RoadMark-AWAConv: Adaptive Weight-Anchor Convolution for Fine-Grained Semantic Segmentation of Road Marking Point Clouds

Road marking point cloud segmentation is essential for autonomous driving perception and high-definition map updating. However, road markings are typically represented by elongated, sparse, and irregular point structures, while non-uniform point density, occlusions, and pavement noise further increase the difficulty of fine-grained segmentation. To address these problems, we propose RoadMark-AWAConv, an adaptive weight-anchor convolution method for fine-grained road marking segmentation. The method introduces a geometry-constrained annular-domain anchor initialization strategy, hierarchical radius-based neighborhood aggregation, and normal vector direction calibration to better capture local geometric features and improve robustness to non-uniform density, occlusions, and pavement noise. Experimental results on a road marking point cloud dataset containing 13 semantic classes show that RoadMark-AWAConv achieves 71.94% mIoU, outperforming PointNet++, RandLA-Net, Point Transformer V3, and DeLA.