PineSegNet: A Deep Learning Method for Fine-Grained Wood-Leaf Segmentation of Masson Pine Point Clouds

Masson pine (Pinus massoniana) is a core timber species in the subtropical regions of Southern China, and the precise monitoring of its growth status and phenotypic characteristics is crucial for forest resource management. To address the segmentation challenges caused by intertwined wood-leaf structures and severe occlusion, this study proposes PineSegNet, an end-to-end deep learning framework for fine-grained semantic segmentation of Masson pine point clouds. The framework adopts an encoder–decoder architecture. In the encoding stage, a Hierarchical Local–Global Aggregation (HLGA) module is introduced to capture multi-scale features through progressive downsampling. This design suppresses noise and enhances high-frequency geometric details of branches. In the decoding stage, a Boundary Refinement Unit (BRU) is designed to effectively curb feature diffusion during the interpolation process, significantly enhancing the clarity of category boundaries. Furthermore, this study develops a composite loss function with a dual-supervisory mechanism, namely the CE-Dice Composite Loss (CDC-Loss), to tackle semantic confusion caused by inter-class geometric similarity and the challenges of extreme sample distribution imbalance. Experimental results demonstrate that on both the self-collected dataset (GAOFENG) and the public dataset (FOR-instance), PineSegNet exhibits exceptional robustness and generalization capability, outperforming all evaluated semantic segmentation baselines under the unified experimental setting. This study provides a reliable technical solution for precision forest resource inventory and tree structural analysis within the framework of smart forestry.