license:mit size_categories:10K<n<100K format:imagefolder modality:image library:datasets library:mlcroissant region:us
---
license: mit
pretty_name: DISTOPIA Image Dataset for Distortion Estimation of Transparencies
---

We provide our HQ image dataset DISTOPIA. The dataset consists of 315 panorama scenes (170 urban and 145 nature), projected onto a sphere and rendered using a camera centered in the sphere. We use physically-based rendering. The camera is rotated in small increments to generate multiple perspectives of the scene. In addition to the base image, we render multiple versions with cirular or rectangular glass elements, introducing distortion. Our images have a very high resolution of 2252x2252 pixels. For more details please refer to our paper. 

| ![Image 1](none_24_0_0.jpg) | ![Image 2](rectangular_15_2_53.jpg) | ![Image 3](circle_1_2_61.jpg) |
|------------------|------------------|------------------|
| ![Image 4](none_2_0_0.jpg) | ![Image 5](rectangular_4_0_73.jpg) | ![Image 6](circle_6_1_37.jpg) |
| no distortion   | rectangular-shaped distortion     | circular-shaped distortion     |

If you find our data useful, please consider citing our work:
```
@inproceedings{knauthe2023distortion,
  title={Distortion-based transparency detection using deep learning on a novel synthetic image dataset},
  author={Knauthe, Volker and Thomas Pöllabauer and Faller, Katharina and Kraus, Maurice and Wirth, Tristan and Buelow, Max von and Kuijper, Arjan and Fellner, Dieter W},
  series={Lecture Notes in Computer Science},
  booktitle={Image Analysis},
  pages={251--267},
  year={2023},
  organization={Springer}
}
```