Bayesian View Synthesis and Image-Based Rendering Principles

Zitieren

Dateien zu dieser Ressource

Dateien Größe Format Anzeige

Zu diesem Dokument gibt es keine Dateien.

PUJADES, Sergi, Frederic DEVERNAY, Bastian GOLDLÜCKE, 2014. Bayesian View Synthesis and Image-Based Rendering Principles. CVPR 2014 : IEEE Conference on Computer Vision and Pattern Recognition. Columbus, Ohio, 23. Jun 2014 - 28. Jun 2014. In: LISA O' CONNER, , ed.. 2014 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2014 : IEEE Conference on Computer Vision and Pattern Recognition. Columbus, Ohio, 23. Jun 2014 - 28. Jun 2014. New York:IEEE, pp. 3906-3913. ISBN 978-1-4799-5117-8

@inproceedings{Pujades2014Bayes-29117, title={Bayesian View Synthesis and Image-Based Rendering Principles}, year={2014}, doi={10.1109/CVPR.2014.499}, isbn={978-1-4799-5117-8}, address={New York}, publisher={IEEE}, booktitle={2014 IEEE Conference on Computer Vision and Pattern Recognition}, pages={3906--3913}, editor={Lisa O' Conner}, author={Pujades, Sergi and Devernay, Frederic 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/123456789/29117"> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-10-15T07:45:21Z</dcterms:available> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-10-15T07:45:21Z</dc:date> <dcterms:title>Bayesian View Synthesis and Image-Based Rendering Principles</dcterms:title> <dc:language>eng</dc:language> <dcterms:abstract xml:lang="eng">In this paper, we address the problem of synthesizing novel views from a set of input images. State of the art methods, such as the Unstructured Lumigraph, have been using heuristics to combine information from the original views, often using an explicit or implicit approximation of the scene geometry. While the proposed heuristics have been largely explored and proven to work effectively, a Bayesian formulation was recently introduced, formalizing some of the previously proposed heuristics, pointing out which physical phenomena could lie behind each. However, some important heuristics were still not taken into account and lack proper formalization. We contribute a new physics-based generative model and the corresponding Maximum a Posteriori estimate, providing the desired unification between heuristics-based methods and a Bayesian formulation. The key point is to systematically consider the error induced by the uncertainty in the geometric proxy. We provide an extensive discussion, analyzing how the obtained equations explain the heuristics developed in previous methods. Furthermore, we show that our novel Bayesian model significantly improves the quality of novel views, in particular if the scene geometry estimate is inaccurate.</dcterms:abstract> <dc:contributor>Goldlücke, Bastian</dc:contributor> <dc:contributor>Pujades, Sergi</dc:contributor> <dcterms:issued>2014</dcterms:issued> <dc:contributor>Devernay, Frederic</dc:contributor> <dc:creator>Goldlücke, Bastian</dc:creator> <dc:creator>Pujades, Sergi</dc:creator> <dc:creator>Devernay, Frederic</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/29117"/> </rdf:Description> </rdf:RDF>

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto