Reflectance Capture Using Univariate Sampling of BRDFs
Abstract
We propose the use of a light-weight setup consisting of a collocated camera and light source --- commonly found on mobile devices --- to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D "univariate" sampling of the 4-D BRDF. We show that a univariate sampling is sufficient to estimate parameters of commonly used analytical BRDF models. Subsequently, we use a dictionary-based reflectance prior to derive a robust technique for per-pixel normal and BRDF estimation. We demonstrate real-world shape and capture, and its application to material editing and classification, using real data acquired using a mobile phone.
Cite
Text
Hui et al. "Reflectance Capture Using Univariate Sampling of BRDFs." International Conference on Computer Vision, 2017. doi:10.1109/ICCV.2017.573Markdown
[Hui et al. "Reflectance Capture Using Univariate Sampling of BRDFs." International Conference on Computer Vision, 2017.](https://mlanthology.org/iccv/2017/hui2017iccv-reflectance/) doi:10.1109/ICCV.2017.573BibTeX
@inproceedings{hui2017iccv-reflectance,
title = {{Reflectance Capture Using Univariate Sampling of BRDFs}},
author = {Hui, Zhuo and Sunkavalli, Kalyan and Lee, Joon-Young and Hadap, Sunil and Wang, Jian and Sankaranarayanan, Aswin C.},
booktitle = {International Conference on Computer Vision},
year = {2017},
doi = {10.1109/ICCV.2017.573},
url = {https://mlanthology.org/iccv/2017/hui2017iccv-reflectance/}
}