Title: ETGS: Explicit Thermodynamics Gaussian Splatting for Dynamic Thermal Reconstruction
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: 3D Reconstruction; Thermal Reconstruction；Explicit Thermodynamics；

Abstract: We propose a method for reconstructing dynamic thermal scenes based on explicit thermodynamics. Unlike some recent approaches rely on implicit neural representations, we introduce physically meaningful thermal variables directly into Gaussian representation, yielding a faithful thermodynamic model. From this model we derive closed-form solutions to ordinary differential equations (ODEs) at any instant in time, enabling efficient and numerically stable rendering under irregular sampling and out-of-order observations, while naturally satisfying thermal priors. We construct the Rapid Heat Dynamics (RHD) dataset, specifically designed for thermal scenes with rapidly evolving temperatures. RHD covers a variety of typical thermodynamic processes, including warming, cooling, heating, and heat transfer, and provides millisecond-accurate timestamps and pixel-aligned RGB-IR multimodal observations. Experimental results demonstrate that our method can reliably reconstruct thermal scenes with rapidly evolving temperatures, with training and rendering efficiency comparable to that of static 3DGS, and significantly outperforms state-of-the-art baselines across various metrics. Code and dataset will be released.