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Dense 3D object reconstruction from a single depth view

Abstract:

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high r...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/TPAMI.2018.2868195

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
Exeter College
Role:
Author
ORCID:
0000-0002-2419-4140
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence Journal website
Volume:
41
Issue:
12
Pages:
2820-2834
Publication date:
2018-09-03
Acceptance date:
2018-08-22
DOI:
EISSN:
1939-3539
Source identifiers:
909843
Keywords:
Pubs id:
pubs:909843
UUID:
uuid:6e2957a8-300c-49a7-b08e-99f1a755fd9c
Local pid:
pubs:909843
Deposit date:
2018-08-24

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