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Diffusion Based Photon Mapping



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.