# imageConferences.bib

@INPROCEEDINGS{schjoth.olsen.ea:06,
AUTHOR = {Lars Schj{\o}th and Ole Fogh Olsen and Jon Sporring},
TITLE = {Diffusion Based Photon Mapping},
BOOKTITLE = {First International conference on Computer Graphics --
Theory and Applications -- {GRAPP} 2006},
EDITORS = {Jos\'{e} Braz and Joaquim Jorge and Miguel Dias and
YEAR = {2006},
MONTH = FEB # {~25--28},
PAGES = {168--175},
ABSTRACT = {Density estimation employed in multi-pass global illumination algorithms give cause to a trade-off problem between bias and noise. The problem is seen most evident as blurring of strong illumination features. In particular this blurring erodes fine structures and sharp lines prominent in caustics. To address this problem we introduce a novel photon mapping algorithm based on nonlinear anisotropic diffusion. Our algorithm adapts according to the structure of the photon map such that smoothing occurs along edges and structures and not across. In this way we preserve the important illumination features, while eliminating noise. We call our method \emph{diffusion based photon mapping}.},
KEYWORDS = {Ray-tracing, global illumination, photon mapping, caustics, density estimation, diffusion filtering}
}


@INPROCEEDINGS{schjoth.frisvad.erleben.sporring.07,
AUTHOR = {Lars Schj{\o}th and Jeppe R. Frisvad and Kenny Erleben and
Jon Sporring},
TITLE = {Photon Differentials},
YEAR = {2007},
MONTH = {December},
KEYWORDS = {Ray tracing, global illumination, photon mapping,
caustics, ray differentials},
BOOKTITLE = {Proceedings of {GRAPHITE} 2007},
ABSTRACT = {A number of popular global illumination algorithms use
density estimation to approximate indirect illumination.
The density estimate is performed on finite points -
particles - generated by a stochastic sampling of the
scene. In the course of the sampling, particles,
representing light, are stochastically emitted from the
light sources and reflected around the scene. The sampling
induces noise, which in turn is handled by the density
estimate during the illumination reconstruction.
Unfortunately, this noise reduction imposes a systematic
error (bias), which is seen as a blurring of prominent
illumination features. This is often not desirable as these
may lose clarity or vanish altogether. We present an
accurate method for reconstruction of indirect illumination
with photon mapping. Instead of reconstructing illumination
using classic density estimation on finite points, we use
the correlation of light footprints, created by using Ray
Differentials during the light pass. This procedure gives a
high illumination accuracy, improving the trade-off between
bias and variance considerable as compared to traditional
particle tracing algorithms. In this way we preserve
structures in indirect illumination.}
}


@INBOOK{schjoth.olsen.ea:07,
AUTHOR = {Lars Schj{\o}th and Ole Fogh Olsen and Jon Sporring},
TITLE = {Advances in Computer Graphics and Computer Vision},
BOOKTITLE = {Advances in Computer Graphics and Computer Vision},
SERIES = {Communications in Computer and Information Science},
EDITORS = {Jos\'{e} Braz and Alpesh Ranchordas and Helder Ara\'{u}jo and Joaquim Jorge},
PUBLISHER = {Springer},
YEAR = {2007},
VOLUME = {4},
PAGES = {109--122},
KEYWORDS = {Ray-tracing, global illumination, photon mapping, caustics, density estimation, diffusion filtering}
}


@ARTICLE{schj08,
AUTHOR = {Lars Schj{\o}th and Ole Fogh Olsen and Jon Sporring},
TITLE = {Diffusion Based Photon Mapping},
ABSTRACT = {Density estimation employed in multi-pass global illumination algorithms give cause to a trade-off problem between
bias and noise. The problem is seen most evident as blurring of strong illumination features. In particular
this blurring erodes fine structures and sharp lines prominent in caustics. To address this problem we introduce
a photon mapping algorithm based on nonlinear anisotropic diffusion. Our algorithm adapts according to the
structure of the photon map such that smoothing occurs along edges and structures and not across. In this way we
preserve important illumination features, while eliminating noise. We demonstrate the applicability of our algorithm
through a series of tests. In the tests we evaluate the visual and computational performance of our algorithm
comparing it to existing popular algorithms.},
JOURNAL = {Computer Graphics Forum},
VOLUME = {27},
NUMBER = {8},
YEAR = {2008},
MONTH = {December},
PAGES = {2114--2127}
}


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