doi:10.1016/j.dib.2022.108375engBerenguel-Baeta, B.Bermudez-Cameo, J.Guerrero, J.J.Non-central panorama indoor datasetART-2022-129555Omnidirectional 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.2022http://zaguan.unizar.es/record/11827810.1016/j.dib.2022.108375http://zaguan.unizar.es/record/118278oai:zaguan.unizar.es:118278info:eu-repo/grantAgreement/ES/AEI-FEDER/RTI2018-096903-B-I00Data in Brief 43 (2022), 108375 [5 pp.]by-nc-ndhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccess