[ICCV 2023] MPI-Flow: Learning Realistic Optical Flow with Multiplane Images

This repository contains the modified online training source code for our paper:

Paper | Checkpoints | Project Page | My Home Page

Code repository is under construction... 🏗️ 🚧 🔨

Update

• 2023.12.24: Update online trained checkpoints.
• 2023.12.18: Code for online training released (It's still being examined).
• 2023.09.13: Code released.

Performance

More training steps are expected to get better performance.

From left to right:

1) original image; 2) generated image; 3) generated optical flow; 4) predicted by RAFT (C+T)

Generated training examples (after augmentation):

Requirements

See also https://github.com/Sharpiless/MPI-Flow

Required Data

To train RAFT with MPI-Flow, you will only need to create a single folder contains:

├── datasets
    ├── custom
        ├── images
        ├── disps
        ├── masks

The "images" folder contains your custom single-view images (for example, COCO dataset).

The "disps" folder contains depths predicted by MiDaS.

The "masks" folder contains masks predicted by Mask2Former.

To evaluate RAFT, you will need to download the required datasets.

• FlyingChairs
• FlyingThings3D
• Sintel
• KITTI
• HD1K (optional)

By default datasets.py will search for the datasets in these locations. You can create symbolic links to wherever the datasets were downloaded in the datasets folder

├── datasets
    ├── Sintel
        ├── test
        ├── training
    ├── KITTI
        ├── testing
        ├── training
        ├── devkit
    ├── FlyingChairs_release
        ├── data
    ├── FlyingThings3D
        ├── frames_cleanpass
        ├── frames_finalpass
        ├── optical_flow

Evaluation

You can evaluate a trained model using evaluate.py

python evaluate.py --model=models/raft-things.pth --dataset=sintel --mixed_precision

Training

We used the following training schedule in our paper. Training logs will be written to the runs which can be visualized using tensorboard

./train.sh

Checkpoints

Checkpoints to reproduce our results in Table 1 can be downloaded in Google Drive.

You can use the code in RAFT to evaluate/train the models.

Contact

If you have any questions, please contact Yingping Liang (liangyingping@bit.edu.cn).

License and Citation

This repository can only be used for personal/research/non-commercial purposes. Please cite the following paper if this model helps your research:

@inproceedings{liang2023mpi,
    author = {Liang, Yingping and Liu, Jiaming and Zhang, Debing and Ying, Fu},
    title = {MPI-Flow: Learning Realistic Optical Flow with Multiplane Images},
    booktitle = {In the IEEE International Conference on Computer Vision (ICCV)},
    year={2023}
}