  Compared to 2D lanes, real 3D lane data is difficult to collect accurately.
In this paper, we propose a novel method for training 3D lanes with only 2D
lane labels, called weakly supervised 3D lane detection WS-3D-Lane. By
assumptions of constant lane width and equal height on adjacent lanes, we
indirectly supervise 3D lane heights in the training. To overcome the problem
of the dynamic change of the camera pitch during data collection, a camera
pitch self-calibration method is proposed. In anchor representation, we propose
a double-layer anchor with a improved non-maximum suppression (NMS) method,
which enables the anchor-based method to predict two lane lines that are close.
Experiments are conducted on the base of 3D-LaneNet under two supervision
methods. Under weakly supervised setting, our WS-3D-Lane outperforms previous
3D-LaneNet: F-score rises to 92.3% on Apollo 3D synthetic dataset, and F1 rises
to 74.5% on ONCE-3DLanes. Meanwhile, WS-3D-Lane in purely supervised setting
makes more increments and outperforms state-of-the-art. To the best of our
knowledge, WS-3D-Lane is the first try of 3D lane detection under weakly
supervised setting.
