Learning Optical Flow from a Few Matches

This repository contains the source code for our paper:

Learning Optical Flow from a Few Matches
CVPR 2021
Shihao Jiang, Yao Lu, Hongdong Li, Richard Hartley
ANU

Requirements

The code has been tested with PyTorch 1.6 and Cuda 10.1.

conda create --name scv
conda activate scv
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch
pip install faiss-gpu

Required Data

To evaluate/train SCV, 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=checkpoints/quarter/scv-chairs.pth --dataset=chairs

Training

We used the following training schedule in our paper (2 GPUs).

./train.sh

License

WTFPL. See LICENSE file.

Acknowledgement

The overall code framework is adapted from RAFT. We thank the authors for the contribution.