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STaRFlow

This repository is the PyTorch implementation of STaRFlow, a recurrent convolutional neural network for multi-frame optical flow estimation. This algorithm is presented in our paper STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation, Pierre Godet, Alexandre Boulch, Aurélien Plyer, and Guy Le Besnerais. [Preprint]

Please cite our paper if you find our work useful.

@article{godet2020starflow,
  title={STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation},
  author={Godet, Pierre and Boulch, Alexandre and Plyer, Aur{\'e}lien and Le Besnerais, Guy},
  journal={arXiv preprint arXiv:2007.05481},
  year={2020}
}

Contact: pierre.godet@onera.fr

Getting started

This code has been developed and tested under Anaconda(Python 3.7, scipy 1.1, numpy 1.16), Pytorch 1.1 and CUDA 10.1 on Ubuntu 18.04.

  1. Please install the followings:

    • Anaconda (Python 3.7)
    • PyTorch 1.1 (Linux, Conda, Python 3.7, CUDA 10) (conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch)
    • Depending on your system, configure -gencode, -ccbin, cuda-path in models/correlation_package/setup.py accordingly
    • scipy 1.1 (conda install scipy=1.1)
    • colorama (conda install colorama)
    • tqdm 4.32 (conda install -c conda-forge tqdm=4.32)
    • pypng (pip install pypng)
  2. Then, install the correlation package:

    ./install.sh
    

Pretrained Models

The saved_checkpoint folder contains the pre-trained models of STaRFlow trained on

  1. FlyingChairsOcc -> FlyingThings3D, or
  2. FlyingChairsOcc -> FlyingThings3D -> MPI Sintel, or
  3. FlyingChairsOcc -> FlyingThings3D -> KITTI (2012 and 2015).

Inference

The script inference.py can be used for testing the pre-trained models. Example:

python inference.py \
  --model StarFlow \
  --checkpoint saved_checkpoint/StarFlow_things/checkpoint_best.ckpt \
  --data-root /data/mpisintelcomplete/training/final/ambush_6/ \
  --file-list frame_0004.png frame_0005.png frame_0006.png frame_0007.png

By default, it saves the results in ./output/.

Training

Data-loaders for multi-frame training can be found in the datasets folder, multi-frame losses are in losses.py, and every architecture used in the experiments presented in our paper is available in the models folder.

Datasets

The datasets used for this project are followings:

Scripts for training

The scripts folder contains training scripts for STaRFlow.
To train the model, you can simply run the script file, e.g., ./train_starflow_chairsocc.sh.
In script files, please configure your own experiment directory (EXPERIMENTS_HOME) and dataset directory in your local system (e.g., SINTEL_HOME or KITTI_HOME).

Acknowledgement

This repository is a fork of the IRR-PWC implementation from Junhwa Hur and Stefan Roth.

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STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation

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