  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.
