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A novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization ability.
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| 2026-08-06 | 288 |
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<p align="center">
<h1 align="center">CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and
Geometry Interaction</h1>
<p align="center">
Gangwei Xu*, Huan Zhou*, Xin Yang
</p>
<h3 align="center"><a href="https://arxiv.org/pdf/2301.02789.pdf">Paper</a>
<div align="center"></div>
</p>
<p align="center">
<a href="">
<img src="https://github.com/gangweiX/CGI-Stereo/blob/main/imgs/CGI-network.jpg" alt="Logo" width="90%">
</a>
</p>
# SOTA results.
The proposed CGF-ACV ranks the first on the KITTI 2012 and 2015 leaderboards among all the published methods. The proposed CGI-Stereo outperforms all other published real-time methods on KITTI benchmarks.
<p align="center"><img width=90% src="imgs/ranking.jpg"></p>
Our proposed CGF can be easily embedded into many existing stereo matching networks, such as PSMNet, GwcNet and ACVNet. The resulting networks are improved in accuracy by a large margin.
| Method | KITTI 2012 <br> (3-noc) | KITTI 2012 <br> (3-all) | KITTI 2015 <br> (D1-bg) | KITTI 2015 <br> (D1-fg) | KITTI 2015 <br> (D1-all) |
|:-:|:-:|:-:|:-:|:-:|:-:|
| PSMNet | 1.49 % | 1.89 % | 1.86 % | 4.62 % | 2.32 % |
| CGF-PSM | 1.21 % | 1.57 % | 1.46 % | 3.47 % | 1.80 % |
| GwcNet | 1.32 % | 1.70 % | 1.74 % | 3.93 % | 2.11 % |
| CGF-Gwc | 1.17 % | 1.52 % | 1.38 % | 3.34 % | 1.71 % |
| ACVNet | 1.13 % | 1.47 % | 1.37 % | 3.07 % | 1.65 % |
| CGF-ACV | 1.03 % | 1.34 % | 1.31 % | 3.08 % | 1.61 % |
# How to use
## Environment
* NVIDIA RTX 3090
* Python 3.8
* Pytorch 1.12
## Install
### Create a virtual environment and activate it.
```
conda create -n CGI python=3.8
conda activate CGI
```
### Dependencies
```
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch -c nvidia
pip install opencv-python
pip install scikit-image
pip install tensorboard
pip install matplotlib
pip install tqdm
pip install timm==0.5.4
```
## Data Preparation
* [Scene Flow Datasets](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
* [KITTI 2012](http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stereo)
* [KITTI 2015](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo)
* [Middlebury](https://vision.middlebury.edu/stereo/submit3/)
## Train
Use the following command to train CGI-Stereo on Scene Flow.
First training,
```
python train_sceneflow.py --logdir ./checkpoints/sceneflow/first/
```
Second training,
```
python train_sceneflow.py --logdir ./checkpoints/sceneflow/second/ --loadckpt ./checkpoints/sceneflow/first/checkpoint_000019.ckpt
```
Use the following command to train CGI-Stereo on KITTI (using pretrained model on Scene Flow),
```
python train_kitti.py --logdir ./checkpoints/kitti/ --loadckpt ./checkpoints/sceneflow/second/checkpoint_000019.ckpt
```
## Evaluation on Scene Flow and KITTI
### Pretrained Model
* [CGI-Stereo](https://drive.google.com/drive/folders/15pVddbGU6ByYWRWB_CFW2pzANU0mzdU5?usp=share_link)
* [CGF-ACV](https://drive.google.com/drive/folders/1sSZctBVYQzCpG_OPFTPIDonDRkWwca3t?usp=share_link)
Generate disparity images of KITTI test set,
```
python save_disp.py
```
# Citation
If you find this project helpful in your research, welcome to cite the paper.
```
@article{xu2023cgi,
title={CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction},
author={Xu, Gangwei and Zhou, Huan and Yang, Xin},
journal={arXiv preprint arXiv:2301.02789},
year={2023}
}
```
# Acknowledgements
Thanks to Antyanta Bangunharcana for opening source of his excellent work [Correlate-and-Excite](https://github.com/antabangun/coex).
Excerpt of 3,637 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:85a74a4ee2577bfd, llm:Repository description: 'A novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization ability.' Language: Python. Project name CGI-Stereo suggests stereo depth estimation (computer vision).
matched fp:85a74a4ee2577bfd, llm:Repository description: 'A novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization ability.' Language: Python. Project name CGI-Stereo suggests stereo depth estimation (computer vision).