Tuesday, November 29, 2011

Spotted: Sample distribution shadow maps

New soft shadow technique

Sample distribution shadow maps

Andrew Lauritzen, Marco Salvi, Aaron Lefohn

This paper introduces Sample Distribution Shadow Maps (SDSMs), a new algorithm for hard and soft-edged shadows that greatly reduces undersampling, oversampling, and geometric aliasing errors compared to other shadow map techniques. SDSMs fall into the space between scene-dependent, variable-performance shadow algorithms and scene-independent, fixed-performance shadow algorithms. They provide a fully automated solution to shadow map aliasing by optimizing the placement and size of a fixed number of Z-partitions using the distribution of the light space samples required by the current frame. SDSMs build on the advantages of current state of the art techniques, including predictable performance and constant memory usage, while removing tedious and ultimately suboptimal parameter tuning.

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