用3D关键点图谱检测车道，提升复杂路况下的泛化能力。
GLane3D : Detecting Lanes with Graph of 3D Keypoints
- 通过预测关键点并连接形成3D车道图谱
- 在Apollo和OpenLane上均超越现有最优方法，F1更高
- 适合需要强泛化能力的自动驾驶车道识别场景
准确高效的3D空间车道检测对自动驾驶系统至关重要，其中鲁棒泛化能力是核心要求。由于全球车道结构差异大，算法需识别多种未见车道模式，传统自顶向下方法依赖训练数据，难以应对新属性。为此，本文提出一种新方法：先检测车道关键点，再预测其序列连接以构建完整3D车道。每个关键点对保持车道连续性至关重要，采用偏移机制允许相邻网格共同预测同一关键点，生成多候选。利用PointNMS消除重叠候选，减少鸟瞰图（BEV）图谱冗余，降低连接估计计算开销。模型在Apollo和OpenLane数据集上均超越现有最先进方法，表现出更高F1分数，且在跨数据集评估中（以OpenLane训练、在Apollo测试）展现出更强泛化能力，优于以往方法。
原文摘要 · Abstract (English)
Accurate and efficient lane detection in 3D space is essential for autonomous driving systems, where robust generalization is the foremost requirement for 3D lane detection algorithms. Considering the extensive variation in lane structures worldwide, achieving high generalization capacity is particularly challenging, as algorithms must accurately identify a wide variety of lane patterns worldwide. Traditional top-down approaches rely heavily on learning lane characteristics from training datasets, often struggling with lanes exhibiting previously unseen attributes. To address this generalization limitation, we propose a method that detects keypoints of lanes and subsequently predicts sequential connections between them to construct complete 3D lanes. Each key point is essential for maintaining lane continuity, and we predict multiple proposals per keypoint by allowing adjacent grids to predict the same keypoint using an offset mechanism. PointNMS is employed to eliminate overlapping proposal keypoints, reducing redundancy in the estimated BEV graph and minimizing computational overhead from connection estimations. Our model surpasses previous state-of-the-art methods on both the Apollo and OpenLane datasets, demonstrating superior F1 scores and a strong generalization capacity when models trained on OpenLane are evaluated on the Apollo dataset, compared to prior approaches.
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