  With the emerging of 360-degree image/video, augmented reality (AR) and
virtual reality (VR), the demand for analysing and processing spherical signals
get tremendous increase. However, plenty of effort paid on planar signals that
projected from spherical signals, which leading to some problems, e.g. waste of
pixels, distortion. Recent advances in spherical CNN have opened up the
possibility of directly analysing spherical signals. However, they pay
attention to the full mesh which makes it infeasible to deal with situations in
real-world application due to the extremely large bandwidth requirement. To
address the bandwidth waste problem associated with 360-degree video streaming
and save computation, we exploit Focused Icosahedral Mesh to represent a small
area and construct matrices to rotate spherical content to the focused mesh
area. We also proposed a novel VertexShuffle operation that can significantly
improve both the performance and the efficiency compared to the original
MeshConv Transpose operation introduced in UGSCNN. We further apply our
proposed methods on super resolution model, which is the first to propose a
spherical super-resolution model that directly operates on a mesh
representation of spherical pixels of 360-degree data. To evaluate our model,
we also collect a set of high-resolution 360-degree videos to generate a
spherical image dataset. Our experiments indicate that our proposed spherical
super-resolution model achieves significant benefits in terms of both
performance and inference time compared to the baseline spherical
super-resolution model that uses the simple MeshConv Transpose operation. In
summary, our model achieves great super-resolution performance on 360-degree
inputs, achieving 32.79 dB PSNR on average when super-resoluting 16x vertices
on the mesh.
