EgoNet

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:

(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

Run a demo with a one-line command!

Check instructions here.

Performance: AP^BEV@R₄₀ on KITTI val set for Car (monocular RGB)

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

Performance: AOS@R₄₀ on KITTI test set for Car (RGB)

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

Inference/Deployment

Check instructions here to reproduce the above quantitative results.

Training

Check instructions here to train Ego-Net and learn how to prepare your own training dataset other than KITTI.

Citation

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}
}

License

This repository can be used freely for non-commercial purposes. Contact me if you are interested in a commercial license.

Links

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

ruhyadi/EgoNet