Spherical Vision meets 3D Semantics: towards efficient LOD3 Model Generation for Smart Cities

Abstract. The generation of Level of Detail 3 (LoD3) building models is essential for applications such as urban digital twins, energy analysis, and smart city planning. However, conventional approaches based on terrestrial LiDAR or UAV photogrammetry remain costly, labor-intensive, and difficult to scale. This paper presents a scalable framework for transforming LoD1 building models into LoD3 façade representations using openly available urban data, including OpenStreetMap footprints, street-level spherical imagery, and weak point-cloud priors. The proposed method formulates the reconstruction problem as a facet-based modeling task, where each façade is processed independently in a local coordinate system derived from LoD1 geometry. A rectification strategy is introduced to generate fronto-parallel façade images directly from spherical panoramas, avoiding perspective distortions and facilitating image analysis. To address the challenges of unstructured data acquisition, a visibility-driven view selection scheme and a multi-view fusion framework are developed to construct robust façade evidence maps. The 3D geometry is estimated as a depth field through a multi-resolution optimization framework integrating ray consistency, appearance cues, point-cloud support, and structural regularization. Planar segmentation, polygonization, and geometric regularization are subsequently applied to derive structured façade elements. Openings such as windows and doors are detected using combined geometric and image-based evidence and further refined through architectural constraints. Experimental results demonstrate that the proposed framework enables reliable reconstruction of façade geometry and structural details using only open and low-cost data sources, providing a practical pathway for large-scale LoD3 generation in real urban environments.