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Meshed up: learnt error correction in 3D reconstructions

Abstract:

Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene. This enables us to Improve the reconstruction accuracy. We train a suitably deep ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICRA.2018.8460977

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6562-8454
Publisher:
IEEE
Host title:
Proceedings - IEEE International Conference on Robotics and Automation
Journal:
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) More from this journal
Issue:
2018
Pages:
3201-3206
Publication date:
2018-09-10
Acceptance date:
2018-01-26
Event title:
IEEE International Conference on Robotics and Automation
Event location:
Brisbane, Queensland
Event website:
https://www.ieee-ras.org/component/rseventspro/event/570-icra-2018-ieee-international-conference-on-robotics-and-automation
Event start date:
2018-05-21
Event end date:
2018-05-25
DOI:
EISSN:
2577-087X
ISSN:
1050-4729
EISBN:
978-1-5386-3081-5
ISBN:
9781538630815
Language:
English
Keywords:
Pubs id:
992959
Local pid:
pubs:992959
Deposit date:
2021-03-04

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