  In this work we contribute a distribution shift benchmark for a computer
vision task; monocular depth estimation. Our differentiation is the
decomposition of the wider distribution shift of uncontrolled testing on
in-the-wild data, to three distinct distribution shifts. Specifically, we
generate data via synthesis and analyze them to produce covariate (color
input), prior (depth output) and concept (their relationship) distribution
shifts. We also synthesize combinations and show how each one is indeed a
different challenge to address, as stacking them produces increased performance
drops and cannot be addressed horizontally using standard approaches.
