WildFireGS：基于物理的大规模语义增强高斯溅射森林场景野火模拟
WildFireGS: Physics-Based Wildfire Simulation in Large-Scale Semantics-Enriched Gaussian Splatting Forest Scenes
浏览论文内容
中文总结 AI 辅助
WildFireGS是直接在大规模语义增强3D高斯溅射森林重建结果上运行的基于物理的野火模拟框架，可实现真实环境下的野火模拟，经实验验证其行为物理一致。
中文摘要 AI 辅助
气候驱动的环境变化导致野火事件的频率和严重程度增加，因此准确的模拟与预测对于有效的风险缓解和景观管理至关重要。尽管近期基于物理的野火模型通过显式模拟燃烧、热传递和燃料动态实现了高真实度，但它们大多仍局限于合成环境，这类环境具备完整且理想化的森林结构知识，限制了其在航拍图像获取的真实环境中的适用性。为了提供一条直接从观测数据构建真实世界野火数字孪生的途径，我们提出了WildFireGS，这是一个直接在大规模语义增强的3D高斯溅射森林重建结果上运行的基于物理的野火模拟框架。我们的方法通过为高斯基元补充编码植被类型和燃料特性的语义与材料属性，将基于学习的场景重建与环境模拟相连接。我们引入了一种基于粒子的燃烧模型，该模型可直接在高斯表示上运行，模拟复杂森林结构上的点火、热传递、燃烧和火焰传播。这使得能够在重建的真实环境上直接进行基于物理的火行为模拟，无需转换为显式网格或体积网格。我们通过一种作为能量汇过程的雨驱动冷却机制展示了WildFireGS的模块化，以真实地模拟火灾控制。在合成场景和真实航拍森林捕获结果上的评估表明，WildFireGS能产生物理上一致的野火行为，再现了与植被密度、风速和地形坡度相关的传播缩放等特征动态。此外，我们通过新型防火带实验和生物量损失估算验证了我们的模型。
英文摘要
Climate-driven environmental change is driving an increase in both the frequency and severity of wildfire events, making accurate simulation and prediction critical for effective risk mitigation and landscape management. While recent physics-based wildfire models achieve high realism by explicitly simulating combustion, heat transfer, and fuel dynamics, they remain largely restricted to synthetic environments with complete and idealized knowledge of forest structure, limiting their applicability to real-world environments captured via aerial imagery. To provide a pathway toward real-world wildfire digital twins derived directly from observational data, we present WildFireGS, a physics-based wildfire simulation framework operating directly on large-scale, semantics-enriched 3D Gaussian Splatting forest reconstructions. Our approach bridges learning-based scene reconstruction and environmental simulation by augmenting Gaussian primitives with semantics and material properties that encode vegetation type and fuel characteristics. We introduce a particle-based combustion model that operates natively on Gaussian representations, simulating ignition, heat transfer, combustion, and flame propagation across complex forest structures. This enables direct physics-based simulation of fire behavior on reconstructed real-world environments, without requiring conversion to explicit meshes or volumetric grids. We demonstrate the modularity of WildFireGS through a rain-driven cooling mechanism in terms of an energy-sink process to realistically model fire containment. Evaluations on synthetic scenes and real aerial forest captures show physically consistent wildfire behavior, reproducing characteristic dynamics including propagation scaling with vegetation density, wind velocity, and terrain slope. In addition, we validate our model through novel firebreak experiments and biomass loss estimation.
