Material Classification Based on Training Data Synthesized Using a BTF Database

Michael Weinmann, Juergen Gall und Reinhard Klein
In proceedings of Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pages 156-171, Springer International Publishing, 2014
 

Abstract

To cope with the richness in appearance variation found in real-world data under natural illumination, we propose to synthesize training data capturing these variations for material classification. Using synthetic training data created from separately acquired material and illumination characteristics allows to overcome the problems of existing material databases which only include a tiny fraction of the possible real-world conditions under controlled laboratory environments. However, it is essential to utilize a representation for material appearance which preserves fine details in the reflectance behavior of the digitized materials. As BRDFs are not sufficient for many materials due to the lack of modeling mesoscopic effects, we present a high-quality BTF database with 22,801 densely measured view-light configurations including surface geometry measurements for each of the 84 measured material samples. This representation is used to generate a database of synthesized images depicting the materials under different view-light conditions with their characteristic surface geometry using image-based lighting to simulate the complexity of real-world scenarios. We demonstrate that our synthesized data allows classifying materials under complex real-world scenarios.

Bilder

Bibtex

@INPROCEEDINGS{weinmann-2014-materialclassification,
     author = {Weinmann, Michael and Gall, Juergen and Klein, Reinhard},
      pages = {156--171},
      title = {Material Classification Based on Training Data Synthesized Using a BTF Database},
  booktitle = {Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014,
               Proceedings, Part III},
       year = {2014},
  publisher = {Springer International Publishing},
   abstract = {To cope with the richness in appearance variation found in real-world data under natural
               illumination, we propose to synthesize training data capturing these variations for material
               classification. Using synthetic training data created from separately acquired material and
               illumination characteristics allows to overcome the problems of existing material databases which
               only include a tiny fraction of the possible real-world conditions under controlled laboratory
               environments. However, it is essential to utilize a representation for material appearance which
               preserves fine details in the reflectance behavior of the digitized materials. As BRDFs are not
               sufficient for many materials due to the lack of modeling mesoscopic effects, we present a
               high-quality BTF database with 22,801 densely measured view-light configurations including surface
               geometry measurements for each of the 84 measured material samples. This representation is used to
               generate a database of synthesized images depicting the materials under different view-light
               conditions with their characteristic surface geometry using image-based lighting to simulate the
               complexity of real-world scenarios. We demonstrate that our synthesized data allows classifying
               materials under complex real-world scenarios.}
}