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/.

Conference paper

The conference paper can be downloaded from here.

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:

@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.