Material Recognition for Efficient Acquisition of Geometry and Reflectance

  • Michael WeinmannEmail author
  • Reinhard Klein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8927)


Typically, 3D geometry acquisition and reflectance acquisition techniques strongly rely on some basic assumptions about the surface reflectance behavior of the sample to be measured. Methods are tailored e.g. to Lambertian reflectance, mirroring reflectance, smooth and homogeneous surfaces or surfaces exhibiting mesoscopic effects. In this paper, we analyze whether multi-view material recognition can be performed robust enough to guide a subsequent acquisition process by reliably recognizing a certain material in a database with its respective annotation regarding the reconstruction methods to be chosen. This allows selecting the appropriate geometry/reflectance reconstruction approaches and, hence, increasing the efficiency of the acquisition process. In particular, we demonstrate that considering only a few view-light configurations is sufficient for obtaining high recognition scores.


Material recognition Reflectance Set-based classification 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computer Science IIUniversity of BonnBonnGermany

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