Abstract
Abstract. In the past decade, a lot of effort is put into applying digital innovations to building life cycles. 3D Models have been proven to be efficient for decision making, scenario simulation and 3D data analysis during this life cycle. Creating such digital representation of a building can be a labour-intensive task, depending on the desired scale and level of detail (LOD). This research aims at creating a new automatic deep learning based method for building model reconstruction. It combines exterior and interior data sources: 1) 3D BAG, 2) archived floor plan images. To reconstruct 3D building models from the two data sources, an innovative combination of methods is proposed. In order to obtain the information needed from the floor plan images (walls, openings and labels), deep learning techniques have been used. In addition, post-processing techniques are introduced to transform the data in the required format. In order to fuse the extracted 2D data and the 3D exterior, a data fusion process is introduced. From the literature review, no prior research on automatic integration of CityGML/JSON and floor plan images has been found. Therefore, this method is a first approach to this data integration.
Highlights
Buildings have a significant role in our daily lives
A lot of effort is put into applying digital innovations to building life cycles (planning, construction, operation, renovation and demolition (Ngwepe and Aigbavboa, 2015)). 3D Models have been proven to be efficient for decision making, scenario simulation and 3D data analysis during this life cycle (Rajat Agarwal and Sridhar, 2016)
The exterior 3D data is in CityJSON format and the interior 2D data is provided by the municipality of Rijssen-Holten, who are interested in the current research
Summary
Buildings have a significant role in our daily lives. a lot of effort is put into improving them. 3D Models have been proven to be efficient for decision making, scenario simulation and 3D data analysis during this life cycle (Rajat Agarwal and Sridhar, 2016) Creating such digital representation of a building can be a labour-intensive task, depending on the desired scale and level of detail (LOD). This research aims at creating a new automatic deep learning based method for building model reconstruction It combines exterior and interior data sources: 1) 3D BAG, the first fully automatically generated 3D building data set with level of detail 2.21, 2) archived floor plan images (e.g. scanned or exported from CAD software). The deep learning breakthrough for floor plan parsing was presented in resarch by Chen Liu (Liu, 2017), that used deep learning to vectorize rasterized images It does so by using a discriminative network to obtain junctions, integer programming to obtain primitives and post-processing to obtain a vector format.
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