indoor reconstruction from floorplan images with a deep learning approach

Clicks: 252
ID: 128523
2020
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Abstract
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.
Reference Key
jang2020isprsindoor Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Hanme Jang;Kiyun Yu;JongHyeon Yang
Journal población y desarrollo
Year 2020
DOI
10.3390/ijgi9020065
URL
https://www.mdpi.com/2220-9964/9/2/65
Keywords
semantic segmentation corner detection

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