High‐Fidelity 3D Large‐Scale Engineering Structure Reconstruction From 2D Images

Engineering structures are essential in the current society, and generating accurate three‐dimensional Digital Twins (3D DTs) is crucial for their effective Structural Health Monitoring (SHM). However, challenges arise due to the complex geometry and details of large‐scale engineering structures. Accordingly, a Computer Vision (CV) method named Adaptive‐Weight 3D Gaussian Splatting (AWGS) was proposed. The method employs an adaptive weighting strategy to balance reconstruction objectives and utilizes 3D smoothing and two‐dimensional (2D) Mip filters to suppress artifacts caused by inconsistent multiview sampling. The performance of AWGS was validated on two practical engineering structures: Gerrard Hall in the United States and an extra‐large concrete bridge in China. Results show that AWGS generates highly realistic 3D models with accurately preserved structural details. Furthermore, under the fixed training configurations, AWGS exhibited empirically stable sampled GPU‐memory trajectories while maintaining high reconstruction fidelity. These measurements characterize the tested implementation rather than an explicitly enforced algorithmic memory bound. Additionally, robustness analysis indicates that even under severe noise and occlusion, AWGS maintains structural coherence and achieves stable performance. These capabilities are expected to provide significant technical advancements for further intelligent inspection and SHM applications.