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Keep geometry in context: using contextual priors for very-large-scale 3D dense reconstructions

Abstract:

This paper is about the efficient generation of dense models of very large-scale environments from depth data and in particular, stereo-camera-based depth data. Better maps make for better understanding; better understanding leads to better robots, but this comes at a cost: the computational and memory requirements of large dense models can be prohibitive.

We provide the theory and the system needed to create verylarge- scale dense reconstructions. To this end, we leverage three sou...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Keble College
Role:
Author
Publisher:
Robotics: Science and Systems Publisher's website
Journal:
RSS 2016 Workshop: Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics, Sunday, June 19 2016, University of Michigan Journal website
Host title:
RSS 2016 Workshop: Geometry and Beyond - Representations, Physics, and Scene Understanding for Robotics, Sunday, June 19 2016, University of Michigan
Publication date:
2016-01-01
Acceptance date:
2016-05-23
Source identifiers:
820373
Pubs id:
pubs:820373
UUID:
uuid:b5f4e591-0078-49fc-a26a-ce4459473fe0
Local pid:
pubs:820373
Deposit date:
2018-01-18

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