Self-Optimization for Parsing Floor Plans
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 6
Abstract
Floor plan vectorization is an emerging research area in geographic information science and computer vision. However, automated recognition of building elements remains a challenge. This work proposes a method that combines the advantages of classical graphics with deep learning. Specifically, a morphological template is introduced to optimize topological relations, enhance completeness, and suppress conflicts. Bezier curves are utilized to represent irregularity contributing to improving visual effects and experimental accuracy. Thus, the proposed method can be directly practiced to boost performance and correct pseudo-samples in self-training. Experiments demonstrate that the proposed method achieves a considerable improvement in CVC-FP and R2V benchmarks. Additionally, our approach outputs instances with consistent topology, enabling direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML). Hopefully, this work can serve as a new baseline for further study.
Practical Applications
Automatic floor plan recognition has many potential applications. From one perspective, structural elements (walls, doors, and windows) are the most typical objects as they illustrate the primary layout of a building and convey essential information to deploy other components. Identifying structural elements is particularly crucial because it provides design, investigation, and assessment representations. From another perspective, retrieving the room types plays a critical role given that it offers the semantics of a scene. Identifying these elements can avoid tedious secondary measurements and even recover the structure from an advertising paper or poster, facilitating the subsequent application deployment. The applications in Building Information Modeling reconstruction (such as 3D models from 2D maps, architectural arrangement, structural redesign, virtual reality, indoor navigation and modeling, 3D reconstruction from interior photographs, bearing structure analysis, renovation, refurbishment, plan illustration interpretation, apartment price estimation, and accessibility for visually impaired people) could provide substantial support for the smart city.
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Data Availability Statement
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The model and data that support the findings of this study are available from the corresponding author upon reasonable request.
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The R2V annotations are available in a repository online in accordance with funder data retention policies (https://github.com/art-programmer/FloorplanTransformation).
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The R2V images used during the study are proprietary and may only be provided with restrictions by LIFULL (https://www.nii.ac.jp/dsc/idr/en/lifull/).
Acknowledgments
This study was supported by the National Key R&D Program of China (No. 266), and the R2V dataset used in the study was provided by LIFULL.
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Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 6 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 2, 2022
Accepted: Jun 13, 2022
Published online: Sep 8, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 8, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Buildings
- Computer programming
- Computer vision and image processing
- Computing in civil engineering
- Curvature
- Engineering fundamentals
- Floors
- Geographic information systems
- Geomatics
- Geometry
- Graphic methods
- Mathematics
- Methodology (by type)
- Neural networks
- Structural engineering
- Structural systems
- Structures (by type)
- Surveying methods
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