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In this work we contribute a distribution shift benchmark for a computervision task; monocular depth estimation. Our differentiation is thedecomposition of the wider distribution shift of uncontrolled testing onin-the-wild data, to three distinct distribution shifts. Specifically, wegenerate data via synthesis and analyze them to produce covariate (colorinput), prior (depth output) and concept (their relationship) distributionshifts. We also synthesize combinations and show how each one is indeed adifferent challenge to address, as stacking them produces increased performancedrops and cannot be addressed horizontally using standard approaches.

A benchmark with decomposed distribution shifts for 360 monocular depth estimation