Pandar128 dataset for lane line detection
This work addresses the lack of standardized data and evaluation for lane detection in autonomous driving, though it is incremental by building on existing datasets and methods.
The authors introduced Pandar128, a large public dataset for lane line detection with over 52,000 camera frames and 34,000 LiDAR scans, and proposed SimpleLidarLane, a baseline method that achieves strong performance in challenging conditions, along with a new evaluation metric IAM-F1.
We present Pandar128, the largest public dataset for lane line detection using a 128-beam LiDAR. It contains over 52,000 camera frames and 34,000 LiDAR scans, captured in diverse real-world conditions in Germany. The dataset includes full sensor calibration (intrinsics, extrinsics) and synchronized odometry, supporting tasks such as projection, fusion, and temporal modeling. To complement the dataset, we also introduce SimpleLidarLane, a light-weight baseline method for lane line reconstruction that combines BEV segmentation, clustering, and polyline fitting. Despite its simplicity, our method achieves strong performance under challenging various conditions (e.g., rain, sparse returns), showing that modular pipelines paired with high-quality data and principled evaluation can compete with more complex approaches. Furthermore, to address the lack of standardized evaluation, we propose a novel polyline-based metric - Interpolation-Aware Matching F1 (IAM-F1) - that employs interpolation-aware lateral matching in BEV space. All data and code are publicly released to support reproducibility in LiDAR-based lane detection.