  Most monocular depth sensing methods use conventionally captured images that
are created without considering scene content. In contrast, animal eyes have
fast mechanical motions, called saccades, that control how the scene is imaged
by the fovea, where resolution is highest. In this paper, we present the
SaccadeCam framework for adaptively distributing resolution onto regions of
interest in the scene. Our algorithm for adaptive resolution is a
self-supervised network and we demonstrate results for end-to-end learning for
monocular depth estimation. We also show preliminary results with a real
SaccadeCam hardware prototype.
