REAL-TIME OPTICAL FLOW ESTIMATION

This repository is the implemenation of our final project, Real-Time Optical Flow Estimation, for CS 395T Advanced Computer Vision:

Paper | Video

Training and Evaluation

Please refer to this colab notebook here

Reference

RAFT: Recurrent All Pairs Field Transforms for Optical Flow
ECCV 2020
Zachary Teed and Jia Deng

Requirements

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

conda create --name raft
conda activate raft
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch

Required Data

To evaluate/train 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