  A single color image can contain many cues informative towards different
aspects of local geometric structure. We approach the problem of monocular
depth estimation by using a neural network to produce a mid-level
representation that summarizes these cues. This network is trained to
characterize local scene geometry by predicting, at every image location, depth
derivatives of different orders, orientations and scales. However, instead of a
single estimate for each derivative, the network outputs probability
distributions that allow it to express confidence about some coefficients, and
ambiguity about others. Scene depth is then estimated by harmonizing this
overcomplete set of network predictions, using a globalization procedure that
finds a single consistent depth map that best matches all the local derivative
distributions. We demonstrate the efficacy of this approach through evaluation
on the NYU v2 depth data set.
