发布128线激光雷达车道线数据集，支持高精度检测与评估
Pandar128 dataset for lane line detection
- 构建含5.2万帧图像与3.4万次激光扫描的公开数据集
- 提出轻量级方法SimpleLidarLane，在雨天等复杂场景表现优异
- 设计新评估指标IAM-F1，更精准衡量车道线匹配效果
我们提出了Pandar128，目前最大且公开的基于128线激光雷达的车道线检测数据集。数据集包含超过52,000帧摄像头图像和34,000次激光扫描，采集于德国多样真实路况。数据包含完整传感器标定（内参、外参）与同步里程计信息，支持投影、融合与时序建模等任务。为补充数据集，我们还引入SimpleLidarLane——一种结合鸟瞰图分割、聚类与多项式拟合的轻量级车道线重建基线方法。尽管结构简单，该方法在雨天、点云稀疏等挑战性条件下仍表现出色，表明模块化流程搭配高质量数据与合理评估可媲美复杂模型。此外，针对评估标准缺失问题，我们提出新的基于多项式匹配的评估指标——插值感知匹配F1（IAM-F1），在鸟瞰空间中采用插值感知横向匹配。所有数据与代码均已公开，以支持激光雷达车道检测研究的可复现性。
原文摘要 · Abstract (English)
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.
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