Microfacet Model - Computer graphicsPage 1 CS348B Lecture 13 Pat Hanrahan, Spring 2004 Reflection...

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Page 1 CS348B Lecture 13 Pat Hanrahan, Spring 2004 Reflection Models Last lecture Phong model Microfacet models Gaussian height field on surface Self-shadowing Today Torrance-Sparrow model Anisotropic reflection models Multiple importance sampling Microfacet Model

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  • Page 1

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Reflection Models

    Last lecture

    Phong model

    Microfacet models

    Gaussian height field on surface

    Self-shadowing

    Today

    Torrance-Sparrow model

    Anisotropic reflection models

    Multiple importance sampling

    Microfacet Model

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Torrance-Sparrow Model

    ( )( )4cos cos

    hr i r

    i r

    Df ωω ωθ θ

    → =

    N̂iωrωhω

    ( )( ) ( ) cos ssh hD Dω α α ω= = = •N

    iθ rθ

    α

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Torrance-Sparrow Theory

    ( )( ) ( ) ( ) ( )4 cos cos

    r i r

    i i r

    i r

    fF S S D

    ω ωθ θ θ α

    θ θ

    →′

    =

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Torrance-Sparrow Comparison

    Aluminum

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Torrance-Sparrow Comparison

    Magnesium Oxide

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    “Diffuse” Reflection

    ExperimentalPressed magnesium oxide powder used as an example of a diffuse reflectorReflection greater at high angles of incidence

    TheoreticalBouguer - Microfacet distribution

    No microfacet distribution can reflect rays equally in all directions

    Multiple surface or subsurface reflections

    Paint manufactures attempt to create ideal diffuse

    Anisotropic Reflection Model

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Anisotropic Reflection

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Quarterhorse

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Reflection from a Cylinder

    Ν̂

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Reflection from a Cylinder

    Ν̂

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Reflection from a Cylinder

    ˆˆ( )NR L

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Reflection from a Cylinder

    ˆˆ( )NR L

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Reflection from a Cylinder

    ˆˆ( )NR L

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Shape of Anisotropic Highlights

    From Lu, Koenderink, Kappers

    Fibers tangent to the plane defined by the halfway vector reflect light

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Shape of Anisotropic Highlights

    From Lu, Koenderink, Kappers

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Anisotropic Reflection

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Kajiya-Kay Model

    Diffuse

    Specular

    ( )2ˆ ˆsin 1Lθ = − •T L

    ( ) ( )cos cos cos sin sin ss E L E L E Lθ θ θ θ θ θ− = +

    ˆˆ( )NR L

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Herbert

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Fiber Model

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Fiber Model

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Fiber Model

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Caustics

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Hair Appearance

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Hair Appearance

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Multiple Importance Sampling

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Multiple Importance Sampling

    Reflection of a circular light source by a rough surface

    Radius

    Shin

    ines

    s

    Sampling the light source Sampling the BRDF( ) ( )f x g x dx∫

    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Multiple Importance Sampling

    Two sampling techniques

    Form weighted combination of samples

    The balance heuristic

    2, 2

    2,2,

    2 2,

    ~ ( )( )( )

    i

    ii

    i

    X p xf X

    Yp X

    =

    1, 1

    1,1,

    1 1,

    ~ ( )( )( )

    i

    ii

    i

    X p xf X

    Yp X

    =

    1 1, 2 2,i i iY w Y w Y= +

    1 1 2 21 2

    ( )( ) ( ) ( ) ( ) ( ) ( )( ) ( )

    ii

    p xw x p x w x p x w x p xp x p x

    = ⇒ = ++

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    CS348B Lecture 13 Pat Hanrahan, Spring 2004

    Multiple Importance Sampling

    Source: Veach and Guibas