We propose Neural Field Thermal Tomography (NeFTY), a differentiable physics framework for the quantitative 3D reconstruction of material properties from transient surface temperature measurements. While traditional thermography relies on pixel-wise 1D approximations that neglect lateral diffusion, and soft-constrained Physics-Informed Neural Networks (PINNs) often fail in transient diffusion scenarios due to gradient stiffness, NeFTY parameterizes the 3D diffusivity field as a continuous neural field optimized through a rigorous numerical solver. By leveraging a differentiable physics solver, our approach enforces thermodynamic laws as hard constraints while maintaining the memory efficiency required for high-resolution 3D tomography. Our discretize-then-optimize paradigm effectively mitigates the spectral bias and ill-posedness inherent in inverse heat conduction, enabling the recovery of subsurface defects at arbitrary scales. Experimental validation on synthetic data demonstrates that NeFTY significantly improves the accuracy of subsurface defect localization over baselines. Additional details at https://cab-lab-princeton.github.io/nefty/

翻译：我们提出神经场热层析成像（NeFTY），这是一种可微分物理框架，用于从瞬态表面温度测量中定量重建材料属性的三维图像。传统热成像技术依赖于忽略横向扩散的逐像素一维近似，而软约束的物理信息神经网络（PINNs）在瞬态扩散场景中常因梯度刚性而失效。NeFTY将三维扩散率场参数化为一个连续神经场，并通过严格的数值求解器进行优化。通过利用可微分物理求解器，我们的方法将热力学定律作为硬约束强制执行，同时保持了高分辨率三维层析成像所需的内存效率。我们“先离散后优化”的范式有效缓解了逆热传导中固有的谱偏差和不适定性，从而能够恢复任意尺度的亚表面缺陷。在合成数据上的实验验证表明，NeFTY在亚表面缺陷定位精度上显著优于基线方法。更多细节请访问 https://cab-lab-princeton.github.io/nefty/