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Towards the rule-based synthesis of realistic floor plan images

  • Building Information Modeling (BIM) relies on 3D models of buildings, which are often time-consuming and expensive to acquire. To address this challenge, deep learning-based computer vision techniques can be used to generate 3D models from 2D drawings automatically. However, training these deep learning models requires large labeled data sets, and creating custom data sets involves tedious manual labor. To overcome this limitation, we propose an approach to generate synthetic labeled floor plan images for training deep learning models. The method produces floor plans with dimension lines, textual room information, and object detection labels in the Common Objects in Context (COCO) format. Potential future improvements include enhancing drawing style diversity and incorporating additional components such as furniture and technical building equipment.

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Metadaten
Author:Marc BrauksiepeGND, Marwin DollendorfGND, Timo SantehanserGND, Sarah WilkopGND, Phillip SchönfelderGND
URN:urn:nbn:de:hbz:294-100892
DOI:https://doi.org/10.13154/294-10089
Parent Title (German):34th Forum Bauinformatik / 34. Forum Bauinformatik (Bochum, 06. - 08.09.2023)
Subtitle (English):A detailed guide
Document Type:Part of a Book
Language:English
Date of Publication (online):2023/09/05
Date of first Publication:2023/09/05
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Tag:Architectural Drawing; Computer Vision; Floor Plan; Rule-Based; Synthetic Data
GND-Keyword:Deep learning
First Page:316
Last Page:323
Institutes/Facilities:Lehrstuhl für Informatik im Bauwesen
Dewey Decimal Classification:Technik, Medizin, angewandte Wissenschaften / Ingenieurbau, Umwelttechnik
open_access (DINI-Set):open_access
faculties:Fakultät für Bau- und Umweltingenieurwissenschaften
Konferenz-/Sammelbände:34th Forum Bauinformatik / 34. Forum Bauinformatik (Bochum, 06. - 08.09.2023)
Licence (German):License LogoCreative Commons - CC BY 4.0 - Namensnennung 4.0 International

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