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README
LATR: 3D Lane Detection from Monocular Images with Transformer
<img src="https://img.shields.io/badge/arXiv-2308.04583-b31b1b.svg">
This is the official PyTorch implementation of LATR: 3D Lane Detection from Monocular Images with Transformer.
News
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2024-01-15 :confetti_ball: Our new work DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation is accepted by ICLR2024.
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2023-08-12 :tada: LATR is accepted as an Oral presentation at ICCV2023! :sparkles:
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}
}