用高斯点云技术实现建筑损伤三维可视化，提升精度与实时性
Three-dimensional Damage Visualization of Civil Structures via Gaussian Splatting-enabled Digital Twins
- 基于高斯点云重建损伤区域，减少分割误差
- 多尺度策略兼顾效率与细节，支持动态更新
- 适合地震后结构评估与数字孪生系统构建
近年来，土木基础设施检测对数字孪生中的三维（3D）损伤可视化需求日益增长，超越传统二维图像识别。相比传统摄影测量3D重建方法，如神经辐射场（NeRF）和高斯点云（GS），现代方法在场景表示、渲染质量及无特征区域处理方面表现更优。其中，GS因其高效性脱颖而出，采用离散的各向异性3D高斯函数表示辐射场，而非NeRF的连续隐式模型。本研究提出一种面向3D损伤可视化的高斯点云增强型数字孪生方法，主要贡献包括：1）利用基于GS的3D重建，将2D损伤分割结果进行三维可视化，同时降低分割误差；2）设计多尺度重建策略，在效率与损伤细节之间取得平衡；3）支持随损伤演化进行数字孪生动态更新。在公开的震后检测合成数据集上验证，该方法为土木基础设施数字孪生中的全面3D损伤可视化提供了可行方案。
原文摘要 · Abstract (English)
Recent advancements in civil infrastructure inspections underscore the need for precise three-dimensional (3D) damage visualization on digital twins, transcending traditional 2D image-based damage identifications. Compared to conventional photogrammetric 3D reconstruction techniques, modern approaches such as Neural Radiance Field (NeRF) and Gaussian Splatting (GS) excel in scene representation, rendering quality, and handling featureless regions. Among them, GS stands out for its efficiency, leveraging discrete anisotropic 3D Gaussians to represent radiance fields, unlike NeRF's continuous implicit model. This study introduces a GS-enabled digital twin method tailored for effective 3D damage visualization. The method's key contributions include: 1) utilizing GS-based 3D reconstruction to visualize 2D damage segmentation results while reducing segmentation errors; 2) developing a multi-scale reconstruction strategy to balance efficiency and damage detail; 3) enabling digital twin updates as damage evolves over time. Demonstrated on an open-source synthetic dataset for post-earthquake inspections, the proposed approach offers a promising solution for comprehensive 3D damage visualization in civil infrastructure digital twins.
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