biased monte carlo ray tracing

Biased Monte Carlo Ray Tracing Filtering, Irradiance Caching, and - PowerPoint PPT Presentation

Biased Monte Carlo Ray Tracing Filtering, Irradiance Caching, and Photon Mapping Henrik Wann Jensen Stanford University May 23, 2002 Unbiased and Consistent Unbiased estimator: E { X } = . . . Consistent estimator: N E


  1. Russian Roulette Probability of termination: p E { X } = p · 0 + (1 − p ) · E { X } 1 − p

  2. Russian Roulette Probability of termination: p E { X } = p · 0 + (1 − p ) · E { X } 1 − p = E { X }

  3. Russian Roulette Probability of termination: p E { X } = p · 0 + (1 − p ) · E { X } 1 − p = E { X } Terminate un-important photons and still get the correct result.

  4. Russian Roulette Example Surface reflectance: R = 0.5 Incoming photon: Φ p = 2 W r = random(); if ( r < 0 . 5 ) reflect photon with power 2 W else photon is absorbed

  5. Russian Roulette Intuition Surface reflectance: R = 0.5 200 incoming photons with power: Φ p = 2 Watt Reflect 100 photons with power 2 Watt instead of 200 photons with power 1 Watt.

  6. Russian Roulette • Very important! • Use to eliminate un-important photons • Gives photons with similar power :)

  7. Sampling a BRDF f r ( x, � ω i , � ω o ) = w 1 f r, 1 ( x, � ω i , � ω o ) + w 2 f r, 2 ( x, � ω i , � ω o )

  8. Sampling a BRDF f r ( x, � ω i , � ω o ) = w 1 · f r,d + w 2 · f r,s r = random() · ( w 1 + w 2 ) ; if ( r < w 1 ) reflect diffuse photon else reflect specular

  9. Rendering

  10. Direct Illumination

  11. Specular Reflection

  12. Caustics

  13. Indirect Illumination

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