Multi-Granularity Feature Fusion For Point Cloud Semantic Segmentation Under Urban Scenes

Point cloud semantic segmentation plays a key role in scene understanding and digital twin cities tasks. This article proposed a multi-granularity feature fusion network (MGF-Net) for point cloud semantic segmentation. The model first used a cluster relation aggregation module to extract fine-grained point features and a 3D convolution module to extract coarse-grained voxel features, followed by feature aggregation via a multi-granularity feature adaptive fusion module. Finally, to further improve the model performance, MGF-Net used a global feature attention module to capture long-distance context information. The performance of MGF-Net was evaluated on three point cloud datasets of urban scenes, i.e., Toronto3D, WHU-MLS, and SensatUrban. The quantitative results showed that MGF-Net achieved 80.16%, 51.27%, and 54.20% of mIoU on these datasets, respectively. Moreover, the comparative results showed that the proposed MGF-Net outperformed the baseline for complex urban scenes, and obtained better point cloud semantic segmentation results.