BRDF Models for Accurate and Efficient Rendering of Glossy Surfaces
ACM Transactions on Graphics, Volume 31, Issue 1, January 2012
Joakim Löw and Joel Kronander and Anders Ynnerman and Jonas Unger
  Media and Information Technology, Linköping University, Sweden


Figure: A comparison between the new smooth surface BRDF model presented in this paper and the Cook-Torrance model. Left sphere rendering using the measured BRDF data. Middle and right show close-ups of renderings using the new BRDF model and the Cook-Torrance model, respectively.

This paper presents two new parametric models of the Bidirectional Reflectance Distribution Function (BRDF), one inspired by the Rayleigh-Rice theory for light scattering from optically smooth surfaces, and one inspired by micro-facet theory. The models represent scattering from a wide range of glossy surface types with high accuracy. In particular, they enable representation of types of surface scattering which previous parametric models have had trouble modelling accurately. In a study of the scattering behaviour of measured reflectance data, we investigate what key properties are needed for a model to accurately represent scattering from glossy surfaces. We investigate different parametrizations and how well they match the behaviour of measured BRDFs. We also examine the scattering curves which are represented in parametric models by different distribution functions. Based on the insights gained from the study, the new models are designed to provide accurate fittings to the measured data. Importance sampling schemes are developed for the new models, enabling direct use in existing production pipelines. In the resulting renderings we show that the visual quality achieved by the models matches that of the measured data.
Paper: BRDF Models for Accurate and Efficient Rendering of Glossy Surfaces (.pdf)
We would like to thank the anonymous reviewers for their constructive comments. We gratefully acknowledge Stefan Gustavson for invaluable discussions and feedback, and Matthew Cooper for proofreading the manuscript. This work was funded through the Swedish Foundation for Strategic Research through the strategic research centre MOVIII grant A3:05:193, the Swedish Knowledge Foundation grant 2009/0091, Forskning och Framtid grant ITN 2009-00116, the Swedish Research Council through the Linnaeus Center for Control, Autonomy, and Decision-making in Complex Systems (CADICS), and the Excellence Center at Linköping and Lund in Information Technology (ELLIIT).


Jonas Unger 2019