Efficient and Lightweight Semantic Segmentation Network for Land Cover Point Cloud With Local-Global Feature Fusion

Utilizing deep learning techniques to extract high-precision features from point clouds is essential for accurately capturing land cover information, which is instrumental in urban planning and environmental conservation. Despite delivering high-accuracy outcomes in the semantic segmentation of extensive terrestrial point clouds, prevalent methodologies encounter considerable hurdles, particularly in training and inference duration, as well as the associated hardware expenses. To solve these issues, this paper introduces an efficient and lightweight deep learning network called Uniform Voxelization Geometric Enhancement and Local-Global Feature Fusion Network (VEF-Net). VEF-Net is designed to improve the training and inference efficiency of large-scale point cloud semantic segmentation while maintaining accuracy. Uniform voxel down-sampling is employed to discretize point clouds, resulting in a substantial enhancement in computational and memory performance. To counteract potential information loss due to voxel down-sampling, VEF-Net integrates a mechanism unit to enhance local geometric features, enriching point cloud data. Furthermore, it incorporates a Local-global feature fusion module, adeptly capturing the global contextual relationships within the point cloud. Experimental results show that VEF-Net achieved excellent performance on both the proprietary Bengbu dataset and the publicly available Toronto-3D dataset. VEF-Net maintained comparable segmentation accuracy to mainstream models while operating at a lower computational cost, and it demonstrated significant advantages in certain key tasks. On the Toronto3D dataset, VEF-Net achieved an IoU of 20.4% in the road marking category. Additionally, VEF-Net demonstrated lower model parameters and computational complexity (FLOPs), achieving training speeds 12 times faster and inference speeds 16.8 times faster than Point Transformer.