Road markings are of great significance to road inventory management, intelligent transportation systems, high-definition maps (HD Maps), and autonomous driving. Most existing methods focus on extracting and classifying the road markings from mobile laser scanning (MLS) point clouds. Nevertheless, the performance suffers from the wear and incompleteness of road markings. Converting the extracted road marking points into a consistent representation with a sparse set of parameters needs extensive study as well. This paper presents a two-stage coarse-to-fine object detection and localization approach for automatically extracting and modeling road markings from mobile laser scanning (MLS) point clouds, which is robust to variations in reflective intensity, various point density, and partial occlusion. The first step is to use a general object detection network to detect bounding boxes with semantic labels of road markings on feature maps, which consists of information about intensity, elevation, and distance to the scanner. Next, accurate positions, orientations, and scales of candidate road markings are determined in the raw point clouds coordinate system through a shape matching operator that leverages the standard geometric structure and radiometric appearance of road markings. Finally, a re-ranking operator combining the coarse detection confidence and fine localization score is used to acquire the final road marking models. Comprehensive experiments revealed that the proposed method achieved an overall performance of 92.3% in recall and 95.1% in precision for extracting 12 types of road markings from urban scene point cloud datasets, even with worn and incomplete road markings. The modeling performance was 0.504 using the mIoU metric.

道路标线对道路库存管理、智能交通系统、高清地图（HD Maps）和自动驾驶具有重要意义。大多数现有方法侧重于从移动激光扫描 (MLS) 点云中提取和分类道路标记。然而，性能受到道路标记的磨损和不完整的影响。将提取的道路标记点转换为具有稀疏参数集的一致表示也需要广泛研究。本文提出了一种两阶段从粗到细的目标检测和定位方法，用于从移动激光扫描 (MLS) 点云中自动提取和建模道路标记，该方法对反射强度、各种点密度和部分遮挡的变化具有鲁棒性. 第一步是使用通用对象检测网络来检测具有特征地图上道路标记语义标签的边界框，其中包含有关强度、高程和到扫描仪的距离的信息。接下来，通过利用道路标记的标准几何结构和辐射外观的形状匹配算子，在原始点云坐标系中确定候选道路标记的准确位置、方向和比例。最后，结合粗检测置信度和精细定位分数的重新排序算子用于获取最终的道路标记模型。综合实验表明，所提出的方法在从城市场景点云数据集中提取12种道路标记时，总体召回率达到92.3%，准确率达到95.1%，即使有磨损和不完整的道路标记。建模性能为 0.504 使用mioU度量。