Shape and Material Capture at Home

Note: We don't have the ability to review paper

PubDate: Apr 2021

Teams: University of Maryland, College Park 2University of Washington

Writers: Daniel Lichy, Jiaye Wu, Soumyadip Sengupta, David W. Jacobs

PDF: Shape and Material Capture at Home


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} resolution given an input image at 2


{k} and estimated geometry and reflectance from the previous step at 2


{k-1}. This recursive architecture, termed RecNet, is trained with 256×256 resolution but can easily operate on 1024×1024 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 this http URL.

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