MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
This work addresses the challenge of generating detailed 3D building models for applications in urban planning and mapping, representing an incremental advancement in reconstruction methods.
The paper tackles the problem of creating highly-detailed LoD3 building models, which are not widely available, by introducing a refinement strategy that uses lower LoD models and MLS point clouds to enable at-scale reconstruction and standard-consistent modeling.
Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds. Such a strategy promises at-scale LoD3 reconstruction and unlocks LoD3 applications, which we also describe and illustrate in this paper. Additionally, we present guidelines for reconstructing LoD3 facade elements and their embedding into the CityGML standard model, disseminating gained knowledge to academics and professionals. We believe that our method can foster development of LoD3 reconstruction algorithms and subsequently enable their wider adoption.