IGEV++

Our significant extension version of IGEV, named IGEV++, is available at Paper, Code

IGEV-Stereo (CVPR 2023)

This repository contains the source code for our paper:

Iterative Geometry Encoding Volume for Stereo Matching
Gangwei Xu, Xianqi Wang, Xiaohuan Ding, Xin Yang

Demos

Pretrained models can be downloaded from google drive

We assume the downloaded pretrained weights are located under the pretrained_models directory.

You can demo a trained model on pairs of images. To predict stereo for Middlebury, run

python demo_imgs.py \
--restore_ckpt pretrained_models/sceneflow/sceneflow.pth \
-l=path/to/your/left_imgs \
-r=path/to/your/right_imgs

or you can demo a trained model pairs of images for a video, run:

python demo_video.py \
--restore_ckpt pretrained_models/sceneflow/sceneflow.pth \
-l=path/to/your/left_imgs \
-r=path/to/your/right_imgs

To save the disparity values as .npy files, run any of the demos with the --save_numpy flag.

Comparison with RAFT-Stereo

Method	KITTI 2012 (3-noc)	KITTI 2015 (D1-all)	Memory (G)	Runtime (s)
RAFT-Stereo	1.30 %	1.82 %	1.02	0.38
IGEV-Stereo	1.12 %	1.59 %	0.66	0.18

Environment

• NVIDIA RTX 3090
• Python 3.8
• Pytorch 1.12

Create a virtual environment and activate it.

conda create -n IGEV_Stereo python=3.8
conda activate IGEV_Stereo

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

Required Data

To evaluate/train IGEV-Stereo, you will need to download the required datasets.

• Scene Flow
• KITTI
• Middlebury
• ETH3D

By default stereo_datasets.py will search for the datasets in these locations.

├── /data
    ├── sceneflow
        ├── frames_finalpass
        ├── disparity
    ├── KITTI
        ├── KITTI_2012
            ├── training
            ├── testing
            ├── vkitti
        ├── KITTI_2015
            ├── training
            ├── testing
            ├── vkitti
    ├── Middlebury
        ├── trainingH
        ├── trainingH_GT
    ├── ETH3D
        ├── two_view_training
        ├── two_view_training_gt
    ├── DTU_data
        ├── dtu_train
        ├── dtu_test

Evaluation

To evaluate on Scene Flow or Middlebury or ETH3D, run

python evaluate_stereo.py --restore_ckpt ./pretrained_models/sceneflow/sceneflow.pth --dataset sceneflow

or

python evaluate_stereo.py --restore_ckpt ./pretrained_models/sceneflow/sceneflow.pth --dataset middlebury_H

or

python evaluate_stereo.py --restore_ckpt ./pretrained_models/sceneflow/sceneflow.pth --dataset eth3d

Training

To train on Scene Flow, run

python train_stereo.py --logdir ./checkpoints/sceneflow

To train on KITTI, run

python train_stereo.py --logdir ./checkpoints/kitti --restore_ckpt ./pretrained_models/sceneflow/sceneflow.pth --train_datasets kitti

Submission

For submission to the KITTI benchmark, run

python save_disp.py

MVS training and evaluation

To train on DTU, run

python train_mvs.py

To evaluate on DTU, run

python evaluate_mvs.py

Citation

If you find our work useful in your research, please consider citing our paper:

@inproceedings{xu2023iterative,
  title={Iterative Geometry Encoding Volume for Stereo Matching},
  author={Xu, Gangwei and Wang, Xianqi and Ding, Xiaohuan and Yang, Xin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21919--21928},
  year={2023}
}

@article{xu2024igev++,
  title={IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching},
  author={Xu, Gangwei and Wang, Xianqi and Zhang, Zhaoxing and Cheng, Junda and Liao, Chunyuan and Yang, Xin},
  journal={arXiv preprint arXiv:2409.00638},
  year={2024}
}

Acknowledgements

This project is based on RAFT-Stereo, and CoEx. We thank the original authors for their excellent works.