Reconstruct From Top View: A 3D Lane Detection Approach Based on Geometry Structure Prior

Jia Shi, Chenguang Li, Ya Wang, Guangliang Cheng

Abstract:

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 top view 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 sp