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Depth Map Prediction from a Single Image using a Multi …

Depth Map Prediction from a Single Imageusing a Multi - scale Deep NetworkDavid of Computer Science, Courant Institute, New York UniversityAbstractPredicting Depth is an essential component in understanding the 3D geometry ofa scene. While for stereo images local correspondence suffices for estimation,finding Depth relations from asingle imageis less straightforward, requiring in-tegration of both global and local information from various cues. Moreover, thetask is inherently ambiguous, with a large source of uncertainty coming from theoverall scale . In this paper, we present a new method that addresses this task byemploying two deep network stacks: one that makes a coarse global predictionbased on the entire Image , and another that refines this Prediction locally. We alsoapply a scale -invariant error to help measure Depth relations rather than scale .

In this way, the local network can edit the global prediction to incorporate finer-scale details. 3.1.1 Global Coarse-Scale Network The task of the coarse-scale network is to predict the overall depth map structure using a global view of the scene. The upper layers of this network are fully connected, and thus contain the entire image

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