  3D lane detection which plays a crucial role in vehicle routing, has recently
been a rapidly developing topic in autonomous driving. Previous works struggle
with practicality due to their complicated spatial transformations and
inflexible representations of 3D lanes. Faced with the issues, our work
proposes an efficient and robust monocular 3D lane detection called BEV-LaneDet
with three main contributions. First, we introduce the Virtual Camera that
unifies the in/extrinsic parameters of cameras mounted on different vehicles to
guarantee the consistency of the spatial relationship among cameras. It can
effectively promote the learning procedure due to the unified visual space. We
secondly propose a simple but efficient 3D lane representation called
Key-Points Representation. This module is more suitable to represent the
complicated and diverse 3D lane structures. At last, we present a light-weight
and chip-friendly spatial transformation module named Spatial Transformation
Pyramid to transform multiscale front-view features into BEV features.
Experimental results demonstrate that our work outperforms the state-of-the-art
approaches in terms of F-Score, being 10.6% higher on the OpenLane dataset and
5.9% higher on the Apollo 3D synthetic dataset, with a speed of 185 FPS. The
source code will released at https://github.com/gigo-team/bev_lane_det.
