Exploiting visibility correlation in direct illumination

The visibility function in direct illumination describes the binary visibility over a light source, e.g., an environment map. Intuitively, the visibility is often strongly correlated between nearby locations in time and space, but exploiting this correlation without introducing noticeable errors is a hard problem. In this paper, we first study the statistical characteristics of the visibility function. Then, we propose a robust and unbiased method for using estimated visibility information to improve the quality of Monte Carlo evaluation of direct illumination. Our method is based on the theory of control variates, and it can be used on top of existing state-of-the-art schemes
for importance sampling. The visibility estimation is obtained by sparsely sampling and caching the 4D visibility
field in a compact bitwise representation. In addition to Monte Carlo rendering, the stored visibility information
can be used in a number of other applications, for example, ambient occlusion and lighting design.