Junpeng Jing, Ye Mao, Krystian Mikolajczyk
The extension of this work is [BiDAVideo]
Download the following datasets and put in ./data/datasets:
Download the following dataset and link to the project ln -s ./dynamic_replica ./bidastereo/:
Installation of BiDAStereo with PyTorch3D, PyTorch 1.12.1 & cuda 11.3
git clone https://github.com/TomTomTommi/BiDAStereo
cd bidastereo
export PYTHONPATH=`(cd ../ && pwd)`:`pwd`:$PYTHONPATH
conda create -n bidastereo python=3.8
conda activate bidastereo
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
pip install -r requirements.txt
To download the checkpoints, click the links below. Copy the checkpoints to ./bidastereo/checkpoints/.
- BiDAStereo trained on SceneFlow
- BiDAStereo trained on SceneFlow and Dynamic Replica
To evaluate BiDAStereo:
sh evaluate_bidastereo.sh
sh evaluate_real.sh
The results are evaluated on an A6000 48GB GPU.
Evaluation on Dynamic Replica requires a 32GB GPU. If you don't have enough GPU memory, you can modify kernel_size from 20 to 10.
Training requires 8 V100 32GB GPUs or 4 A100 80GB GPUs. You can decrease image_size and / or sample_len if you don't have enough GPU memory.
sh train_bidastereo.sh
If you use BiDAStereo in your research, please use the following BibTeX entry.
@inproceedings{jing2024match,
title={Match-stereo-videos: Bidirectional alignment for consistent dynamic stereo matching},
author={Jing, Junpeng and Mao, Ye and Mikolajczyk, Krystian},
booktitle={European Conference on Computer Vision},
pages={415--432},
year={2024},
organization={Springer}
}
In this project, we use parts of public codes and thank the authors for their contribution in: