treeX：密集森林点云中的无监督树木实例分割
treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds
AI总结：
本研究提出修订版无监督treeX树木实例分割算法，新增地面与无人机载激光扫描参数预设，在多数据集上精度优于原版且接近深度学习方法，可作为资源高效替代方案或用于半自动标注，已开源实现。
摘要：
近距激光扫描可提供林分的详细3D采集结果，但需要高效软件来处理3D点云数据并提取单棵树木。尽管近期研究已引入深度学习方法用于树木实例分割，但这些方法需要大型标注数据集和大量计算资源。作为一种资源高效的替代方案，我们提出了treeX算法的修订版本，这是一种无监督方法，将基于聚类的树干检测与区域生长相结合以进行树冠勾勒。原始treeX算法是为个人激光扫描（PLS）数据开发的，我们提供了两个参数预设，一个适用于地面激光扫描（固定式地面激光扫描TLS和PLS），另一个适用于无人机载激光扫描（ULS）。我们在六个公开数据集（FOR-instance、ForestSemantic、LAUTx、NIBIO MLS、TreeLearn、Wytham Woods）上评估了该方法，并将其与六种开源方法（原始treeX、treeiso、RayCloudTools、ForAINet、SegmentAnyTree、TreeLearn）进行了比较。与原始treeX算法相比，我们的修订版本减少了运行时间并提高了精度，对于地面数据，实例检测F₁分数提升了0.11至0.49。对于ULS数据，我们的预设达到了0.58的F₁分数，而原始算法无法分割出任何正确的实例。对于TLS和PLS数据，我们的算法达到了与近期开源方法（包括深度学习方法）相近的精度。基于其算法设计，我们认为该方法有两个主要应用场景：（1）在数据特征与方法设计匹配（树干可见度和点密度充足）的场景中，作为深度学习方法的资源高效替代方案；（2）用于深度学习模型标签的半自动生成。为了促进更广泛的应用，我们在pointtree包中提供了开源Python实现。
英文摘要：
Close-range laser scanning provides detailed 3D captures of forest stands but requires efficient software for processing 3D point cloud data and extracting individual trees. Although recent studies have introduced deep learning methods for tree instance segmentation, these approaches require large annotated datasets and substantial computational resources. As a resource-efficient alternative, we present a revised version of the treeX algorithm, an unsupervised method that combines clustering-based stem detection with region growing for crown delineation. While the original treeX algorithm was developed for personal laser scanning (PLS) data, we provide two parameter presets, one for ground-based laser scanning (stationary terrestrial - TLS and PLS), and one for UAV-borne laser scanning (ULS). We evaluated the method on six public datasets (FOR-instance, ForestSemantic, LAUTx, NIBIO MLS, TreeLearn, Wytham Woods) and compared it to six open-source methods (original treeX, treeiso, RayCloudTools, ForAINet, SegmentAnyTree, TreeLearn). Compared to the original treeX algorithm, our revision reduces runtime and improves accuracy, with instance detection F$_1$-score gains of +0.11 to +0.49 for ground-based data. For ULS data, our preset achieves an F$_1$-score of 0.58, whereas the original algorithm fails to segment any correct instances. For TLS and PLS data, our algorithm achieves accuracy similar to recent open-source methods, including deep learning. Given its algorithmic design, we see two main applications for our method: (1) as a resource-efficient alternative to deep learning approaches in scenarios where the data characteristics align with the method design (sufficient stem visibility and point density), and (2) for the semi-automatic generation of labels for deep learning models. To enable broader adoption, we provide an open-source Python implementation in the pointtree package.
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