  We present a generalized and scalable method, called Gen-LaneNet, to detect
3D lanes from a single image. The method, inspired by the latest
state-of-the-art 3D-LaneNet, is a unified framework solving image encoding,
spatial transform of features and 3D lane prediction in a single network.
However, we propose unique designs for Gen-LaneNet in two folds. First, we
introduce a new geometry-guided lane anchor representation in a new coordinate
frame and apply a specific geometric transformation to directly calculate real
3D lane points from the network output. We demonstrate that aligning the lane
points with the underlying top-view features in the new coordinate frame is
critical towards a generalized method in handling unfamiliar scenes. Second, we
present a scalable two-stage framework that decouples the learning of image
segmentation subnetwork and geometry encoding subnetwork. Compared to
3D-LaneNet, the proposed Gen-LaneNet drastically reduces the amount of 3D lane
labels required to achieve a robust solution in real-world application.
Moreover, we release a new synthetic dataset and its construction strategy to
encourage the development and evaluation of 3D lane detection methods. In
experiments, we conduct extensive ablation study to substantiate the proposed
Gen-LaneNet significantly outperforms 3D-LaneNet in average precision(AP) and
F-score.
