Awesome

Match-Stereo-Videos via Bidirectional Alignment

Imperial College London

Junpeng Jing, Ye Mao, Anlan Qiu, Krystian Mikolajczyk

[Paper] [Project]

What's new?

An update of the previous project BiDAStereo, for stereo videos:

☑ Support BiDAStabilizer for image-based network, RAFTStereo and IGEVStereo, for video stereo matching.

☑ Support added video datasets: KITTI Depth, Infinigen SV, Southkensington SV.

☑ Support new metrics and losses.

☑ Support demo code for arbitrary stereo video as input.

Installation

Installation with PyTorch3D, PyTorch 1.12.1 & cuda 11.3

Setup the root for all source files:

git clone https://github.com/tomtomtommi/bidavideo
cd bidavideo
export PYTHONPATH=`(cd ../ && pwd)`:`pwd`:$PYTHONPATH

Create a conda env:

conda create -n bidavideo python=3.8
conda activate bidavideo

Install requirements

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

Demo

Demo code can be run using the following script:

sh demo.sh

In this script, you can specify which model (RAFTStereo, IGEVStereo with/without BiDAStabilizer; BiDAStereo) you want to use for demo.

Before running, download the checkpoints on google drive. Copy the checkpoints to ./checkpoints/

To test on your own data, modify --path ./demo_video/. More arguments can be found and modified in demo.py

Dataset

Download the following datasets and put in ./data/datasets:

• SceneFlow
• Sintel
• Dynamic_Replica
• KITTI Depth
• Infinigen SV
• SouthKensington SV

The folder stucture of the datasets should look like:

/data/datasets/
            ├── Driving
            ├── FlyingThings3D
            ├── Monkaa
            ├── sintel_stereo
                ├── training
                    ├── camdata_left
                    ├── disparities
                    ├── ...
                    ├── clean_left
                    ├── clean_right
            ├── dynamic_replica_data
                ├── train
                ├── test
                ├── real
            ├── InfinigenStereo
                ├── train
                ├── test
                ├── val
            ├── SouthKensington
                ├── Indoor
                    ├── video001
                    ├── ...
                ├── Outdoor
                    ├── video001
                    ├── ...
            ├── kitti_depth
                ├── gt_depth
                    ├── 2011_09_26_drive_0001_sync
                    ├── 2011_09_26_drive_0002_sync
                    ├── ...
                    ├── 2011_10_03_drive_0047_sync
                ├── input
                │   ├── 2011_09_26
                │   ├── 2011_09_28
                │   ├── ...
                │   ├── 2011_10_03

Evaluation

Download the checkpoints on google drive. Copy the checkpoints to ./checkpoints/.

To evaluate BiDAStabilizer:

sh evaluate_bidastabilizer.sh

To evaluate BiDAStereo:

sh evaluate_bidastereo.sh

The results are evaluated on an A6000 48GB GPU.

Training

To train BiDAStabilizer:

sh train_bidastabilizer.sh

--restore_ckpt is the fixed weights of image-based models. --ckpt_path is the logging path.

To train BiDAStereo:

sh train_bidastereo.sh

You can decrease image_size and / or sample_len if you don't have enough GPU memory.

License

The code is under MIT license.

SouthKensington SV dataset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Under this license, if you want to modify SK-SV or generate new data from SK-SV dataset (e.g., super-resolution, denoising, defocus), the releasing of your new data should be licensed under the same CC BY-NC-SA 4.0.

Citation

If you use BiDAStabilizer or BiDAStereo in your research, please use the following BibTeX entry.

@article{jing2024match,
  title={Match Stereo Videos via Bidirectional Alignment},
  author={Junpeng Jing and Ye Mao and Anlan Qiu and Krystian Mikolajczyk},
  year={2024}
}

@article{jing2024match-stereo-videos,
  title={Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching},
  author={Junpeng Jing and Ye Mao and Krystian Mikolajczyk},
  year={2024}
}