Image Space Gathering

Abstract

Soft shadows, glossy reflections and depth of field are valuable effects for realistic rendering and are often computed using distribution ray tracing (DRT). These blurry effects often need not be accurate and are sometimes simulated by blurring an image with sharper effects, such as blurring hard shadows to simulate soft shadows. One of the most effective examples of such a blurring algorithm is percentage closer soft shadows (PCSS). That technique, however, does not naturally extend to shadows generated in image space, such as those computed by a ray tracer, nor does it extend to glossy reflections or depth of field. This limitation can be overcome by generalizing PCSS to be phrased in terms of a gather from image space textures implemented with cross bilateral filtering. This paper demonstrates a framework to create visually compelling and phenomenologically accurate approximations of DRT effects based on repeatedly gathering from bilaterally weighted image space texture samples. These gathering and filtering operations are well supported by modern parallel architectures, enabling this technique to run at interactive rates.