A novel framework for predicting 3D scene infrared radiation characteristics through AI-enhanced thermodynamic modeling

Abstract

In the evolving landscape of modern warfare, virtual battlefield construction technology significantly impacts fields such as weapon development and tactical formulation. Currently, 3D infrared scene simulation face challenges like high computational resource demands, difficulty in depicting interaction mechanisms between dynamic targets and environments, and a lack of reliable validation for algorithms. In this paper, we propose a three-dimensional infrared characteristics prediction framework (3DICPF) that integrates data-driven and theoretical algorithms, capable of rapidly constructing realistic 3D infrared scenes based on limited input information. Considering the extremely high cost of data acquisition, a step-by-step construction approach is adopted to reduce dependence on data. For the aligned image inputs, a 3D shape reconstruction network is utilized to obtain the mesh calculation domain of the target. Subsequently, 3DICPF employs two temperature field reconstruction networks to obtain the temperature distribution of the target. Finally, by using Monte Carlo ray tracing techniques, 3DICPF calculates the radiative interactions between the target and the background, achieving real-time infrared scene rendering. Moreover, confirmatory experiments with a shape-similar tank replica were conducted, demonstrating that 3DICPF could simulate infrared scene images with an average similarity above 88.13%. Overall, our research presents a significant advancement in virtual warfare technology, offering an efficient tool for enhancing strategic military planning and decision-making.