标题： Depth3DLane：融合单目3D车道检测与自监督单目深度估计 显示英文标题

标题： Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimation

摘要： 单目3D车道检测对于自动驾驶至关重要，但由于缺乏显式的空间信息而具有挑战性。多模态方法依赖于昂贵的深度传感器，而采用全监督深度网络的方法则依赖于难以大规模收集的真实深度数据。此外，现有方法假设可以获取相机参数，在像众包高清（HD）车道映射这样的场景中限制了其适用性。为解决这些限制，我们提出了Depth3DLane，一种新颖的双路径框架，该框架结合自监督单目深度估计来提供显式的结构信息，而无需昂贵的传感器或额外的真实深度数据。利用自监督深度网络获得场景的点云表示，我们的鸟瞰图路径提取显式的空间信息，而我们的前视图路径同时提取丰富的语义信息。Depth3DLane然后使用3D车道锚点从两个路径中采样特征并推断准确的3D车道几何形状。此外，我们将框架扩展到逐帧预测相机参数，并引入一个理论上有依据的拟合过程以在逐段基础上增强稳定性。大量实验表明，Depth3DLane在OpenLane基准数据集上实现了具有竞争力的性能。此外，实验结果表明，使用学习得到的参数而不是真实参数，使Depth3DLane能够在相机校准不可行的场景中应用，这与之前的方法不同。 显示英文摘要

摘要： Monocular 3D lane detection is essential for autonomous driving, but challenging due to the inherent lack of explicit spatial information. Multi-modal approaches rely on expensive depth sensors, while methods incorporating fully-supervised depth networks rely on ground-truth depth data that is impractical to collect at scale. Additionally, existing methods assume that camera parameters are available, limiting their applicability in scenarios like crowdsourced high-definition (HD) lane mapping. To address these limitations, we propose Depth3DLane, a novel dual-pathway framework that integrates self-supervised monocular depth estimation to provide explicit structural information, without the need for expensive sensors or additional ground-truth depth data. Leveraging a self-supervised depth network to obtain a point cloud representation of the scene, our bird's-eye view pathway extracts explicit spatial information, while our front view pathway simultaneously extracts rich semantic information. Depth3DLane then uses 3D lane anchors to sample features from both pathways and infer accurate 3D lane geometry. Furthermore, we extend the framework to predict camera parameters on a per-frame basis and introduce a theoretically motivated fitting procedure to enhance stability on a per-segment basis. Extensive experiments demonstrate that Depth3DLane achieves competitive performance on the OpenLane benchmark dataset. Furthermore, experimental results show that using learned parameters instead of ground-truth parameters allows Depth3DLane to be applied in scenarios where camera calibration is infeasible, unlike previous methods.