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.
Keywords: Computer Vision; Indoor Scene Understanding; Layout Estimation; Monocular Depth Estimation; Non-central Panoramas; Omnidirectional Vision.
© 2022 The Author(s). Published by Elsevier Inc.