Abstract. 360?
panoramas are a rich medium, yet notoriously difficult to visualize in the 2D image plane. We explore how intelligent rotations of a spherical
image may enable content-aware projection with fewer perceptible distortions.
Whereas existing approaches assume the viewpoint is fixed, intuitively some
viewing angles within the sphere preserve high-level objects better than others.
To discover the relationship between these optimal snap angles and the spherical panorama’s content, we develop a reinforcement learning approach for the
cubemap projection model. Implemented as a deep recurrent neural network, our
method selects a sequence of rotation actions and receives reward for avoiding
cube boundaries that overlap with important foreground objects. We show our
approach creates more visually pleasing panoramas while using 5x less computation than the baseline