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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.

sequences; however, this relies on the local displacements provided by stereo. There are also several hardware-based solutions for single-image depth estimation. Levin et al. [11] perform depth from defocus using a modified camera aperture, while the Kinect and Kinect v2 use active stereo and time-of-flight to capture depth.

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