Efficient shadow map filtering

Shadows provide the human visual system with important cues to sense spatial relationships in the environment we live in. As such they are an indispensable part of realistic computer-generated imagery. Unfortunately, visibility determination is computationally expensive. Image-based simplifications to the problem such as Shadow Maps perform well with increased scene complexity but produce artifacts both in the spatial and temporal domain because they lack efficient filtering support. This dissertation presents novel real-time shadow algorithms to enable efficient filtering of Shadow Maps in order to increase the image quality and overall coherence characteristics. This is achieved by expressing the shadow test as a sum of products where the parameters of the shadow test are separated from each other. Ordinary Shadow Maps are then subject to a transformation into new so called 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 used to reconstruct anti-aliased as well as physically plausible all-frequency shadows.