  Semantic segmentation for spherical data is a challenging problem in machine
learning since conventional planar approaches require projecting the spherical
image to the Euclidean plane. Representing the signal on a fundamentally
different topology introduces edges and distortions which impact network
performance. Recently, graph-based approaches have bypassed these challenges to
attain significant improvements by representing the signal on a spherical mesh.
Current approaches to spherical segmentation exclusively use variants of the
UNet architecture, meaning more successful planar architectures remain
unexplored. Inspired by the success of feature pyramid networks (FPNs) in
planar image segmentation, we leverage the pyramidal hierarchy of graph-based
spherical CNNs to design spherical FPNs. Our spherical FPN models show
consistent improvements over spherical UNets, whilst using fewer parameters. On
the Stanford 2D-3D-S dataset, our models achieve state-of-the-art performance
with an mIOU of 48.75, an improvement of 3.75 IoU points over the previous best
spherical CNN.
