Point-SCT: A Multiscale Spatial Convolution-Swin Transformer Network for Point Cloud Ground Filtering in Complex Mountainous Terrains

Deep learning-based point cloud segmentation methods have been extensively explored, but the majority focus either on local or global feature learning, with few integrating both. These integrated approaches have not been sufficiently explored in complex mountainous scenes with low feature heterogeneity. To address this gap, we propose a novel point-based multi-scale spatial Convolution-Swin Transformer network (Point-SCT). Point-SCT combines convolutional local geometric detail capture with global relationship modeling via dynamic window interactions in Transformer, enhancing ground filtering accuracy in challenging mountainous scenes. The encoder incorporates convolution-based Multi-Scale Local Feature Aggregation (MLFA) approach, integrating Local Geometric Feature Encoding (LGSE) and Diluent Pooling (DP) strategies to effectively aggregate local detailed geometric features while suppressing irrelevant feature vectors and enhancing the representation of low-heterogeneity feature. Additionally, the dynamic spatial window strategy within the Transformer facilitates the capture of long-range feature dependencies. To mitigate noise introduced by RGB in point cloud overlays and sharpen geometric distinctions between the ground and low-lying vegetation, we introduce Boundary Detector, Curvature, and Average Elevation (BCE) as prior inputs, replacing RGB. Finally, quantitative and qualitative analyses of Point-SCT are conducted on an airborne laser scanning (ALS) dataset from a mountainous area, with ablation studies validating the effectiveness of LGSE, DP and BCE. The comprehensive experiments demonstrate that Point-SCT robustly segments ground points in complex mountainous scenes, achieving state-of-the-art levels of accuracy and generalization.