  The majority of modern single-view depth estimation methods predict relative
depth and thus cannot be directly applied in many real-world scenarios, despite
impressive performance in the benchmarks. Moreover, single-view approaches
cannot guarantee consistency across a sequence of frames. Consistency is
typically addressed with test-time optimization of discrepancy across views;
however, it takes hours to process a single scene. In this paper, we present
MEDeA, an efficient multi-view test-time depth adjustment method, that is an
order of magnitude faster than existing test-time approaches. Given RGB frames
with camera parameters, MEDeA predicts initial depth maps, adjusts them by
optimizing local scaling coefficients, and outputs temporally-consistent depth
maps. Contrary to test-time methods requiring normals, optical flow, or
semantics estimation, MEDeA produces high-quality predictions with a depth
estimation network solely. Our method sets a new state-of-the-art on TUM RGB-D,
7Scenes, and ScanNet benchmarks and successfully handles smartphone-captured
data from ARKitScenes dataset.
