Data-Driven Enhancement of SVBRDF Reflectance Data

In proceedings of Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Volume 1: GRAPP, 2018
 

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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Bibtex

@INPROCEEDINGS{steinhausen2018enhancement,
     author = {Steinhausen, Heinz Christian and den Brok, Dennis and Merzbach, Sebastian and Weinmann, Michael and
               Klein, Reinhard},
      title = {Data-Driven Enhancement of SVBRDF Reflectance Data},
    journal = {Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer
               Graphics Theory and Applications, Volume 1: GRAPP},
  booktitle = {Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer
               Graphics Theory and Applications, Volume 1: GRAPP},
       year = {2018},
   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.}
}