Distributed Ray Tracing Part 2

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# Distributed Ray Tracing Part 2 - PowerPoint PPT Presentation

Distributed Ray Tracing Part 2. 黃聰賢. Overview. Render Equation BRDF Importance Sampling Implementation. Rendering Equation (1). ω o. x. is the radiance from a point to given direction w o. Rendering Equation (2). ω o. x. is the emitted radiance.

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### Distributed Ray Tracing Part 2

Overview
• Render Equation
• BRDF
• Importance Sampling
• Implementation
Rendering Equation (1)

ωo

x

• is the radiance from a point to given direction wo
Rendering Equation (2)

ωo

x

• is non-zero if x is emissive(a light source)
Rendering Equation (3)

ωi

ωo

x

• Sum of the contributionfrom all of the other direction in the scene
Rendering Equation (4)

ωi

ωo

x

• Radiance from all hemisphere direction
Integration over hemisphere

y0

ω0

y1

normal

ω1

eye

yi

ωi

x

Spherical sample direction

L(x,wo) = (2 PI / #samples) * ∑ [BRDF(x,wo,wi)*L(yi,-wi) * cos(n,ωi)]

Spherical Uniform Sampling

Generate two uniform random variables in [0,1) : ξx, ξy

x = sin(θ) cos(φ)

y = sin(θ) sin(φ)

z = cos(θ)

φ

Why?

Too Many

Too Coarse

Importance

Implement of Importance Sampling
• Generate enough samples (uniform samples)
• Compute the importance of each sample
• Build the CDF of importance
• Generate uniform random variables over [0,1)
• Use Inverse CDF to choose a sample
• Divide the contribution of each sample by its probability
Direct Lighting
• Use Phong Lighting Model.
• Add the lighting effect if visibility is one.

I * (Kd * dot(N, L) + Ks * pow(dot(E, R), Ns) )

N

E

L

R

Indirect Lighting
• Use importance sampling to choose direction
• If the direction hits a point yi ,compute the yi direct lighting

y0

ω0

y1

normal

ω1

eye

yi

ωi

x

L(x, ωo) = (2 PI / #samples) * ∑ [BRDF(x, ωo, ωi)*L(yi,-ωi) * cos(n,ωi)]

L(x, ωo) = (1.0 / #samples) * ∑ { L(yi ,-ωi) * [Kd * dot(ωi, N) + Ks * pow(dot(E, reflect(ωi, N)), Ns) ] }

N

E

yi

ωi

x