For several years, digital 3D city models have taken a central role in a number of different tasks. These models are used in urban and regional planning, surveying, and navigation and telecommunication technology. They also enable in the environmental field precise analyses and simulations of pollutant, flood, and noise propagation. For many applications, realistic building models are an essential component of a 3D city model. Their manual reconstruction provides good results but is associated with a very high time expenditure. In order to meet the high demand for building models and the increased demands on their level of detail, research has been recently conducted to develop various (semi-)automatic reconstruction methods for the time-efficient and cost-effective generation of 3D building models. The goal of designing an automatic building reconstruction method, which fully meets the high demands of the present day, has, however, not yet been achieved. The reasons for this are manifold; a major reason is, for example, the large number of different and complex roof shapes. In this thesis, a new approach to the fully automatic reconstruction of semantic 3D building models based on airborne LiDAR data is presented. The approach is characterized by a strong integration of building knowledge, which is automatically derived during the reconstruction. The derivation of building knowledge is carried out by employing a grammar whose production rules are applied in several bottom-up and top-down phases. In the selection process of the applicable production rules, methods of reinforcement learning from the field of machine learning are utilized. Thereby, it is taken into account that in complex roof structures and neighboring buildings similar roof elements or roof structures often occur multiple times. Through the application of the grammar, knowledge about the buildings is derived. This knowledge is modeled for each building in a separate multi-scale knowledge graph. The use of the grammar, together with the multi-scale knowledge graphs, in alternating bottom-up and top-down phases provides a reliable and robust derivation of further building knowledge. The grammar-guided method for deriving building knowledge can generally be integrated also into already existing data-driven reconstruction methods to improve their performance. This procedure is demonstrated exemplarily for a data-driven reconstruction based on binary space partitioning. In order to reduce the search space for the automatic derivation of building knowledge, a method for the automatic segmentation of roof surfaces is presented. It uses the building points of the underlying data set and employs the principles of surface growing. However, not only the measured height values of the point cloud are incorporated in this procedure, but additionally also virtual points, which are automatically generated during segmentation. As a result, segments can also grow below other roof surfaces, whereby the derivation of building knowledge and the construction of 3D building models are considerably simplified. For the construction of 3D building models, a method based on half-space modeling is presented. It utilizes the building knowledge in the multi-scale knowledge graphs to define and combine half-spaces, whose hyperplanes result from the segments of the point cloud. In this way, buildings of arbitrary complexity are described in canonical form. The resulting building models have the property that they are always both geometrically and topologically correct. The building geometry of the reconstructed 3D building models can be enriched by the semantic information of the multi-scale knowledge graphs. Thereby, also the need for semantic 3D building models is satisfied. In general, the requirements for reconstructed building models depend on the individual application. To take this into account, two methods are presented which can optionally be executed during the reconstruction. The first method is concerned with the reconstruction of small roof elements (e.g., dormers or chimneys) in order to increase the level of detail of insufficiently detailed building models. It does not reconstruct multiple occurrences of roof elements of similar shape individually from one another, but jointly by means of an ICP (iterative closest point) based method. As a result, also those roof elements are recognized and reconstructed which taken individually would not have been reconstructed. The second method recognizes the frequently occurring regularities in buildings and renders them more strongly in the reconstructed models. For this, divisive clustering methods are applied, both in a local and a global context, to the hyperplanes of the half-spaces that are used to define the half-space models in canonical form. The resulting well-shaped building models are particularly suitable for visualization purposes. Finally, the presented reconstruction method together with the resulting building models is evaluated using a benchmark data set and compared with other current state-of-the-art building reconstruction approaches. The results show that the presented method is robust and transferable to different building shapes and regions, and that the resulting 3D building models have a high quality with regard to their completeness and correctness.


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    Title :

    Grammar-guided reconstruction of semantic 3D building models from airborne LiDAR data using half-space modeling


    Additional title:

    Grammatik gesteuerte Rekonstruktion von semantischen 3D-Gebäudemodellen aus luftgestützten LiDAR-Daten mittels Halbraummodellierung


    Contributors:

    Publication date :

    2018



    Type of media :

    Miscellaneous


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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