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.

本文提出了一种新的车道检测方法，通过将车道检测任务分解为曲线建模和地面高度回归两个部分，使用参数化曲线在 BEV 空间中表示车道，以反映车道的原始分布，并通过设计新的框架和一系列损失函数统一了 2D 和 3D 车道检测任务，在 2D 车道检测基准数据集 (TuSimple 和 CULane) 以及最近提出的 3D 车道检测数据集 (ONCE-3Dlane 和 OpenLane) 上取得了显著的改进。