Refinement of semantic 3D building models by reconstructing underpasses from MLS point clouds

Semantic 3D building models are provided by public authorities and can be used in applications, such as urban planning, simulations, navigation, and many others. Since large-scale 3D models are typically derived from top-view digital surface models (DSM), they can have detailed roof structures but render planes for façade elements. Furthermore, buildings’ underpasses are often unmodeled, which impacts road space modeling and the building’s volume score. For refining semantic 3D building models, point clouds obtained from mobile laser scanning (MLS) seem to be suitable. In this paper, we present a method of underpass reconstruction by comparing building models’ façades with co-registered MLS measurements. As an alternative approach to from-scratch reconstruction, it exploits existing semantic 3D building models and street-level MLS point clouds to enhance models where required. The method considers the uncertainties of 3D models and measurements in a Bayesian network. Analyzed conflicts between the two representations resulting from ray tracing are used to delineate the underpass’s contours on a façade. Generalized contours are extruded to 3D solid geometries and subtracted from a raw 3D building model, while the semantics is mapped to form an updated semantic 3D building model. The experiments show that the method reaches an accuracy of 12 cm while testing on CityGML LoD2 building models and the open point cloud datasets TUM-MLS-2016 and TUM-FAÇADE representing the Technical University of Munich (TUM) city campus. The validation reveals differences between the reconstructed and updated models in both volumes (up to 18%) and surfaces (up to 20%). Such an extension of road corridors can improve 3D map usage for vehicle navigation and urban simulations.