This directory aggregates data from three different sources and
is provided to maximally simplify reproducing our results published as
    @inProceedings{DISKTyszkiewicz20,
      title={DISK: Learning local features with policy gradient},
      author={Tyszkiewicz, Micha{\l} J and Fua, Pascal and Trulls, Eduard},
      booktitle={Proceedings of NeurIPS2020},
      year={2020}
    }
We do not claim any contribution with regards to preparation of the data itself.

`imw2020-val` republishes the validation scenes available at
https://vision.uvic.ca/image-matching-challenge/data/
which should be cited as
    @article{jin2020image,
      title={Image Matching across Wide Baselines: From Paper to Practice},
      author={Jin, Yuhe and Mishkin, Dmytro and Mishchuk, Anastasiia and Matas, Jiri and Fua, Pascal and Yi, Kwang Moo and Trulls, Eduard},
      journal={International Journal of Computer Vision},
      year={2020}
    }


`megadepth` republishes part of the MegaDepth v1 dataset, obtainable at
https://www.cs.cornell.edu/projects/megadepth/, which should be cited as
    @inProceedings{MegaDepthLi18,
      title={MegaDepth: Learning Single-View Depth Prediction from Internet Photos},
      author={Zhengqi Li and Noah Snavely},
      booktitle={Computer Vision and Pattern Recognition (CVPR)},
      year={2018}
    }

which is merged with undistorted images, published by the team behind
    @inproceedings{dusmanu2019d2,
      title={D2-net: A trainable cnn for joint description and detection of local features},
      author={Dusmanu, Mihai and Rocco, Ignacio and Pajdla, Tomas and Pollefeys, Marc and Sivic, Josef and Torii, Akihiko and Sattler, Torsten},
      booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
      pages={8092--8101},
      year={2019}
    }
and available from Google Drive at
https://drive.google.com/open?id=1hxpOsqOZefdrba_BqnW490XpNX_LgXPB

For both datasets we have slightly modified their format and added a
`dataset.json` file used as the entry point for our data loading pipeline.