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VidLoc: A deep spatio-temporal model for 6-DoF video-clip relocalization

Abstract:

Machine learning techniques, namely convolutional neural networks (CNN) and regression forests, have recently shown great promise in performing 6-DoF localization of monocular images. However, in most cases image-sequences, rather only single images, are readily available. To this extent, none of the proposed learning-based approaches exploit the valuable constraint of temporal smoothness, often leading to situations where the per-frame error is larger than the camera motion. In this paper we...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/CVPR.2017.284

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
Kellogg College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Journal website
Publication date:
2017-11-01
Acceptance date:
2017-03-03
DOI:
EISSN:
1063-6919
Source identifiers:
681700
Keywords:
Pubs id:
pubs:681700
UUID:
uuid:015b6ca1-e901-4ea7-95cb-f93df95b099b
Local pid:
pubs:681700
Deposit date:
2018-03-05

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