LATR: A Python repository from palfu

LATR: 3D Lane Detection from Monocular Images with Transformer

This is the official PyTorch implementation of LATR: 3D Lane Detection from Monocular Images with Transformer.

News

2024-01-15 🎊 Our new work DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation is accepted by ICLR2024.
2023-08-12 🎉 LATR is accepted as an Oral presentation at ICCV2023! ✨

Environments

To set up the required packages, please refer to the installation guide.

Data

Please follow data preparation to download dataset.

Pretrained Models

Note that the performance of pretrained model is higher than our paper due to code refactoration and optimization. All models are uploaded to google drive.

Dataset	Pretrained	Metrics	md5
OpenLane-1000	Google Drive	F1=0.6297	d8ecb900c34fd23a9e7af840aff00843
OpenLane-1000 (Lite version)	Google Drive	F1=0.6212	918de41d0d31dbfbecff3001c49dc296
ONCE	Google Drive	F1=0.8125	65a6958c162e3c7be0960bceb3f54650
Apollo-balance	Google Drive	F1=0.9697	551967e8654a8a522bdb0756d74dd1a2
Apollo-rare	Google Drive	F1=0.9641	184cfff1d3097a9009011f79f4594138
Apollo-visual	Google Drive	F1=0.9611	cec4aa567c264c84808f3c32f5aace82

Evaluation

You can download the pretrained models to ./pretrained_models directory and refer to the eval guide for evaluation.

Train

Please follow the steps in training to train the model.

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 for your research, please consider citing the paper:

@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}
}

palfu/LATR