Papers › BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline
BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline
Ruihao Wang, Jian Qin, Kaiying Li, Yaochen Li, Dong Cao, Jintao Xu
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Lane Detection | Apollo Synthetic 3D Lane | BEV-LaneDet | F1 | 96.9 | #2 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | BEV-LaneDet | X error far | 0.242 | #2 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | BEV-LaneDet | X error near | 0.016 | #2 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | BEV-LaneDet | Z error far | 0.216 | #2 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | BEV-LaneDet | Z error near | 0.02 | #2 of 10 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | Curve | 63.1 | #10 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | Extreme Weather | 53.4 | #10 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | F1 (all) | 58.4 | #10 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | FPS (pytorch) | 102 | #10 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | Intersection | 50.3 | #10 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | Merge & Split | 53.7 | #10 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | Night | 53.4 | #10 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | BEV-LaneDet | Up & Down | 48.7 | #10 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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