Efficient BRDF importance sampling using a factored representation

Jason Lawrence, Szymon Rusinkiewicz, Ravi Ramamoorthi

Research output: Contribution to conferencePaperpeer-review

23 Scopus citations


High-quality Monte Carlo image synthesis requires the ability to importance sample realistic BRDF models. However, analytic sampling algorithms exist only for the Phong model and its derivatives such as Lafortune and Blinn-Phong. This paper demonstrates an importance sampling technique for a wide range of BRDFs, including complex analytic models such as Cook-Torrance and measured materials, which are being increasingly used for realistic image synthesis. Our approach is based on a compact factored representation of the BRDF that is optimized for sampling. We show that our algorithm consistently offers better efficiency than alternatives that involve fitting and sampling a Lafortune or Blinn-Phong lobe, and is more compact than sampling strategies based on tabulating the full BRDF. We are able to efficiently create images involving multiple measured and analytic BRDFs, under both complex direct lighting and global illumination.

Original languageEnglish (US)
Number of pages10
StatePublished - 2004
EventACM SIGGRAPH 2004, SIGGRAPH 2004 - Los Angeles, CA, United States
Duration: Aug 8 2004Aug 12 2004


Country/TerritoryUnited States
CityLos Angeles, CA

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction


  • BRDF
  • Global illumination
  • Importance sampling
  • Monte carlo integration
  • Ray tracing
  • Rendering


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