SDCBNet: A sparse-to-dense cylinder-BEV fusion network for LiDAR semantic segmentation

LiDAR semantic segmentation is a fundamental task in autonomous driving and robot perception. Although the existing voxel-based methods using cylindrical partition can adapt to the distribution characteristics of point clouds, they are limited by the modeling within a single cylinder and have difficulty effectively capturing the horizontal context information across cylinders, resulting in insufficient discriminative ability for sparse and minority classes. To address this limitation, we propose SDCBNet, a sparse-to-dense Cylinder-BEV network that transforms sparse cylindrical features into dense BEV representations. This method introduces a BEV branch on the basis of Cylinder3D and establishes an explicit injection mechanism between cylindrical features and the BEV space in multiple stages of encoding-decoding to enhance the modeling ability of horizontal context. Further, this paper designs the sparse-to-dense feature aggregation module SDFA, which generates a robust BEV representation through a dual-branch strategy of maximum pooling and attention aggregation; at the same time, it proposes the conditional routing feature selection module CRFS, which selectively injects cross features to suppress irrelevant context interference. Experimental results show that this method achieves 69.5% mIoU on the SemanticKITTI validation set, a 3.4% improvement compared to the Baseline, and achieves significant gains in multiple context-sensitive categories. Ablation experiments further verify the effectiveness of each proposed module.