The curve-based lane representation is a popular approach in many lane detection methods, as it allows for the representation of lanes as a whole object and maximizes the use of holistic information about the lanes. However, the curves produced by these methods may not fit well with irregular lines, which can lead to gaps in performance compared to indirect representations such as segmentation-based or point-based methods. We have observed that these lanes are not intended to be irregular, but they appear zigzagged in the perspective view due to being drawn on uneven pavement. In this paper, we propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression. Specifically, we use a parameterized curve to represent lanes in the BEV space to reflect the original distribution of lanes. For the second part, since ground heights are determined by natural factors such as road conditions and are less holistic, we regress the ground heights of key points separately from the curve modeling. Additionally, we have unified the 2D and 3D lane detection tasks by designing a new framework and a series of losses to guide the optimization of models with or without 3D lane labels. Our experiments on 2D lane detection benchmarks (TuSimple and CULane), as well as the recently proposed 3D lane detection datasets (ONCE-3Dlane and OpenLane), have shown significant improvements. We will make our well-documented source code publicly available.

翻译：基于曲线的车道表示是许多车道检测方法中的主流方式，因其能将车道视为完整整体进行表征，最大化利用车道全局信息。然而，这类方法生成的曲线在应对不规则车道线时拟合效果欠佳，导致其性能不如基于分割或基于点的间接表示方法。我们观察到，这些车道线本身并非不规则，而是由于绘制在非平整路面上，在透视视角下呈现锯齿状。本文提出通过将车道检测任务分解为曲线建模与地面高度回归两部分的新方法：首先在BEV空间中采用参数化曲线表示车道，以还原车道的原始分布特征；其次，鉴于地面高度受路面状况等自然因素影响且缺乏全局性，我们将关键点地面高度的回归与曲线建模分离处理。此外，我们通过设计统一框架及系列损失函数，实现了2D与3D车道检测任务的融合，可对有无3D车道标签的模型进行协同优化。在2D车道检测基准（TuSimple、CULane）及最新3D车道检测数据集（ONCE-3Dlane、OpenLane）上的实验表明，该方法取得了显著性能提升。我们将公开具有详尽文档的源代码。