Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations
The paper introduces ThermoField, a framework that unifies thermal scene reconstruction and thermophysical parameter estimation via differentiable heat-transfer simulation. It uses neural fields to represent spatially varying properties like thermal diffusivity, constrained by scene geometry and physics, enabling joint reconstruction of geometry, estimation of diffusivity, and prediction of thermal evolution under unseen conditions.
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[Submitted on 8 Jul 2026]
Title:Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations
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Abstract: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.
Comments: 31 pages, In submission
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.07962 [cs.CV]
(or arXiv:2607.07962v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.07962
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chenghao Xu [view email] [v1] Wed, 8 Jul 2026 22:34:24 UTC (6,519 KB)
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