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Depth Map Prediction from a Single ... - New York University

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.

common scale-dependent errors. This focuses attention on the spatial relations within a scene rather than general scale, and is particularly apt for applications such as 3D modeling, where the model is often rescaled during postprocessing. In this paper we present a new approach for estimating depth from a single image. We directly

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