Detecting 3D lanes from the camera is a rising problem for autonomous
vehicles. In this task, the correct camera pose is the key to generating
accurate lanes, which can transform an image from perspective-view to the
top-view. With this transformation, we can get rid of the perspective effects
so that 3D lanes would look similar and can accurately be fitted by low-order
polynomials. However, mainstream 3D lane detectors rely on perfect camera poses
provided by other sensors, which is expensive and encounters multi-sensor
calibration issues. To overcome this problem, we propose to predict 3D lanes by
estimating camera pose from a single image with a two-stage framework. The
first stage aims at the camera pose task from perspective-view images. To
improve pose estimation, we introduce an auxiliary 3D lane task and geometry
constraints to benefit from multi-task learning, which enhances consistencies
between 3D and 2D, as well as compatibility in the above two tasks. The second
stage targets the 3D lane task. It uses previously estimated pose to generate
top-view images containing distance-invariant lane appearances for predicting
accurate 3D lanes. Experiments demonstrate that, without ground truth camera
pose, our method outperforms the state-of-the-art perfect-camera-pose-based
methods and has the fewest parameters and computations. Codes are available at
this https URL

本研究提出了一种基于两个阶段的框架，通过从单个图像中估计相机姿态来预测 3D 车道，使用辅助的 3D 车道任务和几何约束来获得多任务学习的好处，增强 3D 和 2D 之间的一致性和在这两个任务之间的兼容性，在没有地面真值相机姿态的情况下，我们的方法胜过基于完美相机姿态的最先进方法，并且具有最少的参数和计算量。