Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation". This repo includes an implementation that performs vehicle orientation estimation on the KITTI dataset from a single RGB image.
News:
(2022-??-??): v-1.1 will be released which include pre-trained models for other object classes (Pedestrian and Cyclist in KITTI).
(2021-08-16): v-1.0 is released. The training documentation is added.
(2021-06-21): v-0.9 (beta version) is released. The inference utility is here! For Q&A, go to discussions. If you believe there is a technical problem, submit to issues.
(2021-06-16): This repo is under final code cleaning and documentation preparation. Stay tuned and come back in a week!
Check our 5-min video (Youtube, 爱奇艺) for an introduction.
中文详解：哔哩哔哩
Check instructions here.
The validation results in the paper was based on R11, the results using R40 are attached here.
| Method | Reference | Easy | Moderate | Hard |
|---|---|---|---|---|
| M3D-RPN | ICCV 2019 | 20.85 | 15.62 | 11.88 |
| MonoDIS | ICCV 2019 | 18.45 | 12.58 | 10.66 |
| MonoPair | CVPR 2020 | 24.12 | 18.17 | 15.76 |
| D4LCN | CVPR 2020 | 31.53 | 22.58 | 17.87 |
| Kinematic3D | ECCV 2020 | 27.83 | 19.72 | 15.10 |
| GrooMeD-NMS | CVPR 2021 | 27.38 | 19.75 | 15.92 |
| MonoDLE | CVPR 2021 | 24.97 | 19.33 | 17.01 |
| Ours (@R11) | CVPR 2021 | 33.60 | 25.38 | 22.80 |
| Ours (@R40) | CVPR 2021 | 34.31 | 24.80 | 20.16 |
| Method | Reference | Configuration | Easy | Moderate | Hard |
|---|---|---|---|---|---|
| M3D-RPN | ICCV 2019 | Monocular | 88.38 | 82.81 | 67.08 |
| DSGN | CVPR 2020 | Stereo | 95.42 | 86.03 | 78.27 |
| Disp-RCNN | CVPR 2020 | Stereo | 93.02 | 81.70 | 67.16 |
| MonoPair | CVPR 2020 | Monocular | 91.65 | 86.11 | 76.45 |
| D4LCN | CVPR 2020 | Monocular | 90.01 | 82.08 | 63.98 |
| Kinematic3D | ECCV 2020 | Monocular | 58.33 | 45.50 | 34.81 |
| MonoDLE | CVPR 2021 | Monocular | 93.46 | 90.23 | 80.11 |
| Ours | CVPR 2021 | Monocular | 96.11 | 91.23 | 80.96 |
Check instructions here to reproduce the above quantitative results.
Check instructions here to train Ego-Net and learn how to prepare your own training dataset other than KITTI.
Please star this repository and cite the following paper in your publications if it helps your research:
@InProceedings{Li_2021_CVPR,
author = {Li, Shichao and Yan, Zengqiang and Li, Hongyang and Cheng, Kwang-Ting},
title = {Exploring intermediate representation for monocular vehicle pose estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {1873-1883}
}
This repository can be used freely for non-commercial purposes. Contact me if you are interested in a commercial license.
Link to the paper: Exploring intermediate representation for monocular vehicle pose estimation
Link to the presentation video: Youtube, 爱奇艺
Relevant ECCV 2020 work: GSNet