Logarithmic perspective shadow maps

We present a novel shadow map parameterization to reduce perspective aliasing artifacts for both point and directional light sources. We derive the aliasing error equations for both types of light sources in general position. Using these equations we compute tight bounds on the aliasing error. From these bounds we derive our shadow map parameterization, which is a simple combination of a perspective projection with a logarithmic transformation. We then extend existing algorithms to formulate three types of logarithmic perspective shadow maps (LogPSMs) and analyze the error for each type. Compared with competing algorithms, LogPSMs can produce significantly less aliasing error. Equivalently, for the same error as competing algorithms, LogPSMs can produce significant savings in storage and bandwidth. We demonstrate the benefit of LogPSMs for several models of varying complexity.

• Type: Journal
• Year: 2008
• Where: TOG
• Authors: Brandon Lloyd, Naga K. Govindaraju, Cory Quammen, Steven E. Molnar, Dinesh Manocha
• Keywords: Aliasing, Light Sources, Shadow Map Parameterization, TOG 2008