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- W2736701250 abstract "Traditional Monte Carlo (MC) integration methods use point samples to numerically approximate the underlying integral. This approximation introduces variance in the integrated result, and this error can depend critically on the sampling patterns used during integration. Most of the well-known samplers used for MC integration in graphics---e.g. jittered, Latin-hypercube (N-rooks), multijittered---are anisotropic in nature. However, there are currently no tools available to analyze the impact of such anisotropic samplers on the variance convergence behavior of Monte Carlo integration. In this work, we develop a Fourier-domain mathematical tool to analyze the variance, and subsequently the convergence rate, of Monte Carlo integration using any arbitrary (anisotropic) sampling power spectrum. We also validate and leverage our theoretical analysis, demonstrating that judicious alignment of anisotropic sampling and integrand spectra can improve variance and convergence rates in MC rendering, and that similar improvements can apply to (anisotropic) deterministic samplers." @default.
- W2736701250 created "2017-07-31" @default.
- W2736701250 creator A5009820805 @default.
- W2736701250 creator A5022506083 @default.
- W2736701250 date "2017-07-20" @default.
- W2736701250 modified "2023-10-11" @default.
- W2736701250 title "Convergence analysis for anisotropic monte carlo sampling spectra" @default.
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- W2736701250 doi "https://doi.org/10.1145/3072959.3073656" @default.
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