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Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry

Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich...

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Autores principales: Murtiyoso, Arnadi, Pellis, Eugenio, Grussenmeyer, Pierre, Landes, Tania, Masiero, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840648/
https://www.ncbi.nlm.nih.gov/pubmed/35161712
http://dx.doi.org/10.3390/s22030966
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author Murtiyoso, Arnadi
Pellis, Eugenio
Grussenmeyer, Pierre
Landes, Tania
Masiero, Andrea
author_facet Murtiyoso, Arnadi
Pellis, Eugenio
Grussenmeyer, Pierre
Landes, Tania
Masiero, Andrea
author_sort Murtiyoso, Arnadi
collection PubMed
description Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively.
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spelling pubmed-88406482022-02-13 Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry Murtiyoso, Arnadi Pellis, Eugenio Grussenmeyer, Pierre Landes, Tania Masiero, Andrea Sensors (Basel) Article Developments in the field of artificial intelligence have made great strides in the field of automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context of 3D recording technology it has also seen application in several areas, most notably in creating semantically rich point clouds which is usually performed manually. In this paper, we propose the introduction of deep learning-based semantic image segmentation into the photogrammetric 3D reconstruction and classification workflow. The main objective is to be able to introduce semantic classification at the beginning of the classical photogrammetric workflow in order to automatically create classified dense point clouds by the end of the said workflow. In this regard, automatic image masking depending on pre-determined classes were performed using a previously trained neural network. The image masks were then employed during dense image matching in order to constraint the process into the respective classes, thus automatically creating semantically classified point clouds as the final output. Results show that the developed method is promising, with automation of the whole process feasible from input (images) to output (labelled point clouds). Quantitative assessment gave good results for specific classes e.g., building facades and windows, with IoU scores of 0.79 and 0.77 respectively. MDPI 2022-01-26 /pmc/articles/PMC8840648/ /pubmed/35161712 http://dx.doi.org/10.3390/s22030966 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
Murtiyoso, Arnadi
Pellis, Eugenio
Grussenmeyer, Pierre
Landes, Tania
Masiero, Andrea
Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
title Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
title_full Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
title_fullStr Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
title_full_unstemmed Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
title_short Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range Photogrammetry
title_sort towards semantic photogrammetry: generating semantically rich point clouds from architectural close-range photogrammetry
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840648/
https://www.ncbi.nlm.nih.gov/pubmed/35161712
http://dx.doi.org/10.3390/s22030966
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