Non-central panorama indoor dataset
This dataset addresses a gap for researchers in computer vision and scene understanding by providing a new resource for training and evaluating algorithms, though it is incremental as it fills a specific data need rather than introducing a novel method.
The authors tackled the lack of annotated datasets for non-central panoramas by creating the first dataset of 2574 RGB non-central panoramas for indoor scene understanding, including depth maps and annotations for room layout and camera pose.
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 [2]. However, due to the lack of commercial non-central devices, up until now there was no dataset of these kinds of panoramas. In this data paper, we present the first dataset of non-central panoramas for indoor scene understanding. The dataset is composed by {\bf 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.