Airborne laser scanning point clouds constitute a primary data source for city-scale 3D building modeling. However, the automated reconstruction of geometrically accurate and topologically consistent roof wireframes persists as a formidable challenge, hindered by data noise, uneven point density, and the topological complexity of urban roofs. Existing data-driven methods exhibit high sensitivity to local fitting errors, frequently leading to topological inconsistencies, whereas model-driven approaches are constrained by predefined primitive libraries, limiting their generalization to complex composite roofs. To address these limitations, this paper proposes a novel framework for roof wireframe reconstruction that fuses topological data analysis (TDA) with hybrid geometric-topological constraints. A core innovation of this framework is the application of persistent homology to extract globally stable topological skeletons from 2.5D height scalar fields, serving as a robust topological prior independent of local geometric noise. Specifically, the method first generates regularized 3D eave lines constrained by 2D building footprints. Subsequently, a “geometry–topology” dual-domain cross-validation mechanism is used to validate geometric hypotheses derived from planar adjacencies against TDA-extracted topological critical points (ridges and valleys), thereby effectively suppressing spurious structure lines. Finally, a global optimization model governed by the minimum description length principle enforces implicit regularization to rectify local geometric distortions and guarantee topological closure. Experiments on the Trondheim dataset demonstrate that the proposed method achieves a root mean square error of 0.443 m, while enhancing the length-weighted completeness and correctness of structure lines to 96.23% and 97.41%, respectively. These results validate the efficacy of integrating height-field–based topological skeletons for reconstructing complex roof wireframes, offering a scalable and robust solution for automated large-scale urban 3D modeling.
Huo et al. (Thu,) studied this question.