Laser-scanned point clouds of forests make it possible to extract valuable information for forest management. To consider single trees, a forest point cloud needs to be segmented into individual tree point clouds. Existing segmentation methods are usually based on hand-crafted algorithms, such as identifying trunks and growing trees from them, and face difficulties in dense forests with overlapping tree crowns. In this study, we propose \mbox{TreeLearn}, a deep learning-based approach for semantic and instance segmentation of forest point clouds. Unlike previous methods, TreeLearn is trained on already segmented point clouds in a data-driven manner, making it less reliant on predefined features and algorithms. Additionally, we introduce a new manually segmented benchmark forest dataset containing 156 full trees, and 79 partial trees, that have been cleanly segmented by hand. This enables the evaluation of instance segmentation performance going beyond just evaluating the detection of individual trees. We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software. An evaluation on the benchmark dataset shows that TreeLearn performs equally well or better than the algorithm used to generate its training data. Furthermore, the method's performance can be vastly improved by fine-tuning on the cleanly labeled benchmark dataset. The TreeLearn code is availabe from https://github.com/ecker-lab/TreeLearn. The data as well as trained models can be found at https://doi.org/10.25625/VPMPID.

翻译：激光扫描的森林点云使得提取森林管理的宝贵信息成为可能。为了考虑单株树木，森林点云需要被分割成独立的树木点云。现有分割方法通常基于手工设计的算法，例如识别树干并从中生长树木，但在树冠重叠的密林环境中面临困难。在本研究中，我们提出了TreeLearn，一种基于深度学习的森林点云语义分割与实例分割方法。与先前方法不同，TreeLearn以数据驱动方式在已分割的点云上进行训练，使其较少依赖预定义特征和算法。此外，我们引入了一个新的人工分割基准森林数据集，包含156棵完整树木和79棵部分树木，这些数据均经过手工精细分割。这使得实例分割性能的评估能够超越仅评估单木检测的范畴。我们使用Lidar360软件标记的6665棵树木的森林点云训练了TreeLearn。在基准数据集上的评估表明，TreeLearn的表现与生成其训练数据的算法相当甚至更优。此外，通过在精细标记的基准数据集上进行微调，该方法的性能可以得到大幅提升。TreeLearn代码可从https://github.com/ecker-lab/TreeLearn获取。数据及训练模型可在https://doi.org/10.25625/VPMPID中找到。