Generating accurate digital tree models from scanned environments is invaluable for forestry, agriculture, and other outdoor industries in tasks such as identifying biomass, fall hazards and traversability, as well as digital applications such as animation and gaming. Existing methods for tree reconstruction rely on feature identification (trunk, crown, etc) to heuristically segment a forest into individual trees and generate a branch structure graph, limiting their application to sparse trees and uniform forests. However, the natural world is a messy place in which trees present with significant heterogeneity and are frequently encroached upon by the surrounding environment. We present a general method for extracting the branch structure of trees from point cloud data, which estimates the structure of trees by adapting the methods of structural topology optimisation to find the optimal material distribution to support wind-loading. We present the results of this optimisation over a wide variety of scans, and discuss the benefits and drawbacks of this novel approach to tree structure reconstruction. Despite the high variability of datasets containing trees, and the high rate of occlusions, our method generates detailed and accurate tree structures in most cases.

翻译：从扫描环境中产生准确的数字树模型对于林业、农业和其他户外产业来说,在诸如查明生物量、秋天危险和可穿越性等任务以及诸如动画和赌博等数字应用方面,对林业、农业和其他户外产业来说是极为宝贵的。现有的树木重建方法依赖于地貌特征识别(树冠、冠等),以便把森林的湿度部分划入单个树木中,并生成一个分支结构图,将其应用限于稀树和统一的森林。然而,自然世界是一个混乱的地方,树木存在显著异质,经常受到周围环境的侵扰。我们提出了一个从点云数据中提取树木分支结构的一般方法,通过调整结构地形优化方法来估计树木的结构结构,以找到最佳物质分布来支持风浪。我们介绍了这种优化的结果,对广泛的扫描进行了介绍,并讨论了这种新颖的树木结构重建方法的好处和缺点。尽管含有树木的数据集变化很大,而且隔离率很高,我们的方法在多数情况下产生了详细和准确的树结构。</s>