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3D object reconstruction from a single depth view with adversarial learning

Abstract:

In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike the 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 by ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1109/ICCVW.2017.86

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Institution:
University of Oxford
Oxford college:
Exeter College
Department:
Oxford, MPLS, Computer Science
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
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Publisher:
Institute of Electrical and Electronics Engineers (IEEE) Publisher's website
Volume:
2017
Publication date:
2018-01-23
Acceptance date:
2017-08-19
DOI:
Pubs id:
pubs:730793
URN:
uri:b2b17a79-ded2-4a64-bc5f-db5a75736bbd
UUID:
uuid:b2b17a79-ded2-4a64-bc5f-db5a75736bbd
Local pid:
pubs:730793

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