Autonomous driving paper index
A Compact 3D Lane Auto Labeling Method for Autonomous Driving
One-line summary
In this paper, we propose a compact road-reconstruction based 3D lane auto labeling method for autonomous driving, termed R2-3DLane.
Engineering notes
Key topics: autonomous driving, lane detection, semantic segmentation, lidar, point cloud, waymo open dataset, waymo. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
In recent years, 3D lane detection has become one of the most crucial and challenging tasks in the field of autonomous driving. While most research focuses on real-time or onboard scenario, there is still limited exploration of using machines to automatically generate high-precision 3D lane labels. Existing 3D lane labeling approaches struggle in efficiency and accuracy due to the single frame interactive annotation paradigm or information restrictions. In this paper, we propose a compact road-reconstruction based 3D lane auto labeling method for autonomous driving, termed R2-3DLane. It involves Lidar point cloud semantic segmentation, lane instance generation and post-processing. Additionally, we design a coarse-to-fine multi-stage Lidar point cloud clustering procedure that converts the lane semantic information into the lane instance annotations. The evaluation results on Waymo open dataset prove the effectiveness of the proposed method.
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