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：从扫描环境中生成精确的数字树木模型对于林业、农业及其他户外行业在识别生物量、坠落危险和可穿越性等任务中至关重要，同时也应用于动画、游戏等数字领域。现有的树木重建方法依赖特征识别（如树干、树冠等）以启发式方式将森林分割为单棵树木并生成分支结构图，这限制了其在稀疏树木和均匀森林中的应用。然而，自然界环境复杂多变，树木呈现显著异质性，并常受周围环境侵蚀。我们提出一种从点云数据中提取树木分支结构的通用方法，该方法通过调整结构拓扑优化的方法，估计树木的结构以找到支撑风荷载的最佳材料分布。我们展示了该方法在不同扫描结果上的优化效果，并讨论了这种新型树木结构重建方法的优缺点。尽管包含树木的数据集高度可变且遮挡率高，我们的方法在大多数情况下仍能生成详细且准确的树木结构。