Bidirectional Estimators for Light Transport

Most of the research on the global illumination problem in computer graphics has been concentrated on finite-element (radiosity) techniques. Monte Carlo methods are an intriguing alternative which are attractive for their ability to handle very general scene descriptions without the need for meshing. In this paper we study techniques for reducing the sampling noise inherent in pure Monte Carlo approaches to global illumination. Every light energy transport path from a light source to the eye can be generated in a number of different ways, according to how we partition the path into an initial portion traced from a light source, and a final portion traced from the eye. Each partitioning gives us a different unbiased estimator, but some partitionings give estimators with much lower variance than others. We give examples of this phenomenon and describe its significance. We also present work in progress on the problem of combining these multiple estimators to achieve near-optimal variance, with the goal of producing images with less noise for a given number of samples.

[1]  Paul S. Heckbert.  Adaptive radiosity textures for bidirectional ray tracing , 1990, SIGGRAPH.

[2]  Peter Shirley,et al.  A ray tracing method for illumination calculation in diffuse-specular scenes , 1990 .

[3]  Peter Shirley.  Time complexity of Monte Carlo radiosity , 1992, Comput. Graph..

[4]  Stephen Mann,et al.  Making radiosity usable: automatic preprocessing and meshing techniques for the generation of accurate radiosity solutions , 1991, SIGGRAPH.

[5]  Mark Watt,et al.  Light-water interaction using backward beam tracing , 1990, SIGGRAPH.

[6]  Sumanta N. Pattanaik,et al.  The Potential Equation and Importance in Illumination Computations , 1993, Comput. Graph. Forum.

[7]  David Salesin,et al.  An importance-driven radiosity algorithm , 1992, SIGGRAPH.

[8]  Sumanta N. Pattanaik,et al.  Efficient potential equation solutions for global illumination computation , 1993, Comput. Graph..

[9]  John Fitch,et al.  Course notes , 1975, SIGS.

[10]  Christian Bouville,et al.  Developments in Ray-Tracing , 1988, Advances in Computer Graphics.

[11]  Anne Lohrli.  Chapman and Hall , 1985 .

[12]  Christophe Schlick,et al.  A Progressive Ray-Tracing-based Radiosity with General Reflectance Functions , 1992 .

[13]  Holly E. Rushmeier,et al.  A progressive multi-pass method for global illumination , 1991, SIGGRAPH.

[14]  Stephen H. Westin,et al.  A global illumination solution for general reflectance distributions , 1991, SIGGRAPH.

[15]  Robert L. Cook,et al.  Distributed ray tracing , 1984, SIGGRAPH.

[16]  James Arvo,et al.  Particle transport and image synthesis , 1990, SIGGRAPH.

[17]  Michael F. Cohen,et al.  Radiosity and realistic image synthesis , 1993 .

[18]  James T. Kajiya,et al.  The rendering equation , 1986, SIGGRAPH.

[19]  J. Hammersley,et al.  Monte Carlo Methods , 1965 .

[20]  Dani Lischinski,et al.  Combining hierarchical radiosity and discontinuity meshing , 1993, SIGGRAPH.

[21]  T. DeRose,et al.  A Continuous Adjoint Formulation for Radiance Transport , 1993 .

[22]  J. Lewins.  Importance, the adjoint function : the physical basis of the variational and perturbation theory in transport and diffusion problems , 1965 .

[23]  James Arvo,et al.  Unbiased sampling techniques for image synthesis , 1991, SIGGRAPH.