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A single color image can contain many cues informative towards differentaspects of local geometric structure. We approach the problem of monoculardepth estimation by using a neural network to produce a mid-levelrepresentation that summarizes these cues. This network is trained tocharacterize local scene geometry by predicting, at every image location, depthderivatives of different orders, orientations and scales. However, instead of asingle estimate for each derivative, the network outputs probabilitydistributions that allow it to express confidence about some coefficients, andambiguity about others. Scene depth is then estimated by harmonizing thisovercomplete set of network predictions, using a globalization procedure thatfinds a single consistent depth map that best matches all the local derivativedistributions. We demonstrate the efficacy of this approach through evaluationon the NYU v2 depth data set.

Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions