This paper presents a novel data-driven paradigm based on 3D point clouds for the automated management and maintenance of bridges, which are suffering from aging and deterioration. To overcome the inefficiencies of existing manual inspection methods, we propose a systematic 3D bridge data generation framework that addresses the challenges of insufficient real-world data (missing labels and scanning interference). This framework automatically generates complete point clouds with component-level instance annotations, high-fidelity colors, and precise normal vectors. Furthermore, it generates diverse and physically realistic incomplete point clouds to support the training of segmentation and completion networks. Experimental results demonstrate that a PointNet++ model trained on synthetic data achieved an average IoU of 84.2% for semantic segmentation of real bridges, while a fine-tuned KT-Net demonstrated superior performance in component completion. This research provides an innovative methodology and a foundational dataset for 3D bridge structural visual analysis, which has significant implications for the advancement of automated infrastructure management and maintenance.
