Data-driven Enhancement of SVBRDF Reflectance Data

Heinz Christian Steinhausen, Dennis den Brok, Sebastian Merzbach, Michael Weinmann, Reinhard Klein

Abstract

Analytical SVBRDF representations are widely used to represent spatially varying material appearance depending on view and light configurations. State-of-the-art industry-grade SVBRDF acquisition devices allow the acquisition within several minutes. For many materials with a surface reflectance behavior exhibiting complex effects of light exchange such as inter-reflections, self-occlusions or local subsurface scattering, SVBRDFs cannot accurately capture material appearance. We therefore propose a method to transform SVBRDF acquisition devices to full BTF acquisition devices. To this end, we use data-driven linear models obtained from a database of BTFs captured with a traditional BTF acquisition device in order to reconstruct high-resolution BTFs from the SVBRDF acquisition devices’ sparse measurements. We deal with the high degree of sparsity using Tikhonov regularization. In our evaluation, we validate our approach on several materials and show that BTF-like material appearance can be generated from SVBRDF measurements in the range of several minutes.

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Paper Citation


in Harvard Style

Steinhausen H., den Brok D., Merzbach S., Weinmann M. and Klein R. (2018). Data-driven Enhancement of SVBRDF Reflectance Data.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, ISBN 978-989-758-287-5, pages 273-280. DOI: 10.5220/0006628602730280


in Bibtex Style

@conference{grapp18,
author={Heinz Christian Steinhausen and Dennis den Brok and Sebastian Merzbach and Michael Weinmann and Reinhard Klein},
title={Data-driven Enhancement of SVBRDF Reflectance Data},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,},
year={2018},
pages={273-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006628602730280},
isbn={978-989-758-287-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,
TI - Data-driven Enhancement of SVBRDF Reflectance Data
SN - 978-989-758-287-5
AU - Steinhausen H.
AU - den Brok D.
AU - Merzbach S.
AU - Weinmann M.
AU - Klein R.
PY - 2018
SP - 273
EP - 280
DO - 10.5220/0006628602730280