ThermoGS: Decoupling Physical Surface Attributes for Spatio-Temporal Thermal Field Emulation via 4D Gaussian Splatting

Thermal infrared (TIR) imaging is indispensable for all-weatherperception and energy management, yet existing 3D thermal modelingmethods remain primarily confined to static environmental snapshots.These approaches typically struggle to capture complex temporal tem-perature variations and lack the capacity to simulate scenes under unseenenvironmental conditions. To bridge this gap, we propose ThermoGS,a unified framework that couples 4D Gaussian Splatting (4DGS) withfundamental thermodynamic principles to achieve physically-consistentthermal field emulation. Unlike previous data-driven methods that di-rectly regress radiance, ThermoGS decouples intrinsic material proper-ties, including emissivity (e), convective heat transfer coefficient (h), vol-umetric heat capacity (c), and absorptivity (α)—from transient temper-ature states. By integrating Physics-Informed Neural Networks (PINNs),our framework ensures that the predicted spatiotemporal thermal distri-bution strictly adheres to energy balance equations, enabling generalizedsimulation across arbitrary viewpoints and time periods. Furthermore,we introduce the Thermo Dataset, a comprehensive, drone-captured col-lection featuring synchronized multi-modal data and diverse environmen-tal parameters. Extensive experiments demonstrate that ThermoGS sig-nificantly outperforms comparative methods in thermal simulation accu-racy, maintaining a predicted temperature error within 1 degree Celsius.The code is available at https://github.com/NPU-CVPG/ThermoGS.