You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
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%.