |
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| 作者姓名： | 曹伟 陈动 史玉峰 曹震 夏少波 |
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| 作者单位： | 南京林业大学土木工程学院;卡尔加里大学工学院地球空间信息工程系 |
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| 基金项目： | 国家自然科学基金(41971415);江苏省自然科学基金(BK20201387,BK20180779);遥感科学国家重点实验室开放基金资助(OFSLRSS202010)。 |
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| 摘 要： | 三维树木几何模型是数字城市与数字林业工程的重要组成部分。针对点云树木建模,深入分析了基于广义(泛在)激光雷达点云的树木模型重建方法,提出了聚类思想建模、图论方法建模、先验假设建模、拉普拉斯算子建模与轻量化表达建模5类建模体系,归纳总结了不同建模体系在树冠枝干的细节表达、建模算法性能、树木模型的多层次细节表达、建模体系综合评价方法等方面存在的共性问题,针对模型信息表达完整性、模型多层次细节重建、融合广义(泛在)点云的树木建模与模型、算法的综合评价研究4个方面给出一些可能的解决方案,并提出三维激光树木几何重建潜在的建模方向。
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| 关 键 词： | 激光雷达 广义(泛在)点云 三维树木建模 树木骨架 树冠重建 数字林业 |
Progress and Prospect of LiDAR Point Clouds to 3D Tree Models |
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| CAO Wei,CHEN Dong,SHI Yufeng,CAO Zhen,XIA Shaobo.Progress and Prospect of LiDAR Point Clouds to 3D Tree Models[J].Geomatics and Information Science of Wuhan University,2021(2). |
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| Authors: | CAO Wei CHEN Dong SHI Yufeng CAO Zhen XIA Shaobo |
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| Institution: | (College of Civil Engineering,Nanjing Forestry University,Nanjing 210037,China;Department of Geomatics Engineering,Schulich School of Engineering,University of Calgary,Calgary T2N 1N4,Canada) |
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| Abstract: | 3 D geometric tree models are of great interest to many applications, such as digital city and digital forestry, among others. Of late, light detection and ranging(LiDAR) technique has been extensively used to capture the geometric shapes of the trees from the outdoor scenes. Despite two decades of research,tree modeling algorithms and the created tree models are still far from being satisfactory. In this paper, we review most of the mainstream tree modeling algorithms by using ubiquitous point clouds. These tree modeling algorithms can be roughly classified into five categories, including clustering-based method, graph-based method, a priori assumption-based method, Laplace’s method, and lightweight expression-based method. In each category, we analyze the strengths and challenges of the tree modeling algorithms. Afterwards, some possible tree modeling methods and strategies are given to overcome the potential limitations in terms of detailed skeleton representation, robustness and scalability, level of details(LoDs) representation,and tree modeling evaluation. We finally propose a few suggestions for future research topics in tree modeling. |
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| Keywords: | light detection and ranging ubiquitous point clouds 3D tree modeling tree skeleton tree crown reconstruction digital forestry |
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