Enhanced Light Field Reconstruction by Combining Disparity and Texture Information in PSVs via Disparity-Guided Fusion

Chen, Y; Huang, X; An, P; Wu, Q

Enhanced Light Field Reconstruction by Combining Disparity and Texture Information in PSVs via Disparity-Guided Fusion

Chen, Y Huang, X An, P Wu, Q

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Imaging, 2023, 9, pp. 665-677
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Chen, Y
dc.contributor.author	Huang, X
dc.contributor.author	An, P
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483
dc.date.accessioned	2024-03-13T10:18:03Z
dc.date.available	2024-03-13T10:18:03Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Computational Imaging, 2023, 9, pp. 665-677
dc.identifier.issn	2573-0436
dc.identifier.issn	2333-9403
dc.identifier.uri	http://hdl.handle.net/10453/176640
dc.description.abstract	Dense Light Fields (LFs) can be reconstructed based on a few sparse views. One popular pipeline is the explicit-depth-based pipeline, which first estimates disparity maps using the existing sparse views and then produces the object texture of the target views by warping the relevant information from the given sparse views. However, due to errors in disparity estimation, such a solution often loses object details. Such problems, particularly in regions of small disparity, can be sorted out by estimating the texture without disparity estimation using the so-called implicit-depth-based pipeline. This work integrates both pipelines. It will adapt to the disparity status in the local region and automatically adjust the LFs produced by both pipelines through disparity-guided fusion. In addition, plane-sweep volumes (PSVs) are exploited for handling the given sparse view information, which can reliably decouple the disparity and texture information into different dimensions. In this way, it can best supply the relevant information to the explicit-depth-based and implicit-depth-based pipelines, respectively. The proposed solution shows superiority in various LF reconstruction tasks in our experiments.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Computational Imaging
dc.relation.isbasedon	10.1109/TCI.2023.3288292
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Enhanced Light Field Reconstruction by Combining Disparity and Texture Information in PSVs via Disparity-Guided Fusion
dc.type	Journal Article
utslib.citation.volume	9
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-13T10:17:53Z
pubs.publication-status	Published
pubs.volume	9

Abstract:

Dense Light Fields (LFs) can be reconstructed based on a few sparse views. One popular pipeline is the explicit-depth-based pipeline, which first estimates disparity maps using the existing sparse views and then produces the object texture of the target views by warping the relevant information from the given sparse views. However, due to errors in disparity estimation, such a solution often loses object details. Such problems, particularly in regions of small disparity, can be sorted out by estimating the texture without disparity estimation using the so-called implicit-depth-based pipeline. This work integrates both pipelines. It will adapt to the disparity status in the local region and automatically adjust the LFs produced by both pipelines through disparity-guided fusion. In addition, plane-sweep volumes (PSVs) are exploited for handling the given sparse view information, which can reliably decouple the disparity and texture information into different dimensions. In this way, it can best supply the relevant information to the explicit-depth-based and implicit-depth-based pipelines, respectively. The proposed solution shows superiority in various LF reconstruction tasks in our experiments.

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http://hdl.handle.net/10453/176640