izhimahu / pwc-net_pytorch

PWC-Net for optical flow estimation. (Adapted for pytorch > 1.0 & Python 3 from the the official pubilshed code.)

PWC-Net adapted for pytorch > 1.0 & Python 3 from the the official pubilshed code.

• The PWC model is from https://github.com/NVlabs/PWC-Net/tree/master/PyTorch.
• The correlation_package is from https://github.com/NVIDIA/flownet2-pytorch/tree/master/networks/correlation_package.

Installation

cd models correlation_package
python setup.py install

Gcc version may affect the compilation. I compiled the correlation_package successfully with gcc 5.4.0.

Download pretrained models

Download from https://github.com/NVlabs/PWC-Net/tree/master/PyTorch

• pwc_net_chairs.pth.tar is the pretrained weight using flyingthings3D dataset
• pwc_net.pth.tar is the fine-tuned weight on MPI Sintel

Paper & Citation

Deqing Sun, Xiaodong Yang, Ming-Yu Liu, Jan Kautz. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. CVPR 2018 Oral. Sun, Deqing, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume." arXiv preprint arXiv:1709.02371(https://arxiv.org/abs/1709.02371), 2017. Project webpage: http://research.nvidia.com/publication/2018-02_PWC-Net:-CNNs-for

If you use PWC-Net, please cite the following paper:

@InProceedings{Sun2018PWC-Net,
  author    = {Deqing Sun and Xiaodong Yang and Ming-Yu Liu and Jan Kautz},
  title     = {{PWC-Net}: {CNNs} for Optical Flow Using Pyramid, Warping, and Cost Volume},
  booktitle = CVPR,
  year      = {2018},
}

or the arXiv paper

@article{sun2017pwc,
  author={Sun, Deqing and Yang, Xiaodong and Liu, Ming-Yu and Kautz, Jan},
  title={{PWC-Net}: {CNNs} for Optical Flow Using Pyramid, Warping, and Cost Volume},
  journal={arXiv preprint arXiv:1709.02371},
  year={2017}
}