Methods for efficient sampling of arbitrary distributed volume densities
In recent years a number of techniques have been developed for rendering volume effects (haze, fog, smoke, clouds, etc.). SWuch techniques have been implemented for projective scanline renderers, ray-tracers and for radiosity. Roughly speaking, such a method depends on an illumination model which accounts fort the light-material interaction, together with a sampling strategy for reading the data of the density field. The illumination models proposed in the literature are quite complicated and require several time-consuming operations, such as exponential functions, roots and trigonometrical functions. Ray-tracing and radiosity evaluate the illumination model at every voxel of the density field. Since several hundred complicated calculations are necessary for each ray, such a rigorous evaluation is very time-consuming. On the other hand, methods proposed for scanline renderes do not account for arbitrary density distributions: The equations describing scattering and reflection of ligh t have been analytically solved along each ray within a volume. The purpose of this paper is not to propose a new illumination model, but to compare several methods for efficiently sampling arbitrary distributed data. We propose that several sampling strategies can be used to reduce the number of evaluations of the illumination calculations along a ray and, thus, reduce the rendering time needed. Such a method is well suited for scanline renderers but can be used with ray-tracers. We propose a Monte-Carlo method and an approximative method with user-adjustable accuracy to sample the volume data. Thus, a trade-off between computing time, sampling accuracy and picture quality exists.