Physically based rendering needs numerical models from real measurements, or analytical models from material definitions, of the Bidirectional Reflectance Distribution Function (BRDF

In this paper, we present a framework based on a generic representation, which is able to handle most of the radiometric quantities required by global illumination softwares. A sparse representation in the wavelet space is built using the separation between the directional and the wavelength dependence of such radiometric quantities. Particularly, we show how to use this representation for spectral power distribution, spectral reflectance and phase function measurements modelling. Then, we explain how the representation is useful to perform spectral rendering. On one hand, it speeds up spectral path tracing by importance sampling to generate reflected directions and by avoiding expensive computations usually done on the fly. On the other hand, it allows efficient spectral photon mapping, both in terms of memory and speed. We also show how complex light emission from real luminaires can be efficiently sampled to emit photons with our numerical model.

The Bi-directional Reflectance Distribution Function (BRDF) is a complex function characterizing the reflection of light on a surface. It depends on five variables: four angles (lighting direction and observer direction) and the wavelength. A complete measurement campaign generates a large data set difficult to model. One way to proceed is to fit an analytical model on this data set. A numerical optimization technique, like simplex, allows to retrieve the best parameters of the model by minimizing the error with regard to measurements. Most of the analytical models obtain poor results for specular surfaces, and no wavelength dependent model actually exists.These reasons lead us to choose a numerical approach and particularly wavelets. This paper shows how wavelets can be used to provide an efficient BRDF model. Results of modeling are presented over a large collection of measurement data sets. At fixed wavelength, wavelet model has pretty good results, comparable to the best analytical models for diffuse surfaces, and much better for specular surfaces. The global relative error is lower than 5% with a compression ratio better than 90%. For spectral data sets, the wavelet model also presents very interesting performances with compression ratios greater than 95% and error lower than 2%.