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Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection
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Identifies truncation flaws in sparse-point lane representations for monocular 3D lane detection, proposing an endpoint patching strategy with an EP-head and PL-attention that injects lane geometric priors into attention, improving Persformer, Anchor3DLane and LATR on OpenLane.
Problem
Sparse-point 3D lane detectors truncate original ground truth at endpoints during training-GT generation, so the lost extent (up to 10-20 m) cannot be predicted, and existing models underuse geometric structure of lanes in model design.
Approach
Patch lane endpoints back to the original ground truth by having an EP-head predict per-preset-point distances to the start and end points, and add a PL-attention module that factorizes self-attention into point-point, lane-lane and point-y attentions to encode lane geometric priors.
Key results
- Patching raises F1 of training ground truth on OpenLane from 78.9% to 98.5% with 20 preset points.
- Applying the method improves overall F1 by 4.4 for Persformer (50.5 to 54.9), 3.2 for Anchor3DLane (53.1 to 56.3), and 2.8 for LATR (61.9 to 64.7) on OpenLane.
- On LATR with 10 preset points, EP-head and PL-attention raise F1 from 40.6 to 54.8.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add EP-head and PL-attention | Persformer · OpenLane val | F1: 50.5 -> 54.9 | Helped |
| Add EP-head and PL-attention | Anchor3DLane · OpenLane val | F1: 53.1 -> 56.3 | Helped |
| Add EP-head and PL-attention | LATR · OpenLane val | F1: 61.9 -> 64.7 | Helped |
| Add EP-head (ablation) | LATR · OpenLane, Lane-IoU=0.75 | F1: 61.9 -> 63.1 | Helped |
| Add EP-head (ablation) | LATR · OpenLane, Lane-IoU=0.90 | F1: 43.6 -> 47.1 | Helped |
| Add EP-head (ablation) | LATR · OpenLane short lanes (0-40m), Lane-IoU=0.75 | Recall: 55.4 -> 58.6 | Helped |
| Add PL-attention to LATR (ablation) | LATR · OpenLane, Lane-IoU=0.75 | F1: 63.1 -> 64.7 | Helped |
| Add PL-attention to LATR (ablation) | LATR · OpenLane short lanes (0-40m), Lane-IoU=0.75 | F1: 44.2 -> 45.4 | Helped |
| Use point-point attention only (ablation) | LATR · OpenLane | F1: 61.9 -> 55.3 | Hurt |
| Use lane-lane attention only (ablation) | LATR · OpenLane | F1: 61.9 -> 57.2 | Hurt |
| Use point-y attention only (ablation) | LATR · OpenLane | F1: 61.9 -> 61.5 | No clear effect |
| Use all three PL-attention components (ablation) | LATR · OpenLane | F1: 61.9 -> 64.7 | Helped |
Surprising findings
- Truncating lane endpoints in common training-GT generation can cause errors of up to 20 m, a flaw the authors say had not been explored before.
- Fewer preset points degrade F1 sharply for existing methods, but the EP-head mitigates this, enabling fewer points.
Limitations
- Patching only handles cases with two or more valid preset points; F1 drops sharply with only 5 preset points (single valid point cases not handled).
- Evaluated mainly on OpenLane with the LATR framework for ablations; code promised 'soon' rather than released.
- Reported gains on some scenarios are small (e.g., LATR up & down +0.1).
Experimental setup
Datasets: OpenLane ApolloSim
Benchmarks: OpenLane ApolloSim
Metrics: F1-score precision recall x error (near/far) z error (near/far) accuracy Lane-IoU
Baselines: Persformer Anchor3DLane LATR 3DLaneNet GenLaneNet CurveFormer BEV-LaneDet GroupLane PVALane
Builds on: LATR Anchor3DLane Persformer
Closest papers by meaning
- LaneCPP: Continuous 3D Lane Detection using Physical Priors (CVPR 2024 · similarity 0.91)
- PVALane: Prior-Guided 3D Lane Detection with View-Agnostic Feature Alignment (AAAI 2024 · similarity 0.91)
- GLane3D: Detecting Lanes with Graph of 3D Keypoints (CVPR 2025 · similarity 0.90)
- DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation (ICLR 2024 · similarity 0.90)
- LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving (ICLR 2024 · similarity 0.90)
- Sketch and Refine: Towards Fast and Accurate Lane Detection (AAAI 2024 · similarity 0.89)
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