  With the recent proliferation of consumer-grade 360{\deg} cameras, it is
worth revisiting visual perception challenges with spherical cameras given the
potential benefit of their global field of view. To this end we introduce a
spherical convolutional hourglass network (SCHN) for the dense labeling on the
sphere. The SCHN is invariant to camera orientation (lifting the usual
requirement for `upright' panoramic images), and its design is scalable for
larger practical datasets. Initial experiments show promising results on a
spherical semantic segmentation task.
