# Thermal Spread Functions (TSF): Physics-guided Material Classification

arXiv: https://arxiv.org/abs/2304.00696 · HTML: https://mlsanity.com/paper/2304.00696/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging](https://mlsanity.com/paper/2004.01160/) (2020)

## Built on by (most cited first, 10 of 10)

- [LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images](https://mlsanity.com/paper/2403.13171/) (2024)
- [Learning to See Inside Opaque Liquid Containers using Speckle Vibrometry](https://mlsanity.com/paper/2507.20757/) (2025)
- [VT-Intrinsic: Physics-Based Decomposition of Reflectance and Shading using a Single Visible-Thermal Image Pair](https://mlsanity.com/paper/2509.10388/) (2025)
- [Dual Band Thermal Videography: Separating Time-Varying Reflection and Emission Near Ambient Conditions](https://mlsanity.com/paper/2509.11334/) (2025)
- [Physics-Informed Modeling for Wood Thermal Analysis and Prediction](https://mlsanity.com/paper/2606.23402/) (2026)
- [Indoor Heat Estimation from a Single Visible-Light Panorama](https://mlsanity.com/paper/2502.06973/) (2025)
- [TDiff: Thermal Plug-And-Play Prior with Patch-Based Diffusion](https://mlsanity.com/paper/2510.06460/) (2025)
- [End-to-End Optimization of Polarimetric Measurement and Material Classifier](https://mlsanity.com/paper/2603.20519/) (2026)
- [Beyond Thermal Imaging: Inferring Thermophysical Properties from Time-Resolved Thermal Observations](https://mlsanity.com/paper/2607.07962/) (2026)
- [A Theory of Shape Reconstruction from Heat Conduction and Shading](https://mlsanity.com/paper/2610.04052/) (2026)

## Abstract (arXiv)

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 classes.