Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations

2026-07-08

AI Summary

This work addresses the key challenge of recovering spatially varying thermophysical properties of complex 3D scenes from thermal imaging observations, a capability critical for applications such as digital twins and infrastructure monitoring. To this end, the authors propose ThermoField, a novel framework that, for the first time, integrates neural scene representations with a differentiable heat conduction solver. By modeling both geometry and spatially varying thermal diffusivity through neural fields and leveraging time-resolved thermal observations, the method enables physics-guided joint optimization. ThermoField unifies high-fidelity geometric reconstruction, accurate estimation of thermophysical parameters, and predictive simulation of thermal evolution under novel environmental conditions. Extensive experiments on both real-world and synthetic datasets demonstrate its effectiveness and strong generalization across diverse environments.

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.

Problem

• thermophysical properties
• thermal imaging
• inverse heat transfer
• spatially varying parameters
• 3D scene reconstruction

Innovation

• neural fields
• differentiable simulation
• thermophysical property estimation
• thermal scene reconstruction
• inverse heat transfer