Papers › CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention
CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention
Yifeng Bai, Zhirong Chen, Zhangjie Fu, Lang Peng, Pengpeng Liang, Erkang Cheng
3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well.
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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 | CurveFormer | F1 | 95.8 | #3 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | CurveFormer | X error far | 0.326 | #3 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | CurveFormer | X error near | 0.078 | #3 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | CurveFormer | Z error far | 0.219 | #3 of 10 | Archive leaderboard | report |
| 3D Lane Detection | Apollo Synthetic 3D Lane | CurveFormer | Z error near | 0.018 | #3 of 10 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | Curve | 56.6 | #19 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | Extreme Weather | 49.7 | #19 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | F1 (all) | 50.5 | #19 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | FPS (pytorch) | - | #19 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | Intersection | 42.9 | #19 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | Merge & Split | 45.4 | #19 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | Night | 49.1 | #19 of 21 | Archive leaderboard | report |
| 3D Lane Detection | OpenLane | CurveFormer | Up & Down | 45.2 | #19 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.
Methods
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