THEOStereo

THEOStereo is a dataset providing synthetic stereo image pairs and their corresponding scene depth and will be published along with [1]. All images follow the omnidirectional camera model. In total, there are *31,250* omnidirectional images pairs. The training set contains *25,000* image pairs. For validation and testing there are *3,125* image pairs, respectively. For each pair, there is a ground truth depth map describing the pixel-wise distance of the object along the left camera's z-axis. The virtual omnidirectional cameras exhibit a FOV of *180* degrees and can be described using Kannala's camera model [2]. The distortion parameters are *k_1 = 1* and *k_2 = k_3 = k_4 = k_5 = 0*. The length of the stereo camera's baseline was *0.3* AU (approx. *15* cm, not *30* cm!). Please do not forget to cite [1] if you use the dataset in your work. Thank you.

## Structure of the Dataset

```
.
├── README.md
├── test
│   ├── depth_exr_abs
│   ├── img_stereo_webp
│   └── img_webp
├── train
│   ├── depth_exr_abs
│   ├── img_stereo_webp
│   └── img_webp
└── valid
    ├── depth_exr_abs
    ├── img_stereo_webp
    └── img_webp
```

The directory `depth_exr_abs` contain the depth maps given in meters. The depth reference to the image of the left camera. All images of the left camera are stored in the `img_webp`. The right camera's images can be found in `img_stereo_webp`.

## License

This dataset is licensed under CC BY 4.0.  
For details, please visit <https://creativecommons.org/licenses/by/4.0/>.

[![CC BY 4.0 Logo](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/by.svg)](https://creativecommons.org/licenses/by/4.0/)

## Conference paper
The conference paper can be downloaded from [here](https://www.scitepress.org/Papers/2021/103248/103248.pdf).

## BibTex
If you use the dataset in your work, we would kindly ask you to cite [1].
You might want to use the following BibTex entry:
```bibtex
@inproceedings{seuffert_study_2021,
	address = {Online Conference},
	title = {A {Study} on the {Influence} of {Omnidirectional} {Distortion} on {CNN}-based {Stereo} {Vision}},
	isbn = {978-989-758-488-6},
	doi = {10.5220/0010324808090816},
	booktitle = {Proceedings of the 16th {International} {Joint} {Conference} on {Computer} {Vision}, {Imaging} and {Computer} {Graphics} {Theory} and {Applications}, {VISIGRAPP} 2021, {Volume} 5: {VISAPP}},
	publisher = {SciTePress},
	author = {Seuffert, Julian Bruno and Perez Grassi, Ana Cecilia and Scheck, Tobias and Hirtz, Gangolf},
	year = {2021},
	month = {2},
	pages = {809--816}
}
```

## References
[1] J. B. Seuffert, A. C. Perez Grassi, T. Scheck, and G. Hirtz, “A Study on the Influence of Omnidirectional Distortion on CNN-based Stereo Vision,” in *Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021, Volume 5: VISAPP*, Online Conference, Feb. 2021, pp. 809–816, doi: 10.5220/0010324808090816.

[2] J. Kannala, J. Heikkilä, and S. S. Brandt, “Geometric Camera Calibration,” in *Wiley Encyclopedia of Computer Science
and Engineering*, B. W. Wah, Ed. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008.

Introducing paper: A Study on the Influence of Omnidirectional Distortion on CNN-based Stereo Vision