"Compact Representation of Reflectance Fields using Clustered Sparse Residual Factorization"
Institute for Creative Technologies, USC, ICT-TR-05-2008, May 2009
Abstract
We present a novel compression method for fixed viewpoint reflectance fields, captured for example by a Light Stage. Our compressed representation consists of a global approximation that exploits the similarities between the reflectance functions of different pixels, and a local approximation that encodes the per-pixel residual with the global approximation. Key to our method is a clustered sparse residual factorization. This sparse residual factorization ensures that the per-pixel residual matrix is as sparse as possible, enabling a compact local approximation. Finally, we demonstrate that the presented compact representation is well suited for high-quality real-time rendering.
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Bibtex
Institute for Creative Technologies, USC, ICT-TR-05-2008, May 2009
AbstractWe present a novel compression method for fixed viewpoint reflectance fields, captured for example by a Light Stage. Our compressed representation consists of a global approximation that exploits the similarities between the reflectance functions of different pixels, and a local approximation that encodes the per-pixel residual with the global approximation. Key to our method is a clustered sparse residual factorization. This sparse residual factorization ensures that the per-pixel residual matrix is as sparse as possible, enabling a compact local approximation. Finally, we demonstrate that the presented compact representation is well suited for high-quality real-time rendering.
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Bibtex
@techreport{Yamada:2009:CRR,
author = {Yamada, Hideshi and Peers, Pieter and Debevec, Paul},
title = {Compact Representation of Reflectance Fields using Clustered Sparse Residual Factorization},
month = {May},
year = {2009},
institution = {Institute for Creative Technologies, USC},
number = {ICT-TR-05-2008},
}
author = {Yamada, Hideshi and Peers, Pieter and Debevec, Paul},
title = {Compact Representation of Reflectance Fields using Clustered Sparse Residual Factorization},
month = {May},
year = {2009},
institution = {Institute for Creative Technologies, USC},
number = {ICT-TR-05-2008},
}