  In this paper, we propose an advanced approach in targeting the problem of
monocular 3D lane detection by leveraging geometry structure underneath the
process of 2D to 3D lane reconstruction. Inspired by previous methods, we first
analyze the geometry heuristic between the 3D lane and its 2D representation on
the ground and propose to impose explicit supervision based on the structure
prior, which makes it achievable to build inter-lane and intra-lane
relationships to facilitate the reconstruction of 3D lanes from local to
global. Second, to reduce the structure loss in 2D lane representation, we
directly extract BEV lane information from front view images, which
tremendously eases the confusion of distant lane features in previous methods.
Furthermore, we propose a novel task-specific data augmentation method by
synthesizing new training data for both segmentation and reconstruction tasks
in our pipeline, to counter the imbalanced data distribution of camera pose and
ground slope to improve generalization on unseen data. Our work marks the first
attempt to employ the geometry prior information into DNN-based 3D lane
detection and makes it achievable for detecting lanes in an extra-long
distance, doubling the original detection range. The proposed method can be
smoothly adopted by other frameworks without extra costs. Experimental results
show that our work outperforms state-of-the-art approaches by 3.8% F-Score on
Apollo 3D synthetic dataset at real-time speed of 82 FPS without introducing
extra parameters.
