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Surface Reconstruction Assessment in Photogrammetric Applications
The image-based 3D reconstruction pipeline aims to generate complete digital representations of the recorded scene, often in the form of 3D surfaces. These surfaces or mesh models are required to be highly detailed as well as accurate enough, especially for metric applications. Surface generation ca...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7594060/ https://www.ncbi.nlm.nih.gov/pubmed/33081315 http://dx.doi.org/10.3390/s20205863 |
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author | Nocerino, Erica Stathopoulou, Elisavet Konstantina Rigon, Simone Remondino, Fabio |
author_facet | Nocerino, Erica Stathopoulou, Elisavet Konstantina Rigon, Simone Remondino, Fabio |
author_sort | Nocerino, Erica |
collection | PubMed |
description | The image-based 3D reconstruction pipeline aims to generate complete digital representations of the recorded scene, often in the form of 3D surfaces. These surfaces or mesh models are required to be highly detailed as well as accurate enough, especially for metric applications. Surface generation can be considered as a problem integrated in the complete 3D reconstruction workflow and thus visibility information (pixel similarity and image orientation) is leveraged in the meshing procedure contributing to an optimal photo-consistent mesh. Other methods tackle the problem as an independent and subsequent step, generating a mesh model starting from a dense 3D point cloud or even using depth maps, discarding input image information. Out of the vast number of approaches for 3D surface generation, in this study, we considered three state of the art methods. Experiments were performed on benchmark and proprietary datasets of varying nature, scale, shape, image resolution and network designs. Several evaluation metrics were introduced and considered to present qualitative and quantitative assessment of the results. |
format | Online Article Text |
id | pubmed-7594060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75940602020-10-30 Surface Reconstruction Assessment in Photogrammetric Applications Nocerino, Erica Stathopoulou, Elisavet Konstantina Rigon, Simone Remondino, Fabio Sensors (Basel) Article The image-based 3D reconstruction pipeline aims to generate complete digital representations of the recorded scene, often in the form of 3D surfaces. These surfaces or mesh models are required to be highly detailed as well as accurate enough, especially for metric applications. Surface generation can be considered as a problem integrated in the complete 3D reconstruction workflow and thus visibility information (pixel similarity and image orientation) is leveraged in the meshing procedure contributing to an optimal photo-consistent mesh. Other methods tackle the problem as an independent and subsequent step, generating a mesh model starting from a dense 3D point cloud or even using depth maps, discarding input image information. Out of the vast number of approaches for 3D surface generation, in this study, we considered three state of the art methods. Experiments were performed on benchmark and proprietary datasets of varying nature, scale, shape, image resolution and network designs. Several evaluation metrics were introduced and considered to present qualitative and quantitative assessment of the results. MDPI 2020-10-16 /pmc/articles/PMC7594060/ /pubmed/33081315 http://dx.doi.org/10.3390/s20205863 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nocerino, Erica Stathopoulou, Elisavet Konstantina Rigon, Simone Remondino, Fabio Surface Reconstruction Assessment in Photogrammetric Applications |
title | Surface Reconstruction Assessment in Photogrammetric Applications |
title_full | Surface Reconstruction Assessment in Photogrammetric Applications |
title_fullStr | Surface Reconstruction Assessment in Photogrammetric Applications |
title_full_unstemmed | Surface Reconstruction Assessment in Photogrammetric Applications |
title_short | Surface Reconstruction Assessment in Photogrammetric Applications |
title_sort | surface reconstruction assessment in photogrammetric applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7594060/ https://www.ncbi.nlm.nih.gov/pubmed/33081315 http://dx.doi.org/10.3390/s20205863 |
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