| Field |
Value |
Language |
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dc.contributor.author |
Hong, W |
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dc.contributor.author |
Zhu, S |
|
|
dc.contributor.author |
Li, J
https://orcid.org/0000-0002-1336-2241
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|
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dc.date.accessioned |
2025-04-10T05:52:12Z |
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dc.date.available |
2025-04-10T05:52:12Z |
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dc.identifier.citation |
Foundations, 4, (1), pp. 61-79 |
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dc.identifier.issn |
0745-3698 |
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dc.identifier.issn |
2673-9321 |
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dc.identifier.uri |
http://hdl.handle.net/10453/186819
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|
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dc.description.abstract |
<jats:p>Natural mathematical objects for representing spatially distributed physical attributes are 3D field functions, which are prevalent in applied sciences and engineering, including areas such as fluid dynamics and computational geometry. The representations of these objects are task-oriented, which are achieved using various techniques that are suitable for specific areas. A recent breakthrough involves using flexible parameterized representations, particularly through neural networks, to model a range of field functions. This technique aims to uncover fields for computational vision tasks, such as representing light-scattering fields. Its effectiveness has led to rapid advancements, enabling the modeling of time dependence in various applications. This survey provides an informative taxonomy of the recent literature in the field of learnable field representation, as well as a comprehensive summary in the application field of visual computing. Open problems in field representation and learning are also discussed, which help shed light on future research.</jats:p> |
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dc.language |
en |
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dc.publisher |
MDPI |
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dc.relation.ispartof |
Foundations |
|
|
dc.relation.isbasedon |
10.3390/foundations4010006 |
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dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.title |
Data-Driven Field Representations and Measuring Processes |
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dc.type |
Journal Article |
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utslib.citation.volume |
4 |
|
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pubs.organisational-group |
University of Technology Sydney |
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|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science |
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pubs.organisational-group |
University of Technology Sydney/UTS Groups |
|
|
pubs.organisational-group |
University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII) |
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|
pubs.organisational-group |
University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR) |
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utslib.copyright.status |
open_access |
* |
|
dc.rights.license |
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ |
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|
dc.date.updated |
2025-04-10T05:52:10Z |
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pubs.issue |
1 |
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pubs.publication-status |
Published online |
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pubs.volume |
4 |
|
|
utslib.citation.issue |
1 |
|
