![]() The presented approach is compared with a random forest (RF) baseline. For each face within the mesh, a multi-scale feature vector is calculated and fed into a 1D convolutional neural network (CNN). Through a hybrid model that combines explicit feature calculation and convolutional feature learning, triangle meshes can be semantically enhanced. Hence, this work presents an approach for semantic segmentation of urban textured meshes. However, in order to generate or extract building models, urban data has to be semantically analyzed. To this end, two approaches are presented - a perceptionbased abstraction and a grammar-based enhancement of building models using category-specific rule sets. This knowledge and respective features can, in turn, be used to modify existing building models to make them better understandable. Thereof, important building category-specific features can be extracted. To investigate the human understanding of building models, user studies on building category classification are performed. The goal of this framework is to generate a geometrically, as well as semantically enriched CityGML LOD3 model from coarse input data. A semi-automatic building reconstruction approach with subsequent grammar-based synthesis of facades is presented. Hence, this work does not focus on a single data representation such as imagery but instead incorporates various representation types to address several aspects of the reconstruction, enhancement, and, most importantly, interpretation of virtual building and city models. Consequently, there is a great variety of geometric representations of urban scenes. There are many applications for digital urban scenes: gaming, urban planning, disaster management, taxation, navigation, and many more. Accordingly, the semantic interpretation of various levels of urban data representations is still in its early stages. To keep detailed urban data understandable for humans, the highest level of detail (LOD) might not always be the best representation to transport intended information. ![]() Correspondingly, the geometric reconstruction of large scale urban scenes is widely solved. The outcomes of these pipelines are various products such as point clouds and textured meshes of complete cities. Structure-from-Motion (SfM), dense image matching (DIM), and multi-view stereo (MVS) algorithms have revolutionized the software side and scale to large data sets. With constant advances both in hardware and software, the availability of urban data is more versatile than ever. Zur Rekonstruktion, Interpretation und Anreicherung von virtuellen Stadtmodellen On the reconstruction, interpretation and enhancement of virtual city models Please use this identifier to cite or link to this item: Authors:
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