This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyperparemeters importance. It introduces a comprehensive and modular approach to processing laser points clouds in Forestry. We tested it on two independent datasets. The first area was located in an actively managed boreal coniferous dominated forest in V{\aa}ler, Norway, 16 circular plots of 400 square meters were selected to cover a range of forest conditions in terms of species composition and stand density. We trained a model based on Pointnet++ architecture which achieves 0.92 F1-score in semantic segmentation. As a second step in our pipeline we used graph-based approach for instance segmentation which reached F1-score approx. 0.6. The optimization allowed to further boost the performance of the pipeline by approx. 4 \% points.

翻译：本文介绍了一种名为Point2Tree的新型框架，该框架融入了包含语义分割、实例分割和超参数重要性优化分析的三阶段流程。它提出了一种全面且模块化的林业激光点云处理方法。我们使用两个独立数据集对其进行了测试。第一个区域位于挪威Våler的一片积极管理的北方针叶优势林，选取了16个面积为400平方米的圆形样地，以覆盖不同树种组成和林分密度的森林条件。我们基于Pointnet++架构训练了一个模型，在语义分割中达到了0.92的F1分数。作为流程的第二步，我们采用基于图的实例分割方法，获得了约0.6的F1分数。优化进一步将流程性能提升了约4个百分点。