  We present a novel method for the upright adjustment of 360 images. Our
network consists of two modules, which are a convolutional neural network (CNN)
and a graph convolutional network (GCN). The input 360 images is processed with
the CNN for visual feature extraction, and the extracted feature map is
converted into a graph that finds a spherical representation of the input. We
also introduce a novel loss function to address the issue of discrete
probability distributions defined on the surface of a sphere. Experimental
results demonstrate that our method outperforms fully connected based methods.
