"On-site Example-based Material Appearance Digitization"
ACM SIGGRAPH 2018 Posters, August 2018
Abstract
We present a novel example-based material appearance modeling method for digital content creation. The proposed method requires a single HDR photograph of an exemplar object made of a desired material under known environmental illumination. While conventional methods for appearance modeling require the object shape to be known, our method does not require prior knowledge of the shape of the exemplar, nor does it require recovering the shape, which improves robustness as well as simplify on-site appearance acquisition by non-expert users.
Download
Supplementary Material
Related Publications
Bibtex
ACM SIGGRAPH 2018 Posters, August 2018
AbstractWe present a novel example-based material appearance modeling method for digital content creation. The proposed method requires a single HDR photograph of an exemplar object made of a desired material under known environmental illumination. While conventional methods for appearance modeling require the object shape to be known, our method does not require prior knowledge of the shape of the exemplar, nor does it require recovering the shape, which improves robustness as well as simplify on-site appearance acquisition by non-expert users.
Download
- Lin_SIGGRAPH2018_Poster.pdf (4.49Mb)
Supplementary Material
- Video: Lin_SIGGRAPH2018_Poster_video.mp4 (22.16Mb)
Related Publications
-
"On-Site Example-Based Material Appearance Acquisition",
Computer Graphics Forum, Volume 38, Issue 4,
July 2019,
@misc{Lin:2018:OEM:Poster,
author = {Lin, Yiming and Peers, Pieter and Ghosh, Abhijeet},
title = {On-site Example-based Material Appearance Digitization},
month = {August},
year = {2018},
howpublished = {ACM SIGGRAPH 2018 Posters},
doi = {https://doi.org/10.1145/3230744.3230805},
}
author = {Lin, Yiming and Peers, Pieter and Ghosh, Abhijeet},
title = {On-site Example-based Material Appearance Digitization},
month = {August},
year = {2018},
howpublished = {ACM SIGGRAPH 2018 Posters},
doi = {https://doi.org/10.1145/3230744.3230805},
}