用无人机扫描+物理模拟，实现滑坡的逼真重建与仿真。
UAV-Assisted Scan-to-Simulation for Landslides Using Physics-Informed Gaussian Splatting
- 通过无人机采集影像重建低各向异性3DGS场景
- 将表面模型体积分解用于滑坡物理仿真，效果优于传统网格方法
- 适用于城市安全评估与公众防灾教育，真实场景验证
滑坡监测与仿真在城市安全评估和灾害预防中至关重要。现有仿真流程多依赖数字高程模型和网格表示，虽适合几何分析，但视觉真实感不足，限制了交互应用、灾害传播与公众教育的效果。本文提出一种基于无人机的扫描到仿真框架，通过3DGS实现从照片级场景重建到物理驱动仿真的无缝衔接。流程包含四个阶段：(1) 无人机获取边坡影像；(2) 重建低各向异性3DGS场景表示；(3) 通过填充表面模型内部实现体积分解；(4) 与材料点法（MPM）集成进行滑坡仿真。在经历严重滑坡事件的香港真实场地进行验证，结果表明该方法兼具逼真视觉重建与有效仿真能力。
原文摘要 · Abstract (English)
Landslide monitoring and simulation play an important role in urban safety assessment and disaster prevention. Existing landslide simulation pipelines typically rely on digital elevation model and mesh-based representations, which are suitable for geometric analysis, but often lack visual realism. This limitation reduces their effectiveness in interactive applications, hazard communication, and public education. In this paper, we propose a UAV-based scan-to-simulation framework that bridges photorealistic scene capture and physics-based landslide simulation through 3DGS. Specifically, our pipeline includes four stages: (1) UAV-based acquisition of slope imagery, (2) reconstruction of a low-anisotropy 3DGS scene representation, (3) volumetric conversion of the target simulation region by filling the interior of the surface-based model, and (4) integration with the Material Point Method (MPM) for landslide simulation. We validate the proposed framework on a real landslide site in Hong Kong that experienced a severe landslide event. The results show that our method supports both realistic visual reconstruction and effective simulation.
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