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Improving the Efficiency of Monte Carlo Raytracing using Importance Sampling

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Author: Dave Jacobsen, Sr. Applications Engineer
Monday, May 18, 2020
Lambda Research Corp.

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.

File: Monte_Carlo_Raytracing_using_Importance_Sampling.pdf (1.35 MB)
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Imaging components & systemsoptical components & softwareSoftwareraytracingMonte Carlooptical designoptical analysisstray lightlidarimportance samplingautomotiveremote sensing
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