[1] Lars Schjøth, Ole Fogh Olsen, and Jon Sporring. Diffusion based photon mapping. In First International conference on Computer Graphics - Theory and Applications - GRAPP 2006, pages 168-175, Setúbal, Portugal, February 25-28 2006.
[ bib ]
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 diffusion based photon mapping.
Keywords: Ray-tracing, global illumination, photon mapping, caustics, density estimation, diffusion filtering
[2] Lars Schjøth, Ole Fogh Olsen, and Jon Sporring. Advances in Computer Graphics and Computer Vision, volume 4 of Communications in Computer and Information Science, pages 109-122. Springer, Berlin, Germany, 2007.
[ bib ]
Keywords: Ray-tracing, global illumination, photon mapping, caustics, density estimation, diffusion filtering
[3] Lars Schjøth, Jeppe R. Frisvad, Kenny Erleben, and Jon Sporring. Photon differentials. In Proceedings of GRAPHITE 2007, December 2007.
[ bib ]
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.
Keywords: Ray tracing, global illumination, photon mapping, caustics, ray differentials
[4] Lars Schjøth, Ole Fogh Olsen, and Jon Sporring. Diffusion based photon mapping. Computer Graphics Forum, 27(8):2114-2127, December 2008.
[ bib ]
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.

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