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3-D Computer Vision CSc83029 / 3-D Computer Vision CSc83029 / Ioannis Stamos Ioannis Stamos Vision Vision CSc 83029 CSc 83029 Radiometry and Reflectance Radiometry and Reflectance

3-D Computer Vision CSc83029 / Ioannis Stamos 3-D Computer Vision CSc 83029 Radiometry and Reflectance

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3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

3-D Computer Vision3-D Computer VisionCSc 83029CSc 83029

Radiometry and ReflectanceRadiometry and Reflectance

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

From 2D to 3DFrom 2D to 3D

DEPTH from TWO or MORE IMAGESDEPTH from TWO or MORE IMAGES StereoStereo Optical Flow -> Factorization Method.Optical Flow -> Factorization Method.

SHAPE from SINGLE IMAGE CUESSHAPE from SINGLE IMAGE CUES SHAPE from SHADINGSHAPE from SHADING SHAPE from TEXTURESHAPE from TEXTURE ……

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Shading Encodes ShapeShading Encodes Shape

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Radiometry and ReflectanceRadiometry and Reflectance

n

I

Image Intensity I = f ( orientation n,surface reflectance,illumination, imaging system )

L

Surface Element

Note: Image Intensity Understanding is an under-constrained problem!

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Solid AngleSolid Angle

δω=(δA cosθ)/R (steradian)

δ

δω

2

Solid AngleSolid Angle

δω=(δA cosθ)/R (steradian)

δ

δω

2

Foreshortened Area

δA’

δω= δA’/R (steradian)2

Solid AngleSolid Angle

δω=(δA cosθ)/R (steradian)

δ

δω

2

Foreshortened Area

UnitSphere

δA’

δω= δA’/R (steradian)2

Solid Angle Sustainedby a hemisphere = 2π

n

Source

Flux

Radiant Intensity of Source: Light flux (power)emitted per unit solid angle:

J=dΦ/dω (watts/steradian)

dA

Surface Irradiance: Flux incident per unit surface area:

E=dΦ/dA (watts/m )Does not depend on where the light is coming from!

2

Flux=dΦ

Surface Radiance(Brightness): Flux emitted per unit foreshortened area,

per unit solid angle:

L= d Φ/(dA cosθr)dω (watts/m . steradian)

Note: L depends on direction θr Surface can radiate into whole hemisphere L is proportional to irradiance E Depends on reflectance properties of surface

dA

2

2

θr

2

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Surface Radiance & Image IrradianceSurface Radiance & Image Irradiance

E=δP/δI:Irradiance at point p (Watts/m )δP= (δO * cosθ)* L * ΔΩ , L scene radiance at P.

2

L (Watts/m * steradian)2Trucco & Verri

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

F=f/d: F-number:How much lightis captured by the camera

Trucco & Verri

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Radiometry and ReflectanceRadiometry and Reflectance

n

I

Image Intensity I = f ( orientation n,surface reflectance,illumination, imaging system )

)(cos4

4

2

f

dLE

Scene radiance

L

Image irradiance

1 / F-number of lens

E

Optics

Brightness falloff

LE Assume Image irradiance is proportional to scene radiance

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Bi-Directional Reflectance Distribution Function Bi-Directional Reflectance Distribution Function (BRDF)(BRDF)

n ii , rr ,

yx

z

φ

θ

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Bi-Directional Reflectance Distribution Function Bi-Directional Reflectance Distribution Function (BRDF)(BRDF)

n ii , rr ,

yx

z

φ

θ

iiE , : Irradiance due to source in direction ii ,

rrL , : Radiance of surface in direction rr ,

ii

rrrrii E

Lf

,

,,,, BRDF:

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Bi-Directional Reflectance Distribution Function Bi-Directional Reflectance Distribution Function (BRDF)(BRDF)

n ii , rr ,

yx

z

φ

θ

iiE , : Irradiance due to source in direction ii ,

rrL , : Radiance of surface in direction rr ,

ii

rrrrii E

Lf

,

,,,, BRDF:

irrif ,,For Rotationally Symmetric Reflectance Properties:BDRF: ISOTROPIC SURFACES

*Bird Feathers are often Non-Isotropic.

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Reflectance ModelsReflectance Models*Reflection: An Electromagnetic Phenomenon

λ

τ

σh

Two Approaches to deriving Reflectance Models:Physical Optics (Wave Optics)Geometrical Optics (Ray Optics)

Geometrical Models are approximation to Physical Models.But easier to use.

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Surface Reflection

Body Reflection

Internal scattering

Body Reflection: *Diffuse Reflection *Matte Appearance *Non-Homogeneous Medium

Surface Reflection: *Specular Reflection *Glossy Appearance *Highlights. *Dominant for Metals

Image Intensity: Diffuse Comp + Specular Comp

Lambertian Reflectance ModelLambertian Reflectance Model

f Albedo: intrinsic brightness or color of surface

n

ikE cos

Theta is the angle between source direction and surface normal

s

i

A Lambertian (diffuse) surface scatters light equally in all directions

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Lambertian Reflectance ModelLambertian Reflectance Model

i

iirr

kL

EL

EfL

cos

, ,

Surface appears equally bright from all viewing directions

A Lambertian (diffuse) surface scatters light equally in all directions

Albedo: intrinsic brightness or color of surface

Illumination strength

f

n

i

s

ikE cos

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Lambertian Reflectance ModelLambertian Reflectance Model

sn

L

kL icos

A Lambertian sphere

ns

Surface normal nDirection of illumination s

Commonly used in Computer Vision and Graphics

Effective albedo

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

What Information Does Shading What Information Does Shading EncodeEncode

In regions of constant albedo, changesof intensity correspond to changes in thesurface normal of the scene

sn LI

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Ideal Specular Model (Mirrors)Ideal Specular Model (Mirrors)

n

))(()( eieikL

Perfect reflector

sr

*Very SMOOTH surface*All incident energy reflected in a single direction

ii , rr ,

ee ,v

Viewer received light only when v=r

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Phong Reflectance ModelPhong Reflectance Model

b=0.3, c=0.7, n=2 b=0.7, c=0.3, n=0.5

ni cbL coscos

diffuse specular

v: viewingdirection

αθi

n

s

r

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Torrance-Sparrow ModelTorrance-Sparrow ModelSpecular Reflection from Rough Surfaces.Surface Micro-Structure Model

Facet orientationMean orientation α

Micro-facet Orientation Model: (example)

2

2

2

1

2

1)(

a

eap

(Gaussian Model) Isotropic

σ: roughness parameter

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Torrance-Sparrow ModelTorrance-Sparrow ModelMasking and Shadowing Effects

shadow shadow

Masked Light

n sr

v

Specular Direction

),,()( vnsGpvn

PL spec

Radiance

Geometric Factor G(Masking-Shadowing)

n’β

3-D Computer Vision CSc83029 / Ioannis Stamos3-D Computer Vision CSc83029 / Ioannis Stamos

Gradient Space (p,q)Gradient Space (p,q)

Surface normal can be represented by the intersection of the normal with a plane.

qp,

1

1,,22

qp

qpn

np

q

x

y

Sourcez

1.0 ss qp ,