A wavelet representation of reflectance functions

Analytical models of light reflection are in common use in computer graphics. However, models based on measured reflectance data promise increased realism by making it possible to simulate many more types of surfaces to a greater level of accuracy than with analytical models. They also require less expert knowledge about the illumination models and their parameters. There are a number of hurdles to using measured reflectance functions, however. The data sets are very large. A reflectance distribution function sampled at five degrees angular resolution, arguably sparse enough to miss highlights and other high frequency effects, can easily require over a million samples, which in turn amount to over four megabytes of data. These data then also require some form of interpolation and filtering to be used effectively. We examine issues of representation of measured reflectance distribution functions. In particular, we examine a wavelet basis representation of reflectance functions, and the algorithms required for efficient point-wise reconstruction of the BRDF. We show that the nonstandard wavelet decomposition leads to considerably more efficient algorithms than the standard wavelet decomposition. We also show that thresholding allows considerable improvement in running times, without unduly sacrificing image quality