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Stochastic Soft Shadow Mapping

Gabor Liktor     Stanislav Spassov     Gregor Mückl     Carsten Dachsbacher

Stochastic shadow maps sample the entire area of the light source and store the visibility information in a texture (left). Our novel algorithm reconstructs a smooth visibility function from the sparse data at real-time frame rates using multi-plane pre-filtering (middle) We closely approximate ray-traced soft shadows even for large area light sources (right).


In this paper, we extend the concept of pre-filtered shadow mapping to stochastic rasterization, enabling real-time rendering of soft shadows from planar area lights. Most existing soft shadow mapping methods lose important visibility information by relying on pinhole renderings from an area light source, providing plausible results only for small light sources. Since we sample the entire 4D shadow light field stochastically, we are able to closely approximate shadows of large area lights as well. In order to efficiently reconstruct smooth shadows from this sparse data, we exploit the analogy of soft shadow computation to rendering defocus blur, and introduce a multi-plane pre-filtering algorithm. We demonstrate how existing pre-filterable approximations of the visibility function, such as variance shadow mapping, can be extended to four dimensions within our framework.