Efficient Shadow Map Filtering
Shadows provide the human visual system with important cues to sense spatial relationshipsin the environment we live in. As such they are an indispensable partof realistic computer-generated imagery. Unfortunately, visibility determinationis computationally expensive. Image-based simplifications to the problem suchas Shadow Maps perform well with increased scene complexity but produce artifactsboth in the spatial and temporal domain because they lack efficient filteringsupport.This dissertation presents novel real-time shadow algorithms to enable efficientfiltering of Shadow Maps in order to increase the image quality and overallcoherence characteristics. This is achieved by expressing the shadow test as asum of products where the parameters of the shadow test are separated from eachother. Ordinary Shadow Maps are then subject to a transformation into new socalled basis-images which can, as opposed to Shadow Maps, be linearly filtered.The convolved basis images are equivalent to a pre-filtered shadow test and usedto reconstruct anti-aliased as well as physically plausible all-frequency shadows.