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This paper introduces ThermoField, a novel framework that integrates thermal scene reconstruction with thermophysical parameter estimation using differentiable heat-transfer simulation. By representing spatially varying properties as neural fields and constraining them with scene geometry and thermal observations, the method enables the joint reconstruction of geometry and estimation of thermal diffusivity in complex 3D environments. The results demonstrate that ThermoField can accurately predict thermal evolution under previously unseen conditions, marking a significant advancement in the interpretation of thermal imaging data for various applications.
ThermoField bridges the gap between thermal scene reconstruction and inverse heat-transfer analysis, enabling accurate predictions of thermal behavior in complex environments.
Inferring latent physical properties from sensory observations is a fundamental challenge in machine perception. Among available sensing modalities, thermal imaging is particularly promising because temperature evolution is directly governed by heat-transfer physics and therefore encodes information about underlying thermophysical properties of a scene. Recovering spatially resolved thermophysical properties from thermal observations could transform applications ranging from digital twins and infrastructure monitoring to robotics and scientific imaging. However, existing thermal scene reconstruction methods can recover temperature fields in complex 3D environments without identifying the thermophyiscal properties that govern thermal evolution, whereas inverse methods provide physically interpretable parameter estimation but typically rely on simplified geometries and controlled experimental conditions. Here we introduce ThermoField, a framework that unifies thermal scene reconstruction and thermophysical parameter estimation through differentiable heat-transfer simulation. The proposed framework represents these quantities as spatially varying neural fields and constrains them through scene geometry, governing heat-transfer physics, and temporal thermal observations. We demonstrate that ThermoField jointly reconstructs geometry, estimates spatially varying thermal diffusivity, and predicts thermal evolution under previously unseen environmental conditions. By integrating neural scene representations with differentiable heat-transfer solver, the framework enables physically interpretable parameter inference in complex 3D scenes. Our results establish a bridge between thermal scene reconstruction and inverse heat-transfer analysis, providing a unified approach for geometry reconstruction, thermophysical property estimation, and predictive thermal simulation from thermal observations.
