A depth–spatial alignment method for multi‐source point clouds on large‐scale construction sites

Three‐dimensional (3D) site models form the digital foundation for modern construction management. However, creating these models from multi‐source imagery presents two key challenges: accurately georeferencing camera poses during wide‐view acquisition and precisely aligning multiple point clouds that possess non‐uniform accuracy. This paper proposes a two‐stage framework to address these challenges. The first stage performs local‐to‐world registration by integrating ground control points, detected via an enhanced HA‐YOLOv8, as early‐stage constraints in the 3D reconstruction process. The second stage, inter‐model alignment, introduces a novel edge‐aware method that utilizes refined structural edge features to merge local models. The framework was validated using images from crane cameras on a high‐rise project, achieving a final modeling accuracy of 0.121 m for the main structures, resulting from precise registrations with low translation (0.102 m) and rotation (0.051°) errors. This approach provides a robust solution for generating high‐fidelity 3D site models, supporting advanced digital construction applications.