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Thesis

Learning shape from images

Abstract:

This thesis explores how to harness neural networks to learn 3D structure from visual data. Being able to estimate 3D structure is of vital importance in many applications such as VR/AR, medical imaging, and computational photography. Recently, neural networks have been shown to be effective at learning complex functions from data, including in a 3D setting. The central question this thesis addresses is how to overcome the limitations inherent in a strongly supervised application of neural...

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Division:
MPLS
Department:
Engineering Science
Role:
Author

Contributors

Role:
Contributor
ORCID:
0000-0002-8945-8573
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Name:
Engineering and Physical Sciences Research Council
Funder identifier:
http://dx.doi.org/10.13039/501100000266
Grant:
Seebibyte EP/M013774/1
EPSRC Excellence Award
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/M013774/1
Programme:
Seebibyte
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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