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Understanding the role of phase function in translucent appearance Ioannis Gkioulekas 1 Bei Xiao 2 Shuang Zhao 3 Edward Adelson 2 Todd Zickler 1 Kavita.

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Presentation on theme: "Understanding the role of phase function in translucent appearance Ioannis Gkioulekas 1 Bei Xiao 2 Shuang Zhao 3 Edward Adelson 2 Todd Zickler 1 Kavita."— Presentation transcript:

1 Understanding the role of phase function in translucent appearance Ioannis Gkioulekas 1 Bei Xiao 2 Shuang Zhao 3 Edward Adelson 2 Todd Zickler 1 Kavita Bala 3 1 Harvard 3 Cornell 2 MIΤ 1

2 Translucency is everywhere foodskin jewelryarchitecture 2

3 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)

4 Phase function is important thick parts (diffusion) thin parts 4

5 Common phase functions single-parameter family: Henyey-Greenstein (HG) lobes 5 Henyey and Greenstein 1941 average cosine

6 What can we represent with HG?  microcrystalline wax 6 marble  white jade  Jensen 2001

7 Henyey-Greenstein is not enough soap microcrystalline wax photo HG setup 7

8 Goals 8 expanded phase function space role in translucent appearance ? ?

9 Expanded phase function space single-parameter family: Henyey-Greenstein (HG) lobes 9 average cosine second moment von Mises-Fisher (vMF) lobes single-parameter family:

10 Expanded phase function space soap microcrystalline wax photo HG setup vMF 10

11 Expanded phase function space single-parameter family: Henyey-Greenstein (HG) lobes von Mises-Fisher (vMF) lobes single-parameter family: Linear mixtures: HG + HGHG + vMF vMF + vMF 11

12 f( ) Redundant phase function space ≈ ≠ 12 ≈

13 Related work 13 Fleming and Bülthoff 2005, Motoyoshi 2010 Pellacini et al. 2000, Wills et al many perceptual cues do not study phase function gloss perception much smaller space Ngan et al gloss perception navigation of appearance space

14 Our approach 1. Computational processing 2. Psychophysical validation 3. Analysis of results image-driven analysistractable experimentvisualization, perceptual parameterization 14

15 Scene design mostly low- order scattering mostly high- order scattering side-lighting thick body and base thin parts and fine details 15

16 von Mises-Fisher (vMF) lobes Linear mixtures: HG + HGHG + vMF Henyey-Greenstein (HG) lobes Expanded phase function space 16 sample 750+ phase functions 3000 machine hours 750+ HDR images

17 Psychophysics Paired-comparison experiments Hmm, left 17

18 Psychophysics 750 images = 200 million comparisons 18

19 d(, ) ǁ - ǁ Image-driven analysis ≈ 19

20 two-dimensional appearance space two-dimensional embedding Computational processing 750 HDR images ǁ - ǁ multidimensional scaling 20 ≈

21 Our approach 1. Computational processing 2. Psychophysical validation image-driven analysistractable experiment Analysis of results visualization, perceptual parameterization

22 40 representative images Psychophysical validation ǁ - ǁ clustering two-dimensional appearance space 22

23 Psychophysical validation 750 phase functions = 200 million comparisons 40 phase functions = 30,000 comparisons 23

24 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)

25 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)

26 Our approach 1. Computational processing 2. Psychophysical validation image-driven analysistractable experiment Analysis of results visualization, perceptual parameterization

27 What we know so far translucent appearance space two-dimensional perceptual consistent across variations of material, shape, illumination 27 see paper for: images, 9 more computational embeddings, 2 more psychophysical experiments including backlighting, analysis and statistics

28 Moving around the space 28

29 Moving around the space moving vertically more diffused appearance 29

30 Moving around the space moving vertically more diffused appearance 30

31 Moving around the space moving horizontally more glass-like appearance 31

32 Moving around the space moving horizontally more glass-like appearance 32

33 we can move anywhere Moving around the space 33

34 What can we render with… single forward lobes forward + isotropic mixtures forward + backward mixtures 34

35 What can we render with… single forward lobes forward + isotropic mixtures forward + backward mixtures 35

36 What can we render with… marble white jade marblewhite jade with vMF + vMF best approximation with HG + isotropic 36 ≠

37 Editing the phase function move horizontallymove vertically 37 more glass-like

38 g Perceptual parameterization move vertically

39 0.32 Perceptual parameterization move vertically 0.64 g2g2 39 0

40 Perceptual parameterization 40 move vertically 0 g g2g2

41 Discussion 41 handling other parameters of appearance: σ t, σ a, 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!

42 Three take-home messages HG is not enough expanded space computation + psychophysics large-scale perceptual studies 2D appearance space uniform parameterization 42 white jademarble

43 Acknowledgements Wenzel Jakob Bonhams Funding: NSF NIH Amazon white jademarble 43 Dataset of images:

44 Computational embeddings material variationshape variationlighting variation more HDR images

45 Scene design 45

46 computational embedding Psychophysical validation 46 ≈ perceptual embedding (non-metric MDS on psych. data) (MDS using image metrics)

47 Computational metrics L 1 -norm L 2 -norm cubic root

48 Perceptual image metrics material variationshape variationlighting variation

49 Embedding stability originalperturbation 1 perturbation 2 perturbation 3 perturbation 4 perturbation 5

50 Distance metric MDS Davis et al sample 750+ phase functions MDS

51 Non-metric MDS Wills et al Learning from relative comparisons non-metric MDS d >d Hmm, left


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