用德劳内图构建自适应搜索空间，精准重建稀疏点云中的建筑线框。
Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph
- 基于德劳内图定义几何自适应搜索空间，提升点云稀疏区域的建模能力。
- 在塔林建筑数据集上达到最新性能，复杂几何结构重建准确率显著提升。
- 适合需要高精度建筑线框的城市场景理解与数字孪生应用。
从机载激光雷达点云中重建建筑线框，可获得紧凑且拓扑聚焦的表示，超越密集网格的结构理解。然而，现有方法在噪声大、点云稀疏或存在内部拐角的区域仍难以实现精确重建，根源在于无法为大规模稀疏点云的丰富三维几何信息建立自适应搜索空间。本文提出Delaunay Canopy，利用德劳内图作为几何先验，构建几何自适应搜索空间。核心是德劳内图评分机制，不仅重建底层几何流形，还生成局部曲率特征，鲁棒引导重建过程。在此基础上，拐角与线段选择模块利用德劳内诱导的先验，聚焦高概率元素，有效塑造搜索空间，实现对以往难以处理区域的精准预测。在Building3D Tallinn城市数据集及入门级数据集上的大量实验表明，该方法达到当前最优线框重建性能，在多样复杂的建筑几何中均表现优异。
原文摘要 · Abstract (English)
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.
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