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

TL;DR

This paper introduces Delaunay Canopy, a novel method for building wireframe reconstruction from airborne LiDAR data that uses Delaunay graphs to improve accuracy in noisy and sparse regions.

Contribution

It proposes a Delaunay graph-based approach with scoring and region-wise curvature signatures to enhance wireframe reconstruction accuracy in challenging conditions.

Findings

Achieves state-of-the-art wireframe reconstruction results.

Effectively handles noisy, sparse, and complex building geometries.

Demonstrates robustness across diverse datasets.

Abstract

Reconstructing building wireframe from airborne LiDAR point clouds yields a compact, topology-centric representation that enables structural understanding beyond dense meshes. Yet a key limitation persists: conventional methods have failed to achieve accurate wireframe reconstruction in regions afflicted by significant noise, sparsity, or internal corners. This failure stems from the inability to establish an adaptive search space to effectively leverage the rich 3D geometry of large, sparse building point clouds. In this work, we address this challenge with Delaunay Canopy, which utilizes the Delaunay graph as a geometric prior to define a geometrically adaptive search space. Central to our approach is Delaunay Graph Scoring, which not only reconstructs the underlying geometric manifold but also yields region-wise curvature signatures to robustly guide the reconstruction. Built on this foundation, our corner and wire selection modules leverage the Delaunay-induced prior to focus on highly probable elements, thereby shaping the search space and enabling accurate prediction even in previously intractable regions. Extensive experiments on the Building3D Tallinn city and entry-level datasets demonstrate state-of-the-art wireframe reconstruction, delivering accurate predictions across diverse and complex building geometries.