Thermal imaging, as a promising approach for scalable and robust scene perception, is invaluable for many applications in various fields, such as architecture and building physics. Despite many recent works having demonstrated their capability to incorporate thermal images into radiance field methods, they typically do not explicitly model how radiation interacts and reflects within the scene before reaching the camera, which is essential for inferring thermal physics and properties of objects in a scene. Using Gaussian primitives as the scene representation, our method estimates surface temperature and material properties to generate infrared renderings that closely match the input images. Taking inspirations from radiosity and hemicube rasterization, our method decomposes the outgoing radiation from each Gaussian primitive into two parts: self-emission and reflection originating from other primitives and the environment. This formulation allows us to simulate radiation under novel heating conditions and to find the best-fit temperature and material parameters given thermal images. The method is verified using both synthetic and real capture datasets.