Thermal Spread Functions (TSF): Physics-Guided Material Classification
Thermal Spread Functions (TSF): Physics-Guided Material Classification
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DOI：
10.1109/cvpr52729.2023.00164
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发表时间：
2023-04
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通讯作者：
Aniket Dashpute;Vishwanath Saragadam;Emma Alexander;F. Willomitzer;A. Katsaggelos;A. Veeraraghavan;O. Cossairt
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作者：
Aniket Dashpute;Vishwanath Saragadam;Emma Alexander;F. Willomitzer;A. Katsaggelos;A. Veeraraghavan;O. Cossairt
稳健且无损的材料分类是众多视觉应用中具有挑战性但至关重要的第一步。我们提出了一个物理指导的材料分类框架，依赖于对象的热性能。我们的关键观察是，物体的加热和冷却速率取决于材料的独特固有特性，即发射率和扩散率。我们利用这一观察结果，用低功率激光器在固定时间内轻轻加热场景中的物体，然后将其关闭，而热成像相机在加热和冷却过程中捕获测量结果。然后，我们采取这种空间和时间的“热扩散函数”（TSF），以解决逆热方程使用有限差分法，从而在空间上变化的扩散率和发射率的估计。然后，这些元组用于训练分类器，该分类器在每个空间像素处产生细粒度的材料标签。我们的方法非常简单，只需要一个小光源（低功率激光器）和一个热成像相机，即可产生稳健的分类结果，在16个类别中具有86%的准确度11代码：https://github.com/aniketdashpute/TSF。
Robust and non-destructive material classification is a challenging but crucial first-step in numerous vision applications. We propose a physics-guided material classification framework that relies on thermal properties of the object. Our key observation is that the rate of heating and cooling of an object depends on the unique intrinsic properties of the material, namely the emissivity and diffusivity. We leverage this observation by gently heating the objects in the scene with a low-power laser for a fixed duration and then turning it off, while a thermal camera captures measurements during the heating and cooling process. We then take this spatial and temporal “thermal spread function” (TSF) to solve an inverse heat equation using the finite-differences approach, resulting in a spatially varying estimate of diffusivity and emissivity. These tuples are then used to train a classifier that produces a fine-grained material label at each spatial pixel. Our approach is extremely simple requiring only a small light source (low power laser) and a thermal camera, and produces robust classification results with 86% accuracy over 16 classes11Code: https://github.com/aniketdashpute/TSF.