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2014 | OriginalPaper | Chapter

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

Authors : Stefan Heber, Thomas Pock

Published in: Pattern Recognition

Publisher: Springer International Publishing

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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.

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next chapter Introducing More Physics into Variational Depth–from–Defocus
Footnotes
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Metadata
Title
Scene Flow Estimation from Light Fields via the Preconditioned Primal-Dual Algorithm
Authors
Stefan Heber
Thomas Pock
Copyright Year
2014
Book
Pattern Recognition

Print ISBN: 978-3-319-11751-5

Electronic ISBN: 978-3-319-11752-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-11752-2_1

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