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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018 (Oral)
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## PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
### License
Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
### Usage
For Caffe users, please refer to [Caffe/README.md](Caffe/README.md).
For PyTorch users, please refer to [PyTorch/README.md](PyTorch/README.md)
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.
### Paper & Citation
[Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume." CVPR 2018 or arXiv:1709.02371](https://arxiv.org/abs/1709.02371)
[Updated and extended version: "Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation." arXiv:1809.05571](https://arxiv.org/abs/1809.05571)
[Project page link](http://research.nvidia.com/publication/2018-02_PWC-Net:-CNNs-for)
[Talk at robust vision challenge workshop](https://www.youtube.com/watch?v=vVU8XV0Ac_0)
[Talk at CVPR 2018 conference](https://youtu.be/LBJ20kxr1a0?t=421)
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}
}
```
### Related Work from NVIDIA
[flownet2-pytorch](https://github.com/NVIDIA/flownet2-pytorch)
[Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation (ECCV 2018)](https://github.com/NVlabs/learningrigidity)
### Contact
Deqing Sun ([email protected])
Excerpt of 3,371 characters
Read on GitHub
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Ming-Yu Liu 劉洺堉 · NVIDIA · United States
6
Chengqi Deng · Zhejiang University · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9cce3b114c4c540d, topic:computer-vision, readme:computer vision, desc:optical flow
matched fp:9cce3b114c4c540d, topic:pytorch