What Sparse Light Field Coding Reveals about Scene Structure

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JOHANNSEN, Ole, Antonin SULC, Bastian GOLDLÜCKE, 2016. What Sparse Light Field Coding Reveals about Scene Structure. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 27. Jun 2016 - 30. Jun 2016. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 27. Jun 2016 - 30. Jun 2016. Piscataway, NJ:IEEE, pp. 3262-3270. ISBN 978-1-4673-8851-1

@inproceedings{Johannsen2016Spars-38202, title={What Sparse Light Field Coding Reveals about Scene Structure}, year={2016}, doi={10.1109/CVPR.2016.355}, isbn={978-1-4673-8851-1}, address={Piscataway, NJ}, publisher={IEEE}, booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, pages={3262--3270}, author={Johannsen, Ole and Sulc, Antonin and Goldlücke, Bastian} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/38202"> <dc:contributor>Sulc, Antonin</dc:contributor> <dc:language>eng</dc:language> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/38202"/> <dc:creator>Goldlücke, Bastian</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-30T06:26:05Z</dcterms:available> <dc:creator>Johannsen, Ole</dc:creator> <dc:contributor>Goldlücke, Bastian</dc:contributor> <dc:contributor>Johannsen, Ole</dc:contributor> <dc:creator>Sulc, Antonin</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-03-30T06:26:05Z</dc:date> <dcterms:issued>2016</dcterms:issued> <dcterms:title>What Sparse Light Field Coding Reveals about Scene Structure</dcterms:title> <dcterms:abstract xml:lang="eng">In this paper, we propose a novel method for depth estimation in light fields which employs a specifically designed sparse decomposition to leverage the depth-orientation relationship on its epipolar plane images. The proposed method learns the structure of the central view and uses this information to construct a light field dictionary for which groups of atoms correspond to unique disparities. This dictionary is then used to code a sparse representation of the light field. Analyzing the coefficients of this representation with respect to the disparities of their corresponding atoms yields an accurate and robust estimate of depth. In addition, if the light field has multiple depth layers, such as for reflective or transparent surfaces, statistical analysis of the coefficients can be employed to infer the respective depth of the superimposed layers.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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