Hair rendering: importance sampling of dual scattering approximation
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Rendering human hair models using individual hair fibers is a challenging task. It is challenging because of the extensive amount of hair fibers required to render a realistic model. Moreover hair fibers are very thin, resulting in noise in the renderings. To reduce this noise in a physically accurate way, more samples need to be taken. Marschner et al. (2003) proposed a physically based single fiber scattering model to render realistic human hair fibers. It is considered the founding work of current approach to physically based rendering human hair fibers. When rendering light colored hair, multiple fiber scattering is essential for the appearance of the hair color. Zinke et al. (2008) extended the work of Marschner by splitting multiple scattering up into two components: global multiple scattering and local multiple scattering. Global multiple scattering approximates the multiple scattering contribution, thereby reducing the rendering time considerably. Local multiple scattering resembles the Marschner model to keep the single fiber scattering characteristics. This approach is known as the dual-scattering method. Importance sampling is a widely used noise-reduction technique to speed up renderings. By applying importance sampling, samples are not taken randomly (as is the case for uniform sampling), but are sampled according to a probability density distribution that favors samples that contribute more to the output rendering. This reduces noise faster and thus reduces the amount of samples that are required to produce noise-free renderings. Both uniform and importance sampling should eventually converge to the same result, with importance sampling reaching it faster. d’Eon et al. (2013) proposed an importance sampling strategy that works particularly well for the Marschner model. This importance sampling strategy can also be applied to the dual scattering method. It is therefore very interesting to evaluate whether applying importance sampling to the dualscattering method reduces the noise as well. This forms the main goal of this thesis: to find out if multiple importance sampling applied to the dualscattering method leads to a significant reduction of noise in the rendered image. This report concludes by measurement and visual inspection that importance sampling for the dual-scattering method leads to a significant increase in rendering quality compared to uniform sampling. Especially up to 32 samples per pixel the noise is reduced substantially. Such an increase in quality is expected when rendering using a small amount of samples per pixel. It then becomes more important to sample high-contributing directions to reduce noise. This is exactly what multiple importance sampling is doing.