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All Projects → Nicholasli1995 → EgoNet

Nicholasli1995 / EgoNet

Licence: MIT License
Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation"

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Labels

deep-learningrepresentation-learningvehiclesautonomous-drivingpose-estimation3d-visionkittiprojective-geometry3d-pose-estimationself-supervised-learning3d-object-detectionorientation-estimationkitti-3drobot-perceptioncvpr2021

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PWC

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:

(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

Run a demo with a one-line command!

Check instructions here.

Performance: APBEV@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

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