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Poisson Distributions10.90.810.90.80.70.70.60.60.50.510 1000.40.40.30.30.20.20.10.100 0.2 0.4 0.6 0.8 100 0.2 0.4 0.6 0.8 110.90.80.70.610000.50.40.30.2CSE 472 Spring 20130.100 0.2 0.4 0.6 0.8 135Compare110.90.90.80.80.70.70.60.60.50.50.40.40.30.30.20.20.10.100 0.2 0.4 0.6 0.8 100 0.2 0.4 0.6 0.8 1RandomPoissonCSE 472 Spring 201336
What’s the minimum distance?I usually use: range / (2 * sqrt(N))• N points in 2D spaced evenly would berange / sqrt(N) apart• We make the minimum distance half that• When sampling an area that is notsquare, use: sqrt(area) / (2 * sqrt(N))CSE 472 Spring 201337Where does the name come from?CSE 472 Spring 201338
Page 4 and 5: How do we get shadows?What is diffe
Page 6 and 7: Problems w/ numerical precisionCSE
Page 8 and 9: Intersect Functionbool Intersect(co
Page 10 and 11: Aliasing, aka “Jaggies”Why does
Page 12 and 13: Characteristics of the Human EyeRec
Page 14 and 15: Instead of one raythough the center
Page 16 and 17: uniform random numbersFirst idea is
Page 20 and 21: Poisson is French for “Fish”CSE
Page 22 and 23: 4x AntialiasingCSE 472 Spring 20134
Page 24 and 25: Next?CSE 472 Spring 201347Reflectio
Page 26 and 27: Recursion issuesDon’t recurse if
Page 28 and 29: How do we solve this problem?All to
Page 30 and 31: RayColor with PenumbraCPoisson2D po
Page 32 and 33: Putting it all togetherWe have thre
Page 34 and 35: Stochastic Ray TracingProblem with
Page 36 and 37: Photon map•Photon by light-based

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