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Learning 3D scene semantics and structure from a single depth image

Abstract:

In this paper, we aim to understand the semantics and 3D structure of a scene from a single depth image. Recent deep neural networks based methods aim to simultaneously learn object class labels and infer the 3D shape of a scene represented by a large voxel grid. However, individual objects within the scene are usually only represented by a few voxels leading to a loss of geometric detail. In addition, significant computational and memory resources are required to process the large scale voxe...

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Publication status:
Published
Peer review status:
Reviewed (other)
Version:
Publisher's Version

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Publisher copy:
10.1109/CVPRW.2018.00069

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Oxford college:
Exeter College
Role:
Author
ORCID:
0000-0002-2419-4140
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Institution:
University of Oxford
Oxford college:
Hertford College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Keble College
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Balliol College
Role:
Author
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Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Pages:
422-425
Publication date:
2018-12-17
Acceptance date:
2018-04-19
DOI:
EISSN:
2160-7516
ISSN:
2160-7508
Pubs id:
pubs:909845
URN:
uri:bbcdf929-45b7-46f0-8122-996c2da0f9c3
UUID:
uuid:bbcdf929-45b7-46f0-8122-996c2da0f9c3
Local pid:
pubs:909845
ISBN:
9781538661000

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