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Improved Stochastic Progressive Photon Mapping with Metropolis Sampling
Author(s) -
Chen Jiating,
Wang Bin,
Yong JunHai
Publication year - 2011
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/j.1467-8659.2011.01979.x
Subject(s) - global illumination , computer science , rendering (computer graphics) , radiance , robustness (evolution) , ray tracing (physics) , photon , estimator , algorithm , distributed ray tracing , computer vision , importance sampling , path tracing , sampling (signal processing) , artificial intelligence , optics , mathematics , monte carlo method , statistics , physics , biochemistry , chemistry , filter (signal processing) , gene
This paper presents an improvement to the stochastic progressive photon mapping (SPPM), a method for robustly simulating complex global illumination with distributed ray tracing effects. Normally, similar to photon mapping and other particle tracing algorithms, SPPM would become inefficient when the photons are poorly distributed. An inordinate amount of photons are required to reduce the error caused by noise and bias to acceptable levels. In order to optimize the distribution of photons, we propose an extension of SPPM with a Metropolis‐Hastings algorithm, effectively exploiting local coherence among the light paths that contribute to the rendered image. A well‐designed scalar contribution function is introduced as our Metropolis sampling strategy, targeting at specific parts of image areas with large error to improve the efficiency of the radiance estimator. Experimental results demonstrate that the new Metropolis sampling based approach maintains the robustness of the standard SPPM method, while significantly improving the rendering efficiency for a wide range of scenes with complex lighting.