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Filtered Stochastic Shadow Mapping Using a Layered Approach
Author(s) -
Andersson M.,
Hasselgren J.,
Munkberg J.,
AkenineMöller T.
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12664
Subject(s) - computer science , computer vision , shadow (psychology) , artificial intelligence , motion blur , computer graphics (images) , representation (politics) , graphics , motion (physics) , shadow mapping , set (abstract data type) , graphics processing unit , computer graphics , image (mathematics) , psychology , politics , political science , law , psychotherapist , programming language , operating system
Given a stochastic shadow map rendered with motion blur, our goal is to render an image from the eye with motion‐blurred shadows with as little noise as possible. We use a layered approach in the shadow map and reproject samples along the average motion vector, and then perform lookups in this representation. Our results include substantially improved shadow quality compared to previous work and a fast graphics processing unit (GPU) implementation. In addition, we devise a set of scenes that are designed to bring out and show problematic cases for motion‐blurred shadows. These scenes have difficult occlusion characteristics, and may be used in future research on this topic.