Back to articles
Volume: 28 | Article ID: art00007
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

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.

Subject Areas :
Views 132
Downloads 0
 articleview.views 132
 articleview.downloads 0
  Cite this article 

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,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2016
Electronic Imaging
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA