19_montecarlo

19_montecarlo - MIT EECS 6.837, Cutler and Durand 1 MIT...

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Unformatted text preview: MIT EECS 6.837, Cutler and Durand 1 MIT 6.837 Monte-Carlo Ray Tracing MIT EECS 6.837, Cutler and Durand 2 Schedule Review Session: Tuesday November 18 th , 7:30 pm bring lots of questions! Quiz 2: Thursday November 20 th , in class (one weeks from today) MIT EECS 6.837, Cutler and Durand 3 Review of last week? MIT EECS 6.837, Cutler and Durand 4 Radiosity Form-factor between patches Geometry and visibility Big Matrix system Diffuse surfaces Subdivide scene Radiosity assumed constant over a patch MIT EECS 6.837, Cutler and Durand 5 Radiosity Smoothing and other gimmicks MIT EECS 6.837, Cutler and Durand 6 Limitations of radiosity Diffuse only for basic method Costly extension to specular Requires meshing Cost of visibility computation If you send rays, why not use ray tracing? Memory consumption vs. time scalability MIT EECS 6.837, Cutler and Durand 7 hy still learn radiosity? Still used in architecture (Lightscape) Introduction to finite element method Project the problem onto a finite basis of functions In the case of radiosity: piecewise constant Express interaction between elements Get a big matrix system Same as deformable object simulation Pre-computed radiance transfer: same strategy Use finite basis function Precompute lighting as a function of primary sources Use in a simplified version in Max Payne 2 MIT EECS 6.837, Cutler and Durand 8 Today: Monte Carlo Ray Tracing Principle of Monte-Carlo Ray Tracing Monte Carlo integration Review of Rendering equation Advanced Monte Carlo Ray Tracing MIT EECS 6.837, Cutler and Durand 9 Probabilistic Soft Shadows Multiple shadow rays to sample area light source Monte-Carlo ray tracing generalizes this one shadow ray lots of shadow rays MIT EECS 6.837, Cutler and Durand 10 Ray Casting Cast a ray from the eye through each pixel MIT EECS 6.837, Cutler and Durand 11 Ray Tracing Cast a ray from the eye through each pixel Trace secondary rays (light, reflection, refraction) MIT EECS 6.837, Cutler and Durand 12 onte-Carlo Ray Tracing Cast a ray from the eye through each pixel Cast random rays from the visible point Accumulate radiance contribution MIT EECS 6.837, Cutler and Durand 13 onte-Carlo Ray Tracing Cast a ray from the eye through each pixel Cast random rays from the visible point Recurse MIT EECS 6.837, Cutler and Durand 14 onte-Carlo Cast a ray from the eye through each pixel Cast random rays from the visible point Recurse MIT EECS 6.837, Cutler and Durand 15 onte-Carlo Systematically sample primary light MIT EECS 6.837, Cutler and Durand 16 Results (Image removed due to copyright considerations.) MIT EECS 6.837, Cutler and Durand 17 onte-Carlo Take reflectance into account Multiply incoming radiance by BRDF value MIT EECS 6.837, Cutler and Durand 18 1 sample per pixel (Image removed due to copyright considerations.) MIT EECS 6.837, Cutler and Durand 19 256 samples per pixel (Image removed due to copyright considerations.) MIT EECS 6.837, Cutler and DurandMIT EECS 6....
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This note was uploaded on 12/14/2011 for the course EECS 6.837 taught by Professor Durand during the Fall '03 term at MIT.

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19_montecarlo - MIT EECS 6.837, Cutler and Durand 1 MIT...

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