  In this paper, we present a technique for estimating the geometry and
reflectance of objects using only a camera, flashlight, and optionally a
tripod. We propose a simple data capture technique in which the user goes
around the object, illuminating it with a flashlight and capturing only a few
images. Our main technical contribution is the introduction of a recursive
neural architecture, which can predict geometry and reflectance at 2^{k}*2^{k}
resolution given an input image at 2^{k}*2^{k} and estimated geometry and
reflectance from the previous step at 2^{k-1}*2^{k-1}. This recursive
architecture, termed RecNet, is trained with 256x256 resolution but can easily
operate on 1024x1024 images during inference. We show that our method produces
more accurate surface normal and albedo, especially in regions of specular
highlights and cast shadows, compared to previous approaches, given three or
fewer input images. For the video and code, please visit the project website
http://dlichy.github.io/ShapeAndMaterialAtHome/.
