PGFormer: A Point Cloud Segmentation Network for Urban Scenes Combining Grouped Transformer and KPConv

Semantic segmentation of large-scale point cloud urban scenes faces significant challenges due to the complexity and diversity of object distribution. Transformer can effectively model the long-range dependencies in large-scale urban scenes, facilitating a comprehensive understanding of the overall structure and global context of urban scenes. Owing to the inherent strengths of the Transformer architecture, an increasing number of studies have applied it to the processing of large-scale point cloud data in complex scenes. But the excessive focus on global relationship inevitably leads to information redundancy, which can affect the model’s performance. To address this issue, we propose a novel Point Cloud Urban Scenes Semantic Segmentation network called PGFormer. This network consists of our proposed Group Representation Transformer (GRT) and KPConv, ensuring the enhancement of local key information while preserving global context. Specifically, in the proposed GRT block, we compute group-based and feature-based attention maps for Q and K across different groupings, and obtain the final attention output by integrating these with the global V, which has been pre-embedded with positional encoding derived from a triangular function. Our model is tested on two MLS (Paris-Lille-3D, Toronto3D) datasets and two ALS (Hessigheim 3D, ISPRS Vaihingen) datasets, and compared with a range of state-of-the-art (SOTA) methods, demonstrating the outstanding performance of our approach. Among various land cover segmentation tasks, our model achieves best or second-best results, particularly attaining the best OA (99.1%) and mF1-score (90.9%) on the Lille2 subset. Codes are available at https://github.com/Kange7/PGFormer.