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Understanding the role of phase function in translucent app Understanding the role of phase function in translucent app

Understanding the role of phase function in translucent app - PowerPoint Presentation

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Understanding the role of phase function in translucent app - PPT Presentation

Ioannis Gkioulekas 1 Bei Xiao 2 Shuang Zhao 3 Edward Adelson 2 Todd Zickler 1 Kavita Bala 3 1 Harvard 3 Cornell 2 MI Τ 1 Translucency is everywhere food skin jewelry ID: 425456

phase space function perceptual space phase perceptual function appearance computational moving vmf embedding mds lobes psychophysical images validation 750

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Slide1

Understanding the role of phase function in translucent appearance

Ioannis

Gkioulekas

1

Bei Xiao2

Shuang Zhao3

Edward Adelson2

Todd Zickler1

Kavita Bala3

1Harvard

3Cornell

2

MIΤ

1Slide2

Translucency is everywhere

food

skin

jewelry

architecture

2Slide3

Subsurface scattering

radiative

transfer equation

Chandrasekhar 1960

phase function p

absorption coefficient

σ

a

extinction coefficient

σ

t

3

isotropic

incident direction

outgoing direction

(

λ)

(

λ)

(

λ)Slide4

Phase function is important

thick parts (diffusion)

thin parts

4Slide5

Common phase functions

single-parameter family:

Henyey

-Greenstein (HG) lobes

 

5

Henyey

and Greenstein 1941

 

average cosine

 Slide6

What can we represent with HG?

microcrystalline wax

6

marble

white jade

Jensen 2001Slide7

Henyey-Greenstein is not enough

s

oap

microcrystalline wax

photo

HG

s

etup

7Slide8

Goals

8

expanded phase function space

role in translucent appearance

?

?Slide9

Expanded phase function space

single-parameter family:

Henyey

-Greenstein (HG) lobes

 

9

 

average cosine

 

second moment

von

Mises

-Fisher (

vMF

) lobes

single-parameter family:

 Slide10

Expanded phase function space

s

oap

microcrystalline wax

photo

HG

s

etup

vMF

10Slide11

Expanded phase function space

single-parameter family:

Henyey

-Greenstein (HG) lobes

von

Mises

-Fisher (

vMF

) lobes

single-parameter family:

Linear mixtures:

HG + HG

HG +

vMF

vMF

+

vMF

11

 

 Slide12

f

( )

f( )

Redundant phase

function space

12

≈Slide13

Related work

13

Fleming and

Bülthoff 2005, Motoyoshi

2010

Pellacini et al. 2000, Wills et al. 2009

many perceptual cuesdo not study phase function

gloss perception

much smaller spaceNgan

et al. 2006

gloss perceptionnavigation of appearance spaceSlide14

Our approach

1. Computational

processing

2

. Psychophysical validation

3. Analysis of results

i

mage-driven analysis

tractable experiment

visualization, perceptual parameterization

14Slide15

Scene design

mostly low-order scattering

mostly high-order scattering

side-lighting

thick body and base

thin parts and fine details

15Slide16

von

Mises

-Fisher (

vMF

) lobes

Linear mixtures:

HG + HG

HG +

vMF

Henyey

-Greenstein (HG) lobes

Expanded phase function space

16

sample 750+ phase functions

3000 machine hours

750+ HDR imagesSlide17

Psychophysics

Paired-comparison experiments

Hmm, left

17Slide18

Psychophysics

750 images = 200 million comparisons

18Slide19

d( , )

ǁ - ǁ

 

 

Image-driven analysis

19Slide20

two-dimensional appearance space

two-dimensional embedding

Computational processing

750 HDR images

ǁ - ǁ

 

 

multidimensional scaling

20

≈Slide21

Our approach

1. Computational

processing

2. Psychophysical validation

i

mage-driven analysis

tractable experiment

21

3. Analysis of results

visualization, perceptual parameterizationSlide22

40 representative images

Psychophysical validation

ǁ - ǁ

 

 

clustering

two-dimensional appearance space

22Slide23

Psychophysical validation

750 phase functions = 200 million comparisons

40

phase functions = 30,000 comparisons

23Slide24

computational embedding

Psychophysical validation

24

perceptual embedding

use computational embedding as proxy for psychophysics

generalize to all 750 images

(non-metric MDS on psych. data)

(MDS using image metrics)Slide25

computational embedding

Psychophysical validation

25

perceptual embedding

use computational embedding as proxy for psychophysics

generalize to all 750 images

(non-metric MDS on psych. data)

(MDS using image metrics)Slide26

Our approach

1. Computational

processing

2. Psychophysical validation

i

mage-driven analysis

tractable experiment

26

3. Analysis of results

visualization, perceptual parameterizationSlide27

What we know so far

translucent appearance space

two-dimensional

perceptual

consistent across

variations of material, shape,

illumination

27see paper for: 5000+ images, 9 more computational

embeddings, 2 more psychophysical experiments including backlighting, analysis and statisticsSlide28

Moving around the space

28Slide29

Moving around the space

moving vertically

m

ore diffused appearance

29Slide30

Moving around the space

moving vertically

m

ore diffused appearance

30Slide31

Moving around the space

moving horizontally

m

ore glass-like appearance

31Slide32

Moving around the space

moving horizontally

m

ore glass-like appearance

32Slide33

we can move anywhere

Moving around the space

33Slide34

What can we render with…

single forward lobes

forward + isotropic mixtures

forward + backward mixtures

34Slide35

What can we render with…

single forward lobes

forward + isotropic mixtures

forward + backward mixtures

35Slide36

What can we render with…

marble

white jade

marble

white jade

with

vMF

+

vMF

best approximation

with HG + isotropic

36

≠Slide37

Editing the phase function

move horizontally

move vertically

37

 

 

more glass-like

more diffusedSlide38

g

Perceptual parameterization

move vertically

0.8

0.4

0

38

HG:

 Slide39

HG:

 

0.32

Perceptual parameterization

move vertically

0.64

g

2

39

0Slide40

HG:

 

Perceptual parameterization

40

move vertically

0

g

0.8

0.4

0.32

0.64

g

2

HG:

 Slide41

Discussion

41

handling other parameters of appearance:

σt,

σa, color

more general or data-driven phase function models

use in translucency editing and design user interfaces

need to (further) scale up methodology

see our SIGGRAPH Asia 2013 paper!Slide42

Three take-home messages

HG is not enough

expanded space

computation + psychophysics

large-scale perceptual studies

2D appearance spaceuniform parameterization

42

white jade

marbleSlide43

Acknowledgements

Wenzel

Jakob

Bonhams

Funding:NSF NIH

Amazon

white jade

marble

43

http://tinyurl.com/s2013-translucency

Dataset of 5000+ images:Slide44

Computational embeddings

material variation

shape variation

lighting variation

5000+ more HDR imagesSlide45

Scene design

45Slide46

computational embedding

Psychophysical validation

46

perceptual embedding

(non-metric MDS on psych. data)

(MDS using image metrics)Slide47

Computational metrics

L

1

-norm

L

2

-norm

c

ubic rootSlide48

Perceptual image metrics

material variation

shape variation

lighting variationSlide49

Embedding stability

original

perturbation 1

perturbation 2

perturbation 3

perturbation 4

perturbation 5Slide50

 

Distance metric

MDS

Davis et al. 2007

sample 750+ phase functions

MDSSlide51

Non-metric MDS

Wills et al. 2009

Learning from relative comparisons

non-metric

MDS

d >d

Hmm, left