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 three-dimensional data, but foliage occlusion and severe class imbalance between woody and non-woody 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 multi-resolution 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.