There are numerous emerging applications for digitizing trees using terrestrial and aerial laser scanning, particularly in the fields of tree crop agriculture and forestry. Interpretation of LiDAR point clouds is increasingly relying on data-driven methods (such as supervised machine learning) that rely on large quantities of hand-labelled data. As this data is potentially expensive to capture, and difficult to clearly visualise and label manually, a means of supplementing real LiDAR scans with simulated data is becoming a necessary step in realising the potential of these methods. We present an open source tool, SimTreeLS (Simulated Tree Laser Scans), for generating point clouds which simulate scanning with user-defined sensor, trajectory, tree shape and layout parameters. Upon simulation, material classification is kept in a pointwise fashion so leaf and woody matter are perfectly known, and unique identifiers separate individual trees, foregoing post-simulation labelling. This allows for an endless supply of procedurally generated data with similar characteristics to real LiDAR captures, which can then be used for development of data processing techniques or training of machine learning algorithms. To validate our method, we compare the characteristics of a simulated scan with a real scan using similar trees and the same sensor and trajectory parameters. Results suggest the simulated data is significantly more similar to real data than a sample-based control. We also demonstrate application of SimTreeLS on contexts beyond the real data available, simulating scans of new tree shapes, new trajectories and new layouts, with results presenting well. SimTreeLS is available as an open source resource built on publicly available libraries.

有许多使用地面和空中激光扫描对树木进行数字化的新兴应用，特别是在树木作物农业和林业领域。LiDAR 点云的解释越来越依赖于依赖于大​​量手工标记数据的数据驱动方法（例如监督机器学习）。由于捕获这些数据可能很昂贵，并且难以手动清晰地可视化和标记，因此用模拟数据补充真实 LiDAR 扫描的方法正在成为实现这些方法潜力的必要步骤。我们提出了一个开源工具 SimTreeLS（模拟树激光扫描），用于生成点云，使用用户定义的传感器、轨迹、树形和布局参数模拟扫描。经模拟，材料分类以逐点方式保持，因此叶子和木质物质是完全已知的，并且独特的标识符将单独的树木分开，无需模拟后标记。这允许源源不断地提供与真实 LiDAR 捕获具有相似特征的程序生成数据，然后可将其用于数据处理技术的开发或机器学习算法的训练。为了验证我们的方法，我们使用类似的树和相同的传感器和轨迹参数比较了模拟扫描和真实扫描的特征。结果表明，与基于样本的对照相比，模拟数据与真实数据的相似度明显更高。我们还展示了 SimTreeLS 在可用真实数据之外的上下文中的应用，模拟新树形状、新轨迹和新布局的扫描，结果很好。SimTreeLS 可作为基于公开可用库的开源资源提供。