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Journal article

Taking visual motion prediction to new heightfields

Abstract:

While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and estimating the associated parameters. In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted relevant states, and then used neural networks to learn the state transitions using simulation runs as training data. Unfortun...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.cviu.2019.02.005

Authors


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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
Department:
Engineering Science
Oxford college:
New College
Role:
Author
Publisher:
Elsevier Publisher's website
Journal:
Computer Vision and Image Understanding Journal website
Volume:
181
Pages:
14-25
Publication date:
2019-02-26
Acceptance date:
2019-02-18
DOI:
EISSN:
1090-235X
ISSN:
1077-3142
Source identifiers:
981679
Pubs id:
pubs:981679
UUID:
uuid:13db6497-b6bc-4da1-8805-e9b9bc800638
Local pid:
pubs:981679
Deposit date:
2019-04-16

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