This paper proposes a building reconstruction framework for airborne LiDAR data to address the challenge of automated modeling under conditions of uneven point cloud density and missing vertical walls, generating high-precision and structurally compact 3D building models. The method first combines adaptive resolution hypervoxels with a global graph cut optimization strategy to extract precise roof plane primitives from sparse point clouds of buildings. Subsequently, it infers building facades and internal vertical walls based on point cloud projection contours and height change detection, thereby completing the wall structures commonly missing in airborne LiDAR data. Finally, a feature line constraint term is introduced into the hypothesis-and-selection-based reconstruction framework to guide the structural optimization of candidate planes, ensuring the reconstructed model closely matches the actual building geometry. The proposed method was evaluated on multiple public airborne LiDAR datasets, demonstrating its effectiveness through qualitative and quantitative comparisons with various state-of-the-art approaches.
Hai et al. (Fri,) studied this question.