Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph

Reconstructing building wireframe from airborne LiDAR pointclouds yields a compact, topology-centric representation that enablesstructural understanding beyond dense meshes. Yet a key limitation per-sists: conventional methods have failed to achieve accurate wireframe re-construction in regions afflicted by significant noise, sparsity, or internalcorners. This failure stems from the inability to establish an adaptivesearch space to effectively leverage the rich 3D geometry of large, sparsebuilding point clouds. In this work, we address this challenge with De-launay Canopy, which utilizes the Delaunay graph as a geometric priorto define a geometrically adaptive search space. Central to our approachis Delaunay Graph Scoring, which not only reconstructs the underlyinggeometric manifold but also yields region-wise curvature signatures torobustly guide the reconstruction. Built on this foundation, our cornerand wire selection modules leverage the Delaunay-induced prior to focuson highly probable elements, thereby shaping the search space and en-abling accurate prediction even in previously intractable regions. Exten-sive experiments on the Building3D Tallinn city and entry-level datasetsdemonstrate state-of-the-art wireframe reconstruction, delivering accu-rate predictions across diverse and complex building geometries.