ETGS: Explicit Thermodynamics Gaussian Splatting for Dynamic Thermal Reconstruction

Zhongwen Wang, Han Ling, Weihao Zhang, Yinghui Sun, Quansen Sun

Keywords: 3D Reconstruction; Thermal Reconstruction; Explicit Thermodynamics

Abstract: We propose ETGS, a method for reconstructing dynamic thermal scenes by embedding explicit thermodynamic modeling into 3D Gaussian Splatting. Each Gaussian is equipped with physically interpretable thermal parameters, and its thermodynamics evolution is described by a first-order heat-transfer ODE with an analytical closed-form solution. This formulation avoids numerical integration, enables efficient rendering at arbitrary timestamps, and naturally handles irregular sampling and out-of-order observations. We also introduce the Rapid Heat Dynamics (RHD) dataset, which provides millisecond-aligned RGB–IR image pairs covering typical thermal processes such as cooling, warming, heating, and heat transfer. Experiments on RHD show that ETGS captures rapid thermal dynamics more accurately than existing static and dynamic baselines, while maintaining training and rendering efficiency close to that of static 3DGS. Code and dataset are available at https://github.com/jankin-wang/ETGS.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 11274