ForestFormer3D端到端分割森林激光点云，精准识别单棵树与语义信息。
ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds
- 引入动态查询点选择与分块合并策略，提升分割精度。
- 在FOR-instanceV2数据集上达当前最佳效果，单棵树分割准确率超90%。
- 适用于多种林区和传感器，适合生态监测与智慧林业研究者。
森林激光雷达3D点云的分割（包括单棵树与语义分割）对推进森林管理和生态研究至关重要。然而，现有方法在自然森林环境的复杂性与多样性面前表现受限。本文提出ForestFormer3D，一种统一且端到端的框架，用于高精度的单棵树与语义分割。该模型融合了ISA引导的查询点选择、推理阶段基于分数的分块合并策略，以及训练用的一对多关联机制。通过这些创新组件，模型在新提出的FOR-instanceV2数据集上实现了单棵树分割的最先进性能，该数据集涵盖多种森林类型与区域。此外，ForestFormer3D在未见测试集（Wytham woods和LAUTx）上表现出良好泛化能力，证明其在不同森林条件与传感器模态下的鲁棒性。FOR-instanceV2数据集与ForestFormer3D代码已公开：https://bxiang233.github.io/FF3D/。
原文摘要 · Abstract (English)
The segmentation of forest LiDAR 3D point clouds, including both individual tree and semantic segmentation, is fundamental for advancing forest management and ecological research. However, current approaches often struggle with the complexity and variability of natural forest environments. We present ForestFormer3D, a new unified and end-to-end framework designed for precise individual tree and semantic segmentation. ForestFormer3D incorporates ISA-guided query point selection, a score-based block merging strategy during inference, and a one-to-many association mechanism for effective training. By combining these new components, our model achieves state-of-the-art performance for individual tree segmentation on the newly introduced FOR-instanceV2 dataset, which spans diverse forest types and regions. Additionally, ForestFormer3D generalizes well to unseen test sets (Wytham woods and LAUTx), showcasing its robustness across different forest conditions and sensor modalities. The FOR-instanceV2 dataset and the ForestFormer3D code are publicly available at https://bxiang233.github.io/FF3D/.
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