Transcription of Depth Map Prediction from a Single Image using a Multi …
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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. Byleveraging the raw datasets as large sources of training data, our method achievesstate-of-the-art results on both NYU Depth and KITTI, and matches detailed depthboundaries without the need for IntroductionEstimating Depth is an important component of understanding geometric relations within a scene.
state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation. 1 Introduction Estimating depth is an important component of understanding geometric relations within a scene. In turn, such relations help provide richer representations of objects and their environment, often lead-
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