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The PyTorch implementation almost matches the Caffe implementation (average EPE on the final pass of the Sintel training set: 2.31 by Pytorch and 2.29 by Caffe).
Network Architecture
PWC-Net fuses several classic optical flow estimation techniques, including image pyramid, warping, and cost volume, in an end-to-end trainable deep neural networks for achieving state-of-the-art results.
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}
}
or the updated and extended version
@article{Sun2018:Model:Training:Flow,
author={Sun, Deqing and Yang, Xiaodong and Liu, Ming-Yu and Kautz, Jan},
title={Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
note = {to appear}
}
For multi-frame flow, please also cite
@inproceedings{ren2018fusion,
title={A Fusion Approach for Multi-Frame Optical Flow Estimation},
author={Ren, Zhile and Gallo, Orazio and Sun, Deqing and Yang, Ming-Hsuan and Sudderth, Erik B and Kautz, Jan},
booktitle={Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2019}
}