Title
Indoor Mapping And Modeling By Parsing Floor Plan Images
Abstract
A large proportion of indoor spatial data is generated by parsing floor plans. However, a mature and automatic solution for generating high-quality building elements (e.g., walls and doors) and space partitions (e.g., rooms) is still lacking. In this study, we present a two-stage approach to indoor mapping and modeling (IMM) from floor plan images. The first stage vectorizes the building elements on the floor plan images and the second stage repairs the topological inconsistencies between the building elements, separates indoor spaces, and generates indoor maps and models. To reduce the shape complexity of indoor boundary elements, i.e., walls and openings, we harness the regularity of the boundary elements and extract them as rectangles in the first stage. Furthermore, to resolve the overlaps and gaps of the vectorized results, we propose an optimization model that adjusts the rectangle vertex coordinates to conform to the topological constraints. Experiments demonstrate that our approach achieves a considerable improvement in room detection without conforming to Manhattan World Assumption. Our approach also outputs instance-separate walls with consistent topology, which enables direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML).
Year
DOI
Venue
2021
10.1080/13658816.2020.1781130
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
DocType
Volume
Floor plan, image vectorization, topological consistency, indoor mapping and modeling, indoor location-based services
Journal
35
Issue
ISSN
Citations 
6
1365-8816
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yijie Wu100.34
Jianga Shang2152.21
Pan Chen300.34
S. Zlatanova437750.93
Xuke Hu573.82
Zhiyong Zhou600.34