Lightcuts: a scalable approach to illumination

  • Authors:

  • Bruce Walter;Sebastian Fernandez;Adam Arbree;Kavita Bala;Michael Donikian;Donald P. Greenberg

  • Affiliations:

  • Program of Computer Graphics, Cornell University;Program of Computer Graphics, Cornell University;Program of Computer Graphics, Cornell University;Program of Computer Graphics, Cornell University;Program of Computer Graphics, Cornell University;Program of Computer Graphics, Cornell University

  • Venue:
  • ACM SIGGRAPH 2005 Papers
  • Year:
  • 2005

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Abstract

Lightcuts is a scalable framework for computing realistic illumination. It handles arbitrary geometry, non-diffuse materials, and illumination from a wide variety of sources including point lights, area lights, HDR environment maps, sun/sky models, and indirect illumination. At its core is a new algorithm for accurately approximating illumination from many point lights with a strongly sublinear cost. We show how a group of lights can be cheaply approximated while bounding the maximum approximation error. A binary light tree and perceptual metric are then used to adaptively partition the lights into groups to control the error vs. cost tradeoff.We also introduce reconstruction cuts that exploit spatial coherence to accelerate the generation of anti-aliased images with complex illumination. Results are demonstrated for five complex scenes and show that lightcuts can accurately approximate hundreds of thousands of point lights using only a few hundred shadow rays. Reconstruction cuts can reduce the number of shadow rays to tens.