Scene Flow Estimation from Light Fields via the Preconditioned Primal-Dual Algorithm

Stefan Heber, Thomas Pock

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we present a novel variational model to jointly estimate geometry and motion from a sequence of light fields captured with a plenoptic camera. The proposed model uses the so-called sub-aperture representation of the light field. Sub-aperture images represent images with slightly different viewpoints, which can be extracted from the light field. The sub-aperture representation allows us to formulate a convex global energy functional, which enforces multi-view geometry consistency, and piecewise smoothness assumptions on the scene flow variables. We optimize the proposed scene flow model by using an efficient preconditioned primal-dual algorithm. Finally, we also present synthetic and real world experiments.
Original languageEnglish
Title of host publicationPattern Recognition: 36th German Conference, GCPR 2014, M{\"u}nster, Germany, September 2-5, 2014, Proceedings
PublisherSpringer International Publishing AG
Pages3-14
Volume8753
ISBN (Electronic)978-3-319-11752-2
ISBN (Print)978-3-319-11751-5
DOIs
Publication statusAccepted/In press - 2014

Fields of Expertise

  • Information, Communication & Computing

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