| Autor: |
Bruno Berenguel-Baeta, Jesus Bermudez-Cameo, Jose J. Guerrero |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Data in Brief, Vol 43, Iss , Pp 108375- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2352-3409 |
| DOI: |
10.1016/j.dib.2022.108375 |
| Popis: |
Omnidirectional images are one of the main sources of information for learning-based scene understanding algorithms. However, annotated datasets of omnidirectional images cannot keep the pace of these learning-based algorithms development. Among the different panoramas and in contrast to standard central ones, non-central panoramas provide geometrical information in the distortion of the image from which we can retrieve 3D information of the environment. However, due to the lack of commercial non-central devices, up until now there was no dataset of these kind of panoramas. In this data paper, we present the first dataset of non-central panoramas for indoor scene understanding. The dataset is composed of 2574 RGB non-central panoramas taken in around 650 different rooms. Each panorama has associated a depth map and annotations to obtain the layout of the room from the image as a structural edge map, list of corners in the image, the 3D corners of the room and the camera pose. The images are taken from photorealistic virtual environments and pixel-wise automatically annotated. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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