Empowering Bridge Digital Twins by Bridging the Data Gap with a Unified Synthesis Framework

TL;DR

This paper introduces a comprehensive framework for generating synthetic 3D bridge data to enhance the training of segmentation and completion models, addressing data incompleteness issues in bridge digital twins.

Contribution

It presents a systematic method for creating complete and incomplete 3D bridge point clouds with detailed annotations, improving model training and generalization.

Findings

PointNet++ trained with synthetic data achieves 84.2% mIoU in real-world segmentation

The framework enables realistic simulation of incomplete point clouds for model robustness

The dataset supports advanced 3D visual analysis of bridge structures.

Abstract

As critical transportation infrastructure, bridges face escalating challenges from aging and deterioration, while traditional manual inspection methods suffer from low efficiency. Although 3D point cloud technology provides a new data-driven paradigm, its application potential is often constrained by the incompleteness of real-world data, which results from missing labels and scanning occlusions. To overcome the bottleneck of insufficient generalization in existing synthetic data methods, this paper proposes a systematic framework for generating 3D bridge data. This framework can automatically generate complete point clouds featuring component-level instance annotations, high-fidelity color, and precise normal vectors. It can be further extended to simulate the creation of diverse and physically realistic incomplete point clouds, designed to support the training of segmentation and completion networks, respectively. Experiments demonstrate that a PointNet++ model trained with our synthetic data achieves a mean Intersection over Union (mIoU) of 84.2% in real-world bridge semantic segmentation. Concurrently, a fine-tuned KT-Net exhibits superior performance on the component completion task. This research offers an innovative methodology and a foundational dataset for the 3D visual analysis of bridge structures, holding significant implications for advancing the automated management and maintenance of infrastructure.