Indoor Reconstruction from Floorplan Images with a Deep Learning Approach
Bibliographic Data
| ID | 22031433 |
|---|---|
| Authors | Hanme Jang (0000-0003-3895-4224, Seoul National University), Kiyun Yu (Seoul National University), JongHyeon Yang (0000-0003-4827-242X, Seoul National University, corresponding author) |
| Year | 2020 |
| Volume | 9 |
| Issue | 2 |
| Pages | 65 |
| Publication date | 2020-01-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9020065 |
| OpenAlex | W3001213871 |
| Language | EN |
| Citations received | 2 |
| References cited | 24 |
Although interest in indoor space modeling is increasing, the quantity of indoor spatial data available is currently very scarce compared to its demand. Many studies have been carried out to acquire indoor spatial information from floorplan images because they are relatively cheap and easy to access. However, existing studies do not take international standards and usability into consideration, they consider only 2D geometry. This study aims to generate basic data that can be converted to indoor spatial information using IndoorGML (Indoor Geography Markup Language) thick wall model or the CityGML (City Geography Markup Language) level of detail 2 by creating vector-formed data while preserving wall thickness. To achieve this, recent Convolutional Neural Networks are used on floorplan images to detect wall and door pixels. Additionally, centerline and corner detection algorithms were applied to convert wall and door images into vector data. In this manner, we obtained high-quality raster segmentation results and reliable vector data with node-edge structure and thickness attributes that enabled the structures of vertical and horizontal wall segments and diagonal walls to be determined with precision. Some of the vector results were converted into CityGML and IndoorGML form and visualized, demonstrating the validity of our work
Bilinear interpolation · CityGML · Computer vision · Data mining · Floorplan · Geography · Inverse distance weighting · Markup language · Multivariate interpolation · Pixel · Raster data · Raster graphics · Remote sensing · Segmentation · Spatial analysis · Visualization · XML · 3D Modeling in Geospatial Applications · Automated Road and Building Extraction · Computer Science · Remote Sensing and LiDAR Applications · Artificial Intelligence
| Citations per year | 0.4 |
|---|---|
| Citation span | 2021 - 2024 (4) |
| Highly cited | No |