Paper Info
Title
Sample distribution shadow maps
Abstract
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. We compare SDSMs to Parallel-Split Shadow Maps (PSSMs, a state of the art Z-partitioning scheme) and show that SDSMs produce higher quality shadows. Finally, we demonstrate that SDSMs outperform PSSMs in a large 2009 game scene at high resolutions, making them suitable for games and other interactive applications.
Year
DOI
Venue
2011
10.1145/1944745.1944761
interactive 3d graphics and games
Keywords
Field
DocType
current frame,higher quality shadow,shadows,z-partitioning,rendering,sdsms fall,art technique,shadow map technique,sample distribution shadow map,parallel-split shadow maps,soft-edged shadow,shadow maps,variable-performance shadow algorithm,fixed-performance shadow algorithm,cascaded shadow maps,shadow mapping,high resolution
Sampling distribution,Computer vision,Computer graphics (images),Oversampling,Computer science,Shadow algorithms,Undersampling,Shadow mapping,DirectX,Aliasing,Artificial intelligence,Rendering (computer graphics)
Conference
Citations 
PageRank 
References 
19
1.46
7
Authors
3
Name
Order
Citations
PageRank
Andrew Lauritzen11406.20
Marco Salvi2986.80
Aaron Lefohn328222.38