Non-central panorama indoor dataset

Berenguel-Baeta, B. (Universidad de Zaragoza) ; Bermudez-Cameo, J. (Universidad de Zaragoza) ; Guerrero, J.J. (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.dib.2022.108375

Año: 2022

Publicado en: Data in Brief 43 (2022), 108375 [5 pp.]
ISSN: 2352-3409

Factor impacto CITESCORE: 2.6 - General (Q2)

Factor impacto SCIMAGO: 0.258 - Multidisciplinary (Q2) - Education (Q3)

Financiación: info:eu-repo/grantAgreement/ES/AEI-FEDER/RTI2018-096903-B-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)