[ICCV2023 Oral] LATR: 3D Lane Detection from Monocular Images with Transformer

Official PyTorch implementation of LATR: 3D Lane Detection from Monocular Images with Transformer

Code will be released.

Benchmark

OpenLane

Models	F1	Accuracy	X error near \| far	Z-error near \| far
3DLaneNet	44.1	-	0.479 \| 0.572	0.367 \| 0.443
GenLaneNet	32.3	-	0.593 \| 0.494	0.140 \| 0.195
Cond-IPM	36.3	-	0.563 \| 1.080	0.421 \| 0.892
PersFormer	50.5	89.5	0.319 \| 0.325	0.112 \| 0.141
CurveFormer	50.5	-	0.340 \| 0.772	0.207 \| 0.651
PersFormer-Res50	53.0	89.2	0.321 \| 0.303	0.085 \| 0.118
LATR-Lite	61.5	91.9	0.225 \| 0.249	0.073 \| 0.106
LATR	61.9	92.0	0.219 \| 0.259	0.075 \| 0.104

Apollo

Plaes kindly refer to our paper for the performance on other scenes.

Scene	Models	F1	AP	X error near \| far	Z error near \| far
Balanced Scene	3DLaneNet	86.4	89.3	0.068 \| 0.477	0.015 \| 0.202
	GenLaneNet	88.1	90.1	0.061 \| 0.496	0.012 \| 0.214
	CLGo	91.9	94.2	0.061 \| 0.361	0.029 \| 0.250
	PersFormer	92.9	-	0.054 \| 0.356	0.010 \| 0.234
	GP	91.9	93.8	0.049 \| 0.387	0.008 \| 0.213
	CurveFormer	95.8	97.3	0.078 \| 0.326	0.018 \| 0.219
	LATR-Lite	96.5	97.8	0.035 \| 0.283	0.012 \| 0.209
	LATR	96.8	97.9	0.022 \| 0.253	0.007 \| 0.202

ONCE

Method	F1	Precision(%)	Recall(%)	CD error(m)
3DLaneNet	44.73	61.46	35.16	0.127
GenLaneNet	45.59	63.95	35.42	0.121
SALAD	64.07	75.90	55.42	0.098
PersFormer	72.07	77.82	67.11	0.086
LATR	80.59	86.12	75.73	0.052

Acknowledgment

This library is inspired by OpenLane, GenLaneNet, mmdetection3d, SparseInst, ONCE and many other related works, we thank them for sharing the code and datasets.

Citation

If you find LATR is useful, please cite:

@article{luo2023latr,
  title={LATR: 3D Lane Detection from Monocular Images with Transformer},
  author={Luo, Yueru and Zheng, Chaoda and Yan, Xu and Kun, Tang and Zheng, Chao and Cui, Shuguang and Li, Zhen},
  journal={arXiv preprint arXiv:2308.04583},
  year={2023}
}