CS 431/636 Advanced Rendering Techniques"


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1 CS 431/636 Advanced Rendering Techniques" Dr. David Breen" Korman 105D" Wednesday 6PM 8:50PM" Photon Mapping" 5/2/12"
2 Slide Credits  UC San Diego
3 Goal Efficiently create global illumination images with caustics and complex surface properties (BRDFs)
4 Box: Direct Illlumination
5 Box: Global Illlumination
6 Approach Two pass process Distribute light info into scene (photons) Store light flux in photon map Combine photon info with ray tracing when rendering
7 Emitting Photons Photons emitted from light sources Traced through scene Stored (energy & direction) at surfaces Russian Roulette determines if photon is absorbed or reflected BRDF used to determine direction of reflection
8 Two Photon Maps Global Low resolution photons in all directions Classify Caustic Direct, Shadow and Indirect High resolution at refracting/reflecting objects
9 Global Illumination global photon map caustics photon map
10 The photon map datastructure The photons are stored in a left balanced kdtree struct photon = { float position[3]; rgbe power; char phi, theta; short flags; } // power packed as 4 bytes // incoming direction
11 Photon tracing Photon emission Photon scattering Photon storing
12 Photon emission Given Φ Watt lightbulb. Emit N photons. Each photon has the power Φ N Watt. Photon power depends on the number of emitted photons. Not on the number of photons in the photon map.
13 What is a photon? Flux (power)  not radiance! Collection of physical photons A fraction of the light source power Several wavelengths combined into one entity
14 Diffuse point light Generate random direction Emit photon in that direction // Find random direction do { x = 2.0*random()1.0; y = 2.0*random()1.0; z = 2.0*random()1.0; } while ( (x*x + y*y + z*z) > 1.0 );
15 Example: Diffuse square light  Generate random position p on square  Generate diffuse direction d  Emit photon from p in direction d // Generate diffuse direction u = random(); v = 2*π*random(); d = vector( cos(v) u, sin(v) u, 1 u );
16 Surface interactions The photon is Stored (at diffuse surfaces) and Absorbed (A) or Reflected (R) or Transmitted (T ) A + R + T = 1.0
17 Photon scattering The simple way: Given incoming photon with power Φ p Reflect photon with the power R Φ p Transmit photon with the power T Φ p
18 Photon scattering The simple way: Given incoming photon with power Φ p Reflect photon with the power R Φ p Transmit photon with the power T Φ p Risk: Too many lowpowered photons  wasteful! When do we stop (systematic bias)? Photons with similar power is a good thing.
19 Russian Roulette Statistical technique Known from Monte Carlo particle physics Introduced to graphics by Arvo and Kirk in 1990
20 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
21 Russian Roulette Intuition Surface reflectance: R = incoming photons with power: Φ p = 2 Watt Reflect 100 photons with power 2 Watt instead of 200 photons with power 1 Watt.
22 Russian Roulette Very important! Use to eliminate unimportant photons Gives photons with similar power :)
23 Bidirectional Reflection Distribution Function (BRDF) function which defines the spectral and spatial reflection characteristic of a surface f(θ i, Θ r, λ)
24 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 )
25 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
26 Rendering Ray trace scene Use photon maps to approximate low importance shading values to evaluate Bidirectional Reflection Distribution Function (BRDF) to perform shadow calculation to calculate caustics
27 Ray Tracing vs. Radiance Approximation Approximation Diffuse surfaces Deep in ray tree (contribution less significant) Shadow calculations Ray Tracing Direct illumination High gloss, specular surfaces
28 A simple test scene
29 Rendering
30 Direct Illumination
31 Specular Reflection
32 Caustics
33 Indirect Illumination
34 Radiance Estimate L(x, ω) = = = f r (x, ω, ω)l (x, ω ) cos θ dω Ω f r (x, ω, ω) dφ2 (x, ω ) Ω dω cos θ da cos θ dω f r (x, ω, ω) dφ2 (x, ω ) Ω da n f r (x, ω p, ω) Φ p(x, ω p) πr 2 p=1
35 Radiance Estimate L
36 Fast estimate 200 photons / 50 photons in radiance estimate
37 Indirect illumination photons / 500 photons in radiance estimate
38 Global Illumination photons / 50 photons in radiance estimate
39 Global Illumination photons / 500 photons in radiance estimate
40 Box global photons, caustic photons
41 Box: Global Photons global photons
42 Fractal Box global photons, caustic photons
43 Cornell Box
44 Caustic from a Glass Sphere Photon Mapping: photons / 50 photons in radiance estimate
45 Sphereflake Caustic
46 Reflection Inside A Metal Ring photons / 50 photons in radiance estimate
47 Caustics On Glossy Surfaces photons / 100 photons in radiance estimate
48 HDR environment illumination Using lightprobe from
49 Cognac Glass
50 Indirect Illumination
51 Little Matterhorn
52 Mies house (3pm)
53 Mies house (6pm)
54 More Information Foreword by Pat Hanrahan The creation of realistic threedimensional images is central to computer graphics. Photon mapping, an extension of ray tracing, makes it possible to efficiently simulate global illumination in complex scenes. Photo mapping can simulate caustics (focused light, such as shimmering waves at the bottom of a swimming pool), diffuse interreflections (e.g., the `bleeding' of colored light from a red wall onto a white floor, giving the floor a reddish tint), and participating media (e.g., clouds or smoke). This book is a practical guide to photon mapping; it provides both the theory and the practical insight necessary to implement photon mapping and simulate all types of direct and indirect illumination efficiently. A K PETERS LTD. Realistic Image Synthesis Using Photon Mapping Realistic Image Synthesis Using Photon Mapping Jensen AK PETERS Realistic Image Synthesis Using Photon Mapping Foreword by Pat Hanrahan
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