  Learning based approaches for depth perception are limited by the
availability of clean training data. This has led to the utilization of view
synthesis as an indirect objective for learning depth estimation using
efficient data acquisition procedures. Nonetheless, most research focuses on
pinhole based monocular vision, with scarce works presenting results for
omnidirectional input. In this work, we explore spherical view synthesis for
learning monocular 360 depth in a self-supervised manner and demonstrate its
feasibility. Under a purely geometrically derived formulation we present
results for horizontal and vertical baselines, as well as for the trinocular
case. Further, we show how to better exploit the expressiveness of traditional
CNNs when applied to the equirectangular domain in an efficient manner.
Finally, given the availability of ground truth depth data, our work is
uniquely positioned to compare view synthesis against direct supervision in a
consistent and fair manner. The results indicate that alternative research
directions might be better suited to enable higher quality depth perception.
Our data, models and code are publicly available at
https://vcl3d.github.io/SphericalViewSynthesis/.
