Accurate 3-D Semantic Segmentation of Point Clouds for Intelligent Vehicles Based on Multiview Edge Guidance and Fusion

Semantic segmentation based on LiDAR plays an important role in the environment perception, path planning, and decision control of unmanned ground systems. However, in challenging environments with complex background interleaving, the high proportion of drivable areas and significant target elements pose serious challenges to segmentation tasks. Existing studies attempt to address these challenges but still grapple with issues such as interclass semantic conflicts and inaccurate edge segmentation. To address these challenges effectively, a reliable point cloud segmentation network driven by multiview fusion and multiedge guidance is specially customized. Specifically, the network’s point cloud multiview range image view (RIV) and bird’s eye view (BEV) encoding backbones use lightweight pyramid architectures to extract specific details and semantic features of multiscale point clouds. Subsequently, a dedicated multiview edge guidance module focuses on enhancing interclass edge differentiation features from RIV and BEV, respectively. These features are fused with the encoding backbone hierarchy, and the extraction of edge information is supervised by a new hybrid edge loss to maximize the consistency of semantic segmentation and predicted edges. Furthermore, a multiview and multiscale feature fusion module based on a multihead attention mechanism is introduced to enhance the potential complementarity and interaction of the captured features. Through comprehensive experiments and ablation studies on the SemanticKITTI and RELLIS-3D datasets, recognized in the field of autonomous driving, the results demonstrate that our custom method is competitive in terms of intersection over union (IoU) accuracy and real-time indicators compared with typical baseline methods.