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3D modeling of building indoor spaces and closed doors from imagery and point clouds

Identificadores del recurso

Sensors, 15(2): 3491-3512 (2015)

14248220

http://hdl.handle.net/11093/10944

10.3390/s150203491

https://www.mdpi.com/1424-8220/15/2/3491

Procedencia

(Investigo)

Ficha

Título:: 3D modeling of building indoor spaces and closed doors from imagery and point clouds
Tema:: 33 Ciencias Tecnológicas; 3305.22 Metrología de la Edificación
Descripción:: 3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction.; Ministerio de Economía y Competitividad | Ref. FPU AP2010-2969
Idioma:: English
Autor/Productor:: Díaz Vilariño, Lucía; Khoshelham, Kourosh; Martínez Sánchez, Joaquín; Arias Sánchez, Pedro
Editor:: Sensors; Deseño na enxeñaría; Enxeñaría dos recursos naturais e medio ambiente; Xeotecnoloxías Aplicadas
Derechos:: Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/; openAccess
Fecha:: 2026-01-13T17:45:27Z; 2015-02-03; 2026-01-02T18:16:16Z
Tipo de recurso:: article

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  1. < mods:namePart > Díaz Vilariño, Lucía </ mods:namePart >
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  1. < mods:namePart > Khoshelham, Kourosh </ mods:namePart >
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  1. < mods:namePart > Martínez Sánchez, Joaquín </ mods:namePart >
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  1. < mods:namePart > Arias Sánchez, Pedro </ mods:namePart >
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8. < mods:identifier type =" citation " > Sensors, 15(2): 3491-3512 (2015) </ mods:identifier >
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10. < mods:identifier type =" uri " > http://hdl.handle.net/11093/10944 </ mods:identifier >
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12. < mods:identifier type =" editor " > https://www.mdpi.com/1424-8220/15/2/3491 </ mods:identifier >
13. < mods:abstract > 3D models of indoor environments are increasingly gaining importance due to the wide range of applications to which they can be subjected: from redesign and visualization to monitoring and simulation. These models usually exist only for newly constructed buildings; therefore, the development of automatic approaches for reconstructing 3D indoors from imagery and/or point clouds can make the process easier, faster and cheaper. Among the constructive elements defining a building interior, doors are very common elements and their detection can be very useful either for knowing the environment structure, to perform an efficient navigation or to plan appropriate evacuation routes. The fact that doors are topologically connected to walls by being coplanar, together with the unavoidable presence of clutter and occlusions indoors, increases the inherent complexity of the automation of the recognition process. In this work, we present a pipeline of techniques used for the reconstruction and interpretation of building interiors based on point clouds and images. The methodology analyses the visibility problem of indoor environments and goes in depth with door candidate detection. The presented approach is tested in real data sets showing its potential with a high door detection rate and applicability for robust and efficient envelope reconstruction. </ mods:abstract >
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  1. < mods:title > 3D modeling of building indoor spaces and closed doors from imagery and point clouds </ mods:title >
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