Passive Reflectometry

  • Fabiano Romeiro
  • Yuriy Vasilyev
  • Todd Zickler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)


Different materials reflect light in different ways, so reflectance is a useful surface descriptor. Existing systems for measuring reflectance are cumbersome, however, and although the process can be streamlined using cameras, projectors and clever catadioptrics, it generally requires complex infrastructure. In this paper we propose a simpler method for inferring reflectance from images, one that eliminates the need for active lighting and exploits natural illumination instead. The method’s distinguishing property is its ability to handle a broad class of isotropic reflectance functions, including those that are neither radially-symmetric nor well-represented by low-parameter reflectance models. The key to the approach is a bi-variate representation of isotropic reflectance that enables a tractable inference algorithm while maintaining generality. The resulting method requires only a camera, a light probe, and as little as one HDR image of a known, curved, homogeneous surface.


Input Image Synthetic Image Bilateral Symmetry Grazing Angle Light Probe 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Fabiano Romeiro
    • 1
  • Yuriy Vasilyev
    • 1
  • Todd Zickler
    • 1
  1. 1.School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA

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