Combining semantic scene priors and haze removal for single image depth estimation

Ke Wang, Enrique Dunn, Joseph Tighe, Jan Michael Frahm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

We consider the problem of estimating the relative depth of a scene from a monocular image. The dark channel prior, used as a statistical observation of haze free images, has been previously leveraged for haze removal and relative depth estimation tasks. However, as a local measure, it fails to account for higher order semantic relationship among scene elements. We propose a dual channel prior used for identifying pixels that are unlikely to comply with the dark channel assumption, leading to erroneous depth estimates. We further leverage semantic segmentation information and patch match label propagation to enforce semantically consistent geometric priors. Experiments illustrate the quantitative and qualitative advantages of our approach when compared to state of the art methods.

Original languageEnglish
Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Pages800-807
Number of pages8
DOIs
StatePublished - 2014
Event2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States
Duration: 24 Mar 201426 Mar 2014

Publication series

Name2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Conference

Conference2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Country/TerritoryUnited States
CitySteamboat Springs, CO
Period24/03/1426/03/14

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