GAF-Net: Geometric Contextual Feature Aggregation and Adaptive Fusion for Large-Scale Point Cloud Semantic Segmentation

Large-scale point cloud semantic segmentation is a challenging task due to the complexity and diversity of real-world 3D scenes. Most existing methods primarily rely on spatial coordinates to learn geometric representations without fully exploring local structural relationships. Additionally, the semantic gap between the encoder and decoder in segmentation networks is an important factor that constrains model performance. To address these challenges, we propose a novel network architecture called GAF-Net, which comprises a Geometric Contextual Feature Aggregation (GCFA) module and a Multi-scale Feature Adaptive Fusion (MFAF) module. The GCFA module consists of three primary blocks: (1) a Geometric Edge…