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From visual query to visual portrayal

Abstract:
In this paper we show how online images can be automatically exploited for scene visualization and reconstruction starting from a mere visual query provided by the user. A visual query is used to retrieve images of a landmark place using a visual search engine. These images are used to reconstruct robust 3–D features and camera poses in projective space. Novel views are then rendered corresponding to a virtual camera flying smoothly through the projective space by triangulation of the projected points in the output view. We introduce a method to fuse the rendered novel views from all input images at each virtual view point by computing their intrinsic image and illuminations. This approach allows us to remove the occlusions and maintain consistent and controlled illumination throughout the rendered sequence. We demonstrate the performance of our prototype system on two landmark structures.
Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
https://bmva-archive.org.uk/bmvc/2008/papers/195.html

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0009-0006-0259-5732
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author


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Funder identifier:
https://ror.org/0439y7842
Grant:
EP/D0370771/1
EP/C007220/1
GR/T24685


Publisher:
British Machine Vision Association
Host title:
Proceedings of the British Machine Vision Conference 2008
Pages:
117.1-117.10
Publication date:
2008-01-01
Event title:
20th British Machine Vision Conference (BMVC 2008)
Event location:
Leeds, UK
Event start date:
2008-09-01
Event end date:
2008-09-08
ISBN:
1901725367


Language:
English
Pubs id:
971472
Local pid:
pubs:971472
Deposit date:
2024-05-21
ARK identifier:

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