Paper Info
Title
Deep Reflectance Scanning: Recovering Spatially-Varying Material Appearance From A Flash-Lit Video Sequence
Abstract
In this paper we present a novel method for recovering high-resolution spatially-varying isotropic surface reflectance of a planar exemplar from a flash-lit close-up video sequence captured with a regular hand-held mobile phone. We do not require careful calibration of the camera and lighting parameters, but instead compute a per-pixel flow map using a deep neural network to align the input video frames. For each video frame, we also extract the reflectance parameters, and warp the neural reflectance features directly using the per-pixel flow, and subsequently pool the warped features. Our method facilitates convenient hand-held acquisition of spatially-varying surface reflectance with commodity hardware by non-expert users. Furthermore, our method enables aggregation of reflectance features from surface points visible in only a subset of the captured video frames, enabling the creation of high-resolution reflectance maps that exceed the native camera resolution. We demonstrate and validate our method on a variety of synthetic and real-world spatially-varying materials.
Year
DOI
Venue
2021
10.1111/cgf.14387
COMPUTER GRAPHICS FORUM
Keywords
DocType
Volume
SVBRDF, hand-held capture, automatic alignment
Journal
40
Issue
ISSN
Citations 
6
0167-7055
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wenjie Ye100.34
Yue Dong242825.42
Pieter Peers3110955.34
Baining Guo43970194.91