  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.
