3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods

Research on converting 2D raster drawings into 3D vector data has a long history in the field of pattern recognition. Prior to the achievement of machine learning, existing studies were based on heuristics and rules. In recent years, there have been several studies employing deep learning, but a gre...

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Autores principales:	Sungsoo Park, Hyeoncheol Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning 2D floor plan 3D model data based methods rule based methods ensemble methods Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/f29d4d6d70e445e496c2c61da2269e85
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id	oai:doaj.org-article:f29d4d6d70e445e496c2c61da2269e85
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spelling	oai:doaj.org-article:f29d4d6d70e445e496c2c61da2269e852021-11-25T17:24:01Z3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods10.3390/electronics102227292079-9292https://doaj.org/article/f29d4d6d70e445e496c2c61da2269e852021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2729https://doaj.org/toc/2079-9292Research on converting 2D raster drawings into 3D vector data has a long history in the field of pattern recognition. Prior to the achievement of machine learning, existing studies were based on heuristics and rules. In recent years, there have been several studies employing deep learning, but a great effort was required to secure a large amount of data for learning. In this study, to overcome these limitations, we used 3DPlanNet Ensemble methods incorporating rule-based heuristic methods to learn with only a small amount of data (30 floor plan images). Experimentally, this method produced a wall accuracy of more than 95% and an object accuracy similar to that of a previous study using a large amount of learning data. In addition, 2D drawings without dimension information were converted into ground truth sizes with an accuracy of 97% or more, and structural data in the form of 3D models in which layers were divided for each object, such as walls, doors, windows, and rooms, were created. Using the 3DPlanNet Ensemble proposed in this study, we generated 110,000 3D vector data with a wall accuracy of 95% or more from 2D raster drawings end to end.Sungsoo ParkHyeoncheol KimMDPI AGarticledeep learning2D floor plan3D modeldata based methodsrule based methodsensemble methodsElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2729, p 2729 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning 2D floor plan 3D model data based methods rule based methods ensemble methods Electronics TK7800-8360
spellingShingle	deep learning 2D floor plan 3D model data based methods rule based methods ensemble methods Electronics TK7800-8360 Sungsoo Park Hyeoncheol Kim 3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods
description	Research on converting 2D raster drawings into 3D vector data has a long history in the field of pattern recognition. Prior to the achievement of machine learning, existing studies were based on heuristics and rules. In recent years, there have been several studies employing deep learning, but a great effort was required to secure a large amount of data for learning. In this study, to overcome these limitations, we used 3DPlanNet Ensemble methods incorporating rule-based heuristic methods to learn with only a small amount of data (30 floor plan images). Experimentally, this method produced a wall accuracy of more than 95% and an object accuracy similar to that of a previous study using a large amount of learning data. In addition, 2D drawings without dimension information were converted into ground truth sizes with an accuracy of 97% or more, and structural data in the form of 3D models in which layers were divided for each object, such as walls, doors, windows, and rooms, were created. Using the 3DPlanNet Ensemble proposed in this study, we generated 110,000 3D vector data with a wall accuracy of 95% or more from 2D raster drawings end to end.
format	article
author	Sungsoo Park Hyeoncheol Kim
author_facet	Sungsoo Park Hyeoncheol Kim
author_sort	Sungsoo Park
title	3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods
title_short	3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods
title_full	3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods
title_fullStr	3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods
title_full_unstemmed	3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods
title_sort	3dplannet: generating 3d models from 2d floor plan images using ensemble methods
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f29d4d6d70e445e496c2c61da2269e85
work_keys_str_mv	AT sungsoopark 3dplannetgenerating3dmodelsfrom2dfloorplanimagesusingensemblemethods AT hyeoncheolkim 3dplannetgenerating3dmodelsfrom2dfloorplanimagesusingensemblemethods
_version_	1718412444754247680

3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods

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