A Multi-Level 3D Building Reconstruction Framework Integrating LiDAR Point Cloud and Imagery-Based Building Footprints

Three-dimensional (3D) building reconstruction plays an important role in urban planning, disaster resilience, and sustainable infrastructure development. Conventional approaches based on airborne Light Detection and Ranging (LiDAR) data can be computationally intensive and often require large labeled datasets, particularly for large-scale applications. This study presents a semi-automated workflow for reconstructing multi-level 3D building models by integrating airborne LiDAR point cloud data with building footprints extracted from National Agriculture Imagery Program (NAIP) imagery using a Mask Region-Based Convolutional Neural Network (Mask R-CNN). The extracted footprints were used to spatially isolate building-specific LiDAR subsets for 3D reconstruction. The proposed methodology generated building models at multiple Levels of Detail (LOD), ranging from two-dimensional (2D) footprints to volumetric representations with detailed roof structures. Building footprint extraction was quantitatively evaluated against LiDAR-derived footprints generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which served as the reference dataset and detected buildings obscured by tree canopy. The reconstruction workflow was implemented in Open3D and incorporated a boundary-aware mesh refinement strategy based on ear-clipping triangulation to improve rooftop continuity in LOD2 models. The proposed footprint extraction framework achieved a mean Intersection over Union (IoU) of 0.8226 relative to LiDAR-derived reference building footprints, indicating reliable building delineation that supports the proposed multi-level 3D building reconstruction workflow.