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Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners

In this paper we present a method that automatically yields Boundary Representation Models (B-rep) for indoors after processing dense point clouds collected by laser scanners from key locations through an existing facility. Our objective is particularly focused on providing single models which conta...

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Detalles Bibliográficos
Autores principales: Valero, Enrique, Adán, Antonio, Cerrada, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571773/
https://www.ncbi.nlm.nih.gov/pubmed/23443369
http://dx.doi.org/10.3390/s121216099
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author Valero, Enrique
Adán, Antonio
Cerrada, Carlos
author_facet Valero, Enrique
Adán, Antonio
Cerrada, Carlos
author_sort Valero, Enrique
collection PubMed
description In this paper we present a method that automatically yields Boundary Representation Models (B-rep) for indoors after processing dense point clouds collected by laser scanners from key locations through an existing facility. Our objective is particularly focused on providing single models which contain the shape, location and relationship of primitive structural elements of inhabited scenarios such as walls, ceilings and floors. We propose a discretization of the space in order to accurately segment the 3D data and generate complete B-rep models of indoors in which faces, edges and vertices are coherently connected. The approach has been tested in real scenarios with data coming from laser scanners yielding promising results. We have deeply evaluated the results by analyzing how reliably these elements can be detected and how accurately they are modeled.
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spelling pubmed-35717732013-02-19 Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners Valero, Enrique Adán, Antonio Cerrada, Carlos Sensors (Basel) Article In this paper we present a method that automatically yields Boundary Representation Models (B-rep) for indoors after processing dense point clouds collected by laser scanners from key locations through an existing facility. Our objective is particularly focused on providing single models which contain the shape, location and relationship of primitive structural elements of inhabited scenarios such as walls, ceilings and floors. We propose a discretization of the space in order to accurately segment the 3D data and generate complete B-rep models of indoors in which faces, edges and vertices are coherently connected. The approach has been tested in real scenarios with data coming from laser scanners yielding promising results. We have deeply evaluated the results by analyzing how reliably these elements can be detected and how accurately they are modeled. Molecular Diversity Preservation International (MDPI) 2012-11-22 /pmc/articles/PMC3571773/ /pubmed/23443369 http://dx.doi.org/10.3390/s121216099 Text en © 2012 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 license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Valero, Enrique
Adán, Antonio
Cerrada, Carlos
Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners
title Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners
title_full Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners
title_fullStr Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners
title_full_unstemmed Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners
title_short Automatic Method for Building Indoor Boundary Models from Dense Point Clouds Collected by Laser Scanners
title_sort automatic method for building indoor boundary models from dense point clouds collected by laser scanners
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571773/
https://www.ncbi.nlm.nih.gov/pubmed/23443369
http://dx.doi.org/10.3390/s121216099
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