Global Illumination Using Local Linear Density Estimation

Authors

• Bruce Walter 
• Philip Hubbard 
• Peter Shirley 
• Donald P. Greenberg 

Abstract

This paper presents the density estimation framework for generating view-independent global illumination solutions.  It works by probabilisticly simulating the light flow in an environment with light particles that trace random walks originating at luminaires and then using statistical density estimation techniques to reconstruct the lighting on each surface.  By splitting the computation into separate transport and reconstruction stages, we gain many advantages including reduced memory usage, the ability to simulate non-diffuse transport, and natural parallelism.

This paper also describes how several theoretical and practical difficulties can be overcome in implementing this framework.  Light sources that vary spectrally and directionally are integrated into a spectral particle tracer using non-uniform rejection.  A new local linear density estimation technique eliminates boundary bias and extends to arbitrary polygons.  A mesh decimation algorithm with perceptual calibration is introduced to simplify the Gouraud-shaded representation of the solution for interactive display.
 

Paper without color plates (pdf 800K)

Paper with color plates (pdf 4700K)

This version has different formatting and other small cosmetic differences from the version published in ACM Transactions on Graphics July 1997.  The figure and image quality is somewhat better than the
official online version which is available from the ACM digital library.

A page of color plates is also available.  These are somewhat reduced from the originals.