This is a personal reimplementation of LiteFlowNet3 [1] using PyTorch, which is inspired by the pytorch-liteflownet implementation of LiteFlowNet by sniklaus. Should you be making use of this work, please cite the paper accordingly. Also, make sure to adhere to the licensing terms of the authors.
For the original Caffe version of this work, please see: https://github.com/twhui/LiteFlowNet3
The correlation layer is borrowed from NVIDIA-flownet2-pytorch
cd correlation_package
python setup.py install
Download network-sintel.pytorch from Google-Drive . To run it on your demo pair of images, use the following command. Only sintel-model is supported now. It's tested with pytorch 1.3.0 and cuda-9.0, later pytorch/cuda version should also work.
python run.py
I am afraid that I cannot guarantee that this reimplementation is correct. However, it produced results pretty much identical to the implementation of the original authors in the examples that I tried. There are some numerical deviations that stem from differences in the DownsampleLayer of Caffe and the torch.nn.functional.interpolate function of PyTorch. Please feel free to contribute to this repository by submitting issues and pull requests.
As stated in the licensing terms of the authors of the paper, their material is provided for research purposes only. Please make sure to further consult their licensing terms.
[1] @inproceedings{hui2020liteflownet3,
title={LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation},
author={Hui, Tak-Wai and Loy, Chen Change},
booktitle={European Conference on Computer Vision},
pages={169--184},
year={2020},
organization={Springer}
}
Many code of this repo are borrowed from pytorch-liteflownet. And the correlation layer is borrowed from NVIDIA-Flownet2-pytorch.