understanding the role of phase function in translucent appearance
DESCRIPTION
Understanding the role of phase function in translucent appearance. Ioannis Gkioulekas 1. Shuang Zhao 3. Bei Xiao 2. Kavita Bala 3. Todd Zickler 1. Edward Adelson 2. 1 Harvard. 2 MI Τ. 3 Cornell. Translucency is everywhere. skin. food. architecture. jewelry. Subsurface scattering. - PowerPoint PPT PresentationTRANSCRIPT
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Understanding the role of phase function in translucent appearance
Ioannis Gkioulekas1 Bei Xiao2 Shuang Zhao3
Edward Adelson2 Todd Zickler1 Kavita Bala3
1Harvard 3Cornell2MIΤ
1
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Translucency is everywhere
food skin
jewelry architecture
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Subsurface scattering
radiative transfer equation
Chandrasekhar 1960
phase function pabsorption coefficient σa
extinction coefficient σt
3
isotropic
incident direction
outgoing direction
(λ)(λ)
(λ)
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Phase function is important
thick parts (diffusion)
thin parts4
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Common phase functions
single-parameter family:
Henyey-Greenstein (HG) lobes
g=𝜇1
5 Henyey and Greenstein 1941
average cosine
g∈ (−1,1 )❑
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What can we represent with HG?
microcrystalline wax
6
marble white jade Jensen 2001
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Henyey-Greenstein is not enough
soap
microcrystalline wax
photo HG
setup
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Goals
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expanded phase function space role in translucent appearance
??
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Expanded phase function space
single-parameter family:
Henyey-Greenstein (HG) lobes
g=𝜇1
9
average cosine second moment
von Mises-Fisher (vMF) lobes
single-parameter family:𝜅=2𝜇1/ (1−𝜇2 )
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Expanded phase function space
soap
microcrystalline wax
photo HG
setup
vMF
10
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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
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g=𝜇1 𝜅=2𝜇1/ (1−𝜇2 )
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f( ) f( )
Redundant phase function space
≈
≠12
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Related work
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• Fleming and Bülthoff 2005, Motoyoshi 2010
• Pellacini et al. 2000, Wills et al. 2009
• many perceptual cues• do not study phase function
• gloss perception• much smaller space
• Ngan et al. 2006 • gloss perception• navigation of appearance space
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Our approach1. Computational
processing2. Psychophysical
validation3. Analysis of
results
image-driven analysis tractable experiment visualization, perceptual parameterization
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Scene design
mostly low-order scattering
mostly high-order scattering
side-lighting
thick body and base
thin parts and fine details
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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 images
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Psychophysics
Paired-comparison experiments
Hmm, left
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Psychophysics
750 images = 200 million comparisons
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d( , ) ǁ - ǁ𝟑√¿∨¿𝟑√¿∨¿
Image-driven analysis
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two-dimensional appearance spacetwo-dimensional
embedding
Computational processing
750 HDR images
ǁ - ǁ𝟑√¿∨¿𝟑√¿∨¿
multidimensional scaling
20
≈
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Our approach1. Computational
processing2. Psychophysical
validation
image-driven analysis tractable experiment
21
3. Analysis of results
visualization, perceptual parameterization
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40 representative images
Psychophysical validation
ǁ - ǁ𝟑√¿∨¿𝟑√¿∨¿
clustering
two-dimensional appearance space
22
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Psychophysical validation
750 phase functions = 200 million comparisons40 phase functions = 30,000 comparisons23
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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)
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Our approach1. Computational
processing2. Psychophysical
validation
image-driven analysis tractable experiment
26
3. Analysis of results
visualization, perceptual parameterization
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What we know so far
translucent appearance space• two-dimensional• perceptual• consistent across variations of
material, shape, illumination
27
see paper for: 5000+ images, 9 more computational embeddings, 2 more psychophysical experiments including backlighting, analysis and statistics
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Moving around the space
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Moving around the space
moving vertically more diffused appearance30
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Moving around the space
moving horizontally more glass-like appearance32
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we can move anywhere
Moving around the space
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What can we render with…
single forward lobesforward + isotropic mixturesforward + backward mixtures
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What can we render with…marble
white jade
marble white jadewith vMF + vMF
best approximationwith HG + isotropic
36
≠
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Editing the phase function
move horizontally move vertically37
1/√1−𝜇2𝜇12more glass-like
mor
e di
ffuse
d
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g
Perceptual parameterization
move vertically0.8
0.4
0
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HG:
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HG:
0.32
Perceptual parameterization
move vertically0.64
g239
0
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HG:
Perceptual parameterization
40
move vertically
0
g0.8
0.40.32
0.64
g2
HG:
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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!
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Three take-home messages• HG is not enough• expanded space
• computation + psychophysics• large-scale perceptual studies
• 2D appearance space• uniform parameterization
42
white jademarble
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Acknowledgements• Wenzel Jakob• Bonhams
Funding:• NSF • NIH • Amazon
white jademarble
43 http://tinyurl.com/s2013-translucency
Dataset of 5000+ images:
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Computational embeddings
material variation shape variation lighting variation
5000+ more HDR images
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Scene design
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computational embedding
Psychophysical validation
46≈perceptual embedding
(non-metric MDS on psych. data) (MDS using image metrics)
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Computational metrics
L1-normL2-normcubic root
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Perceptual image metrics
material variation shape variation lighting variation
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Embedding stability
original perturbation 1 perturbation 2
perturbation 3 perturbation 4 perturbation 5
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Distance metric
MDS
Davis et al. 2007
sample 750+ phase functions
MDS
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Non-metric MDS
Wills et al. 2009
Learning from relative comparisons
non-metric MDS
d >d
Hmm, left
min𝐾 ≥ 0
λ‖𝐾‖∗+1𝑆∑
𝑠=1
𝑆
𝐿 (𝑑𝐾 (𝑖𝑠 ,𝑘𝑠 )−𝑑𝐾 (𝑖𝑠 , 𝑗𝑠 )+𝑏)