用深度学习自动识别树木分枝结构，提升林业数据分析精度。
BranchPoseNet: Characterizing tree branching with a deep learning-based pose estimation approach
- 基于姿态估计算法处理激光点云，提取树干分枝关键点。
- 在实测树木数据上实现高精度分枝定位与结构参数计算。
- 适合林业监测、木材质量评估及树木全生命周期追踪。
本文提出一种自动化流程，利用基于姿态估计的深度学习模型，在近距激光扫描数据中检测树木轮生枝。精准识别轮生枝可揭示树木生长规律、评估木材质量，并有望作为生物特征标记，用于追踪树木在整个林业价值链中的状态。该流程将点云数据转换为截面图像，进而识别代表轮生枝和分枝的关键点。方法在破坏性采样个体树木的数据集上进行了测试，其中轮生枝位置由伐倒树木的茎干上标注。结果表明，该方法具有强大潜力，能准确识别轮生枝并精确计算关键结构指标，从单棵树点云中解锁新见解与更深层次的信息。
原文摘要 · Abstract (English)
This paper presents an automated pipeline for detecting tree whorls in proximally laser scanning data using a pose-estimation deep learning model. Accurate whorl detection provides valuable insights into tree growth patterns, wood quality, and offers potential for use as a biometric marker to track trees throughout the forestry value chain. The workflow processes point cloud data to create sectional images, which are subsequently used to identify keypoints representing tree whorls and branches along the stem. The method was tested on a dataset of destructively sampled individual trees, where the whorls were located along the stems of felled trees. The results demonstrated strong potential, with accurate identification of tree whorls and precise calculation of key structural metrics, unlocking new insights and deeper levels of information from individual tree point clouds.
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