Abstract
Over the past few years, there has been an increasing need for semantic information in automatic city modelling. However, due to the complexity of building structure, the semantic reconstruction of buildings is still a challenging task because it is difficult to extract architectural rules and semantic information from the data. To improve the insufficiencies, we present a semantic modelling framework-based approach for automated building reconstruction using the semantic information extracted from point clouds or images. In this approach, a semantic modelling framework is designed to describe and generate the building model, and a workflow is established for extracting the semantic information of buildings from an unorganized point cloud and converting the semantic information into the semantic modelling framework. The technical feasibility of our method is validated using three airborne laser scanning datasets, and the results are compared with other related works comprehensively, which indicate that our approach can simplify the reconstruction process from a point cloud and generate 3D building models with high accuracy and rich semantic information.
Highlights
Remote Sens. 2016, 8, 737; doi:10.3390/rs8090737 www.mdpi.com/journal/remotesensingRemote Sens. 2016, 8, 737 approach is unable to be used to design architecture, and there is no semantic information, such as façade, windows, doors, and roofs, that restricts the application of the city models.in the model-driven methods, several grammar-based methods are used for creating models, and most of the methods are inspired by the shape grammar [8], which is used to describe geometric shapes in 2D or 3D
To simplify the semantic expression and building descriptions, we presented a lightweight semantic modelling framework, which included two parts: the extensible building modeling language (XBML) for building description and building element class library that is responsible for geometric implementation in the 3D modelling engine
The algorithms used for recognizing the semantic features from the floor analysis (Figure 11), and the outline information was extracted from the roof point cloud
Summary
Remote Sens. 2016, 8, 737; doi:10.3390/rs8090737 www.mdpi.com/journal/remotesensingRemote Sens. 2016, 8, 737 approach is unable to be used to design architecture, and there is no semantic information, such as façade, windows, doors, and roofs, that restricts the application of the city models.in the model-driven methods, several grammar-based methods are used for creating models, and most of the methods are inspired by the shape grammar [8], which is used to describe geometric shapes in 2D or 3D. 2016, 8, 737 approach is unable to be used to design architecture, and there is no semantic information, such as façade, windows, doors, and roofs, that restricts the application of the city models. Parish et al [9] proposed a system based on the shape grammar and. L-system for the procedural modelling of a large-scale city. Wonka [10] presented the split grammar, a parametric grammar, which was inspired by the shape grammar. The split grammar is primarily used to generate the geometric details on the facades of buildings. Based on their works, Müller et al
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