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WHITE PAPER: Improving the Efficiency of Monte Carlo Raytracing using Importance Sampling (5/21/2020)

WHITE PAPER: Improving the Efficiency of Monte Carlo Raytracing using Importance Sampling

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Thursday, May 21, 2020

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Improving the Efficiency of Monte Carlo Raytracing using Importance Sampling

Monte Carlo raytracing algorithms have long been used in optical design and analysis software. A limitation of the Monte Carlo method is that low probability events or ray paths may be undersampled. In this paper we will look at using Importance Sampling to improve the results in undersampled ray paths.

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