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DKT-Stereo: Robust Synthetic-to-Real Transfer for Stereo Matching

This is the official repository for our CVPR 2024 paper: Robust Synthetic-to-Real Transfer for Stereo Matching.

paper: [arxiv]

Introduction

We aim to fine-tune stereo networks without compromising robustness to unseen domains. We identify that learning new knowledge without sufficient regularization and overfitting GT details can degrade the robustness. We propose the DKT framework, which improves fine-tuning by dynamically measuring what has been learned.

TODO

• Release Training Code.
• Release Checkpoint.

Demos

Fine-tuned checkpoints of DKT-Stereo can be downloaded from google drive

The sceneflow pre-trained checkpoints can be obtained from IGEV and RAFT-Stereo.

Environment

• NVIDIA RTX 3090

• Python 3.8

• pytorch 1.12

  Create a virtual environment and activate it.

conda create -n DKT_Stereo python=3.8
conda activate DKT_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 DKT-Stereo, you will need to download the required datasets.

• KITTI
• Booster
• Middlebury
• ETH3D

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

├── /data
    ├── KITTI
        ├── KITTI_2012
            ├── training
            ├── testing
        ├── KITTI_2015
            ├── training
            ├── testing
    ├── Booster_dataset
        ├── full
        ├── half
        ├── quarter
            ├── train
    ├── Middlebury
        ├── MiddEval3
            ├── trainingF
            ├── trainingH
            ├── trainingQ
    ├── ETH3D
        ├── two_view_training
        ├── two_view_training_gt

Evaluation

python tools/evaluate_stereo.py --config configs/raft_stereo/base.json --restore_ckpt ckpt/dkt-raft/booster_ft.pth --logdir output/eval/dkt-raft

python tools/evaluate_stereo.py --config configs/igev_stereo/base.json --restore_ckpt ckpt/dkt-igev/kitti_ft.pth --logdir output/eval/dkt-igev

Training

Booster fine-tuning. This current fine-tuning code on booster is different from the implementation for online submission checkpoints, which use the cascade training strategy as PCVNet.

bash run_scripts/raft-stereo/ft_booster.sh gpus(0,1) output_dir(/output/raftstereo/booster_ft)

KITTI fine-tuning.

bash run_scripts/igev/ft_kitti.sh gpus(0,1,2,3) output_dir(/output/igevstereo/kitti_ft)

Citation

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

@inproceedings{zhang2024robust,
  title={Robust Synthetic-to-Real Transfer for Stereo Matching},
  author={Zhang, Jiawei and Li, Jiahe and Huang, Lei and Yu, Xiaohan and Gu, Lin and Zheng, Jin and Bai, Xiao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20247--20257},
  year={2024}
}

Acknowledgements

This project is based on IGEV and RAFT-Stereo. Thanks for these great projects!