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Capturing the geometry of object categories from video supervision

Abstract:

In this article, we are interested in capturing the 3D geometry of object categories simply by looking around them. Our unsupervised method fundamentally departs from traditional approaches that require either CAD models or manual supervision. It only uses video sequences capturing a handful of instances of an object category to train a deep architecture tailored for extracting 3D geometry predictions. Our deep architecture has three components. First, a Siamese viewpoint factorization networ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/tpami.2018.2871117

Authors


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Institution:
University of Oxford
Department:
Engineering Science
Oxford college:
New College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
New College
Role:
Author
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Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence Journal website
Volume:
42
Issue:
2
Pages:
261 - 275
Publication date:
2018-06-14
Acceptance date:
2016-12-19
DOI:
EISSN:
1939-3539
ISSN:
0162-8828
Pmid:
30235118
Source identifiers:
920896
Language:
English
Keywords:
Pubs id:
pubs:920896
UUID:
uuid:51fa438e-ed36-4043-a6f4-a30609c9e428
Local pid:
pubs:920896
Deposit date:
2018-10-23

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