Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Anisimov, Yuriy, Rambach, Jason Raphael, Stricker, Didier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784650120521842688
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