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Volume: 28 | Article ID: art00007
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Depth Extraction from a Single Image Based on Block-Matching and Robust Regression
  DOI :  10.2352/ISSN.2470-1173.2016.5.SDA-434  Published OnlineFebruary 2016
Abstract

Predicting scene depth (or geometric information) from single monocular images is a challenging task. This paper addresses such challenging and essentially ill-posed problem by regression on samples for which the depth is known. In this regard, we first retrieve semantically similar RGB and depth pairs from datasets using a deep convolutional activation feature. We show that our framework provides a richer foundation for depth estimation than existing hand-craft representations. Subsequently, an initial estimation is then integrated by block-matching and robust patch regression. It assigns perceptually appropriate depth values to an input query in accordance with a data-driven depth prior. A final post processor aligns depth maps with RGB discontinuities, resulting in visually plausible results. Experiments on the Make 3D and NYU RGB-D datasets show competitive results compared to recent state-of-the-art methods.

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Hyeongju Jeong, Changjae Oh, Youngjung Kim, Kwanghoon Sohn, "Depth Extraction from a Single Image Based on Block-Matching and Robust Regressionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Stereoscopic Displays and Applications XXVII,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.5.SDA-434

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