  Using convolutional neural networks for 360images can induce sub-optimal
performance due to distortions entailed by a planar projection. The distortion
gets deteriorated when a rotation is applied to the 360image. Thus, many
researches based on convolutions attempt to reduce the distortions to learn
accurate representation. In contrast, we leverage the transformer architecture
to solve image classification problems for 360images. Using the proposed
transformer for 360images has two advantages. First, our method does not
require the erroneous planar projection process by sampling pixels from the
sphere surface. Second, our sampling method based on regular polyhedrons makes
low rotation equivariance errors, because specific rotations can be reduced to
permutations of faces. In experiments, we validate our network on two aspects,
as follows. First, we show that using a transformer with highly uniform
sampling methods can help reduce the distortion. Second, we demonstrate that
the transformer architecture can achieve rotation equivariance on specific
rotations. We compare our method to other state-of-the-art algorithms using the
SPH-MNIST, SPH-CIFAR, and SUN360 datasets and show that our method is
competitive with other methods.
