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Learn about Monte Carlo Sampling, Importance Metrics, Variance Analysis for Visibility, Hierarchical Environment Map Stratification, and Rendering Optimizations to optimize rendering processes in computer graphics.
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Structured Importance Sampling of Environment Maps Agarwal, S., R. Ramamoorthi, S. Belongie, and H. W. Jensen
Outline • Monte Carlo Sampling and Importance metric • Variance Analysis for Visibility • Hierarchical Environment Map Stratification • Rendering Optimizations
Monte Carlo Sampling and Importance • Area based stratified sampling • Illumination-based importance sampling
Importance Metric • Illumination-based importance sampling • ( a=1 b=0 ) • Area based stratified sampling • ( a=0 b=1 )
Variance Analysis for Visibility • (variance) (empirical)
Variance Analysis for Visibility • Correlation model for visibility
Variance Analysis for Visibility • Mean visibility = ½ (assuming) P(S=0) = P(S=1) = ½ θ-> 0 , α(θ) = 1 θbecomes large α(θ) = ½ (T is the correlation angle)
The Number of Samples The number of samples is proportional to Uniform lighting
Hierarchical Environment Map Stratification • Hierarchical Thresholding • Hierarchical Stratification
Hierarchical Thresholding • σ:Standard deviation of the illumnation in the map
Hierarchical Thresholding N1 N2 N3 N4
Hierarchical Stratification • Hochbaum-Shmoys Algorithm • (K-center problem)
Rendering Optimizations • Pre-integrating the illumination • Jittering • Sorting