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Nonlinear Optimization of Light Field Point Cloud
The problem of accurate three-dimensional reconstruction is important for many research and industrial applications. Light field depth estimation utilizes many observations of the scene and hence can provide accurate reconstruction. We present a method, which enhances existing reconstruction algorit...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838410/ https://www.ncbi.nlm.nih.gov/pubmed/35161563 http://dx.doi.org/10.3390/s22030814 |
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author | Anisimov, Yuriy Rambach, Jason Raphael Stricker, Didier |
author_facet | Anisimov, Yuriy Rambach, Jason Raphael Stricker, Didier |
author_sort | Anisimov, Yuriy |
collection | PubMed |
description | The problem of accurate three-dimensional reconstruction is important for many research and industrial applications. Light field depth estimation utilizes many observations of the scene and hence can provide accurate reconstruction. We present a method, which enhances existing reconstruction algorithm with per-layer disparity filtering and consistency-based holes filling. Together with that we reformulate the reconstruction result to a form of point cloud from different light field viewpoints and propose a non-linear optimization of it. The capability of our method to reconstruct scenes with acceptable quality was verified by evaluation on a publicly available dataset. |
format | Online Article Text |
id | pubmed-8838410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88384102022-02-13 Nonlinear Optimization of Light Field Point Cloud Anisimov, Yuriy Rambach, Jason Raphael Stricker, Didier Sensors (Basel) Article The problem of accurate three-dimensional reconstruction is important for many research and industrial applications. Light field depth estimation utilizes many observations of the scene and hence can provide accurate reconstruction. We present a method, which enhances existing reconstruction algorithm with per-layer disparity filtering and consistency-based holes filling. Together with that we reformulate the reconstruction result to a form of point cloud from different light field viewpoints and propose a non-linear optimization of it. The capability of our method to reconstruct scenes with acceptable quality was verified by evaluation on a publicly available dataset. MDPI 2022-01-21 /pmc/articles/PMC8838410/ /pubmed/35161563 http://dx.doi.org/10.3390/s22030814 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Anisimov, Yuriy Rambach, Jason Raphael Stricker, Didier Nonlinear Optimization of Light Field Point Cloud |
title | Nonlinear Optimization of Light Field Point Cloud |
title_full | Nonlinear Optimization of Light Field Point Cloud |
title_fullStr | Nonlinear Optimization of Light Field Point Cloud |
title_full_unstemmed | Nonlinear Optimization of Light Field Point Cloud |
title_short | Nonlinear Optimization of Light Field Point Cloud |
title_sort | nonlinear optimization of light field point cloud |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838410/ https://www.ncbi.nlm.nih.gov/pubmed/35161563 http://dx.doi.org/10.3390/s22030814 |
work_keys_str_mv | AT anisimovyuriy nonlinearoptimizationoflightfieldpointcloud AT rambachjasonraphael nonlinearoptimizationoflightfieldpointcloud AT strickerdidier nonlinearoptimizationoflightfieldpointcloud |