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A depth–spatial alignment method for multi-source point clouds on large-scale construction sites
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DOI：10.1111/mice.70120.png)
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Abstract
En 中文
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.
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C
IF:
9.1
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2.0K
Citations:
10.0K