Dense disparity maps from sparse disparity measurements

Simon Hawe, Martin Kleinsteuber, Klaus Diepold

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

89 Scopus citations

Abstract

In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and recovers the disparity map efficiently. Experiments are provided that show the effectiveness of the proposed approach and robust behavior under noisy conditions.

Original languageEnglish
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages2126-2133
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: 6 Nov 201113 Nov 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference2011 IEEE International Conference on Computer Vision, ICCV 2011
Country/TerritorySpain
CityBarcelona
Period6/11/1113/11/11

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