Dynamic Noise Injection for Geometry-Guided in Forest Point Cloud Semantic Segmentation

Accurate forest resource assessment is critical but challenging due to forest structural complexity. Unmanned aerial vehicle laser scanning provides detailed 3-D data, but foliage occlusion and severe class imbalance between woody and nonwoody component hinder segmentation. Conventional methods often misclassify uncertain points as leaves due to imbalance, while existing approaches overemphasizing woody features risk structural bias. To address this, we propose a novel geometric-semantic enhancement network with a dual-branch architecture. The first branch employs a dynamic mask generation mechanism simulating natural forest occlusion patterns and contextual modeling of occluded regions, enhancing cross-class feature learning and mitigating imbalance. The second branch integrates a cross-scale attention feature fusion mechanism within an encoder–decoder framework, effectively preserving multiresolution geometric features and minimizing information loss. Extensive experiments on SOUL and For-Instance datasets demonstrate the superiority of proposed network. On the SOUL dataset, it improves Specificity index by 8.3% and G-mean by 1.0% over baselines. Notably, the proposed network achieves high-quality segmentation on For-Instance without fine-tuning, highlighting its strong generalization and practical utility for diverse forests.