IGEV-Stereo & IGEV-MVS

This repository contains the source code for our paper:

Iterative Geometry Encoding Volume for Stereo Matching
CVPR 2023
Gangwei Xu, Xianqi Wang, Xiaohuan Ding, Xin Yang

Demos

Pretrained models can be downloaded from google drive

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

python demo.py --restore_ckpt ./pretrained_models/sceneflow/sceneflow.pth

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.

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

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

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

Training

To train on Scene Flow, run

python train_stereo.py

To train on KITTI, run

python train_stereo.py --restore_ckpt ./pretrained_models/sceneflow/sceneflow.pth --dataset 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},
  year={2023}
}

Acknowledgements

This project is heavily based on RAFT-Stereo, We thank the original authors for their excellent work.

wioponsen/IGEV