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Visual Perception of Surface Materials Roland W. Fleming Max Planck Institute for Biological Cybernetics.

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1 Visual Perception of Surface Materials Roland W. Fleming Max Planck Institute for Biological Cybernetics

2 2

3 3  Different materials, such as quartz, satin and chocolate have distinctive visual appearances  Without touching an object we usually have a clear impression of what it would feel like: hard or soft; wet or dry; rough or smooth Visual Perception of Material Qualities

4 4  The appearance of “stuff” was a major preoccupation in C17th Dutch art:  Sensual quality of things  Stasis, and contemplation in Still Life  Contrast with Italian Renaissance:  Perspective and the spatial arrangement of things  Drama, events and dynamism. Willem Kalf detail from Still Life with Silver Jug Rijksmuseum, Amsterdam

5 5 Material-ism

6 6 Everyday life

7 7 The state of things

8 8 Edibility

9 9 Edibility

10 10 Usability

11 11 Dung

12 12 Materials in Computer Graphics  Hollywood and the games industry know that photorealism means getting materials right  Henrik Wann Jensen, Steve Marschner and Pat Hanrahan won a technical Oscar in 2004 for their work on Sub- surface scattering Henrik Wann Jensen and Craig Donner

13 13  What gives a material its characteristic appearance?  How does the human brain identify materials across a wide variety of viewing conditions? Research Questions

14 14  Visual estimation of surface reflectance properties: gloss  Perception of materials that transmit light  Refraction  Sub-surface scattering  Exploiting the assumptions of the visual system to edit the material appearance of objects in photographs Outline

15 15 Surface Reflectance  These spheres look different because they have different surface reflectance properties.  Everyday language: colour, gloss, lustre, etc. Images: Ron Dror

16 16 Surface Reflectance  Visual system’s goal: Estimate the BRDF f(  i,  i ;  r,  r ) BRDF Images: Ron Dror

17 17 Confounding Effects of Illumination  Identical materials can lead to very different images  Different materials can lead to very similar images

18 18 Ambiguity between Reflectance and Illumination a(f) = 1 / f   = 0  = 0.4  = 0.8  = 1.2  = 1.6  = 2.0  = 4.0  = 8.0

19 19 Hypothesis Humans exploit statistical regularities of real-world illumination in order to eliminate unlikely image interpretations

20 20 Real-world Illumination Directly from luminous sources Indirectly, reflected from other surfaces Illumination at a point in space: amount of light arriving from every direction.

21 21 Photographically captured light probes (Debevec et al., 2000) Panoramic projection

22 22 Statistics of typical Illumination (Dror et al. 2004)  Intensity histogram is heavily skewed  Few direct light sources

23 23 Statistics of typical Illumination (Dror et al. 2004)  Typical 1/f amplitude spectrum

24 24 Hypothesis Humans exploit statistical regularities of real-world illumination in order to eliminate unlikely image interpretations

25 25 Dismissing unlikely interpretations Blurry feature 2 interpretations:  Sharp reflection, blurry world  Blurry reflection, sharp world But the world usually isn’t blurry! Therefore it is probably a blurry reflection

26 26 Dismissing unlikely interpretations  In practice, unlikely image interpretations do not need to be explicitly entertained  Under normal illumination conditions, different materials yield to diagnostic image features that are responsible for their ‘look’  The brain doesn’t need to model the physics, it just needs to look out for tell-tale image features

27 27  Context has surprisingly little effect on apparent gloss Observations

28 28 Findings  Subjects are good at judging surface reflectance across real-world illuminations: ‘gloss constancy’ Beach Light probe Campus Single point source source Gaussian 1/f Noise  Subjects are poorer at estimating gloss under illuminations with atypical statistics.

29 29 Illuminations have skewed intensity histograms Intensity, I N(I) >10 3 Intensity, I N(I) 40 0 >10 2

30 30 Original Illumination distribution is important Campus Pink noise Modified Intensity, I N(I) >10 3 Intensity, I N(I) >10 3 Intensity, I N(I) 40 0 >10 2 Intensity, I N(I) 40 0 >10 2Original Modified

31 31 Histograms aren’t everything White noise with histogram of Campus Campus original

32 32 Heeger-Bergen texture synthesis Input texture Synthesized texture Treat illumination maps as if they are stochastic texture Taken from Pyramid-Based Texture Analysis/Synthesis

33 33 Wavelet Statistics Beach BuildingCampus Eucalyptus Uffizi St. Peter’s KitchenGrace Synthetic illuminations with same wavelet statistics as real-world illuminations

34 34  Visual estimation of surface reflectance properties: gloss  Perception of materials that transmit light  Refraction  Sub-surface scattering  Exploiting the assumptions of the visual system to edit the material appearance of objects in photographs Outline

35 35 Previous work on materials that transmit light Adelson E.H. (1993). Perceptual organization and the judgment of brightness. Science. 262(5142): Adelson, E. H. (1999). Lightness perception and lightness illusions. In In The New Cognitive Neurosciences. 2nd edn. (ed. Gazzaniga, M.S.) 339—351 (MIT Press, Cambridge, Massachusetts, 1999). Adelson, E. H., and Anandan, P. (1990). Ordinal characteristics of transparency. Proceedings of the AAAI-90 Workshop on Qualitative Vision, Anderson B.L. (1997). A theory of illusory lightness and transparency in monocular and binocular images: the role of contour junctions. Perception. 26(4): Anderson, B.L. (1999). Stereoscopic surface perception. Neuron. 24: Anderson, B.L. (2001). Contrasting theories of White's illusion. Perception, 30: Anderson, B.L. (2003). The Role of Occlusion in the Perception of Depth, Lightness, and Opacity. Psychological Review, 110(4): Anderson B.L. (2003). Perceptual organization and White's illusion. Perception. 32(3): Beck J. (1978). Additive and subtractive color mixture in color transparency. Percept Psychophys. 23(3): Beck J, Prazdny K, Ivry R. (1984). The perception of transparency with achromatic colors. Percept Psychophys. 35(5): Beck J, Ivry R. (1988). On the role of figural organization in perceptual transparency. Percept Psychophys. 44(6): Chen, J.V. & D'Zmura, M. (1998). Test of a convergence model for color transparency perception. Perception, 27: D'Zmura, M., Colantoni, P., Knoblauch, K. & Laget, B. (1997). Color transparency, Perception, 26, D Zmura, M., Rinner, O., & Gegenfurtner, K. R. (2000). The colors seen behind transparent filters. Perception, 29, Gerbino W., Stultiens C.I., Troost J.M. and de Weert C.M. (1990). Transparent layer constancy. J Exp Psychol Hum Percept Perform. 16(1): Gerbino, W. (1994). Achromatic transparency. In A.L. Gilchrist, Ed. Lightness, Brightness and Transparency. (pp ). Hove, England: Lawrence Erlbaum. Heider, G. M. (1933). New studies in transparency, form and color. Psychologische Forschung. 17: 13—56. Kersten, D. (1991) Transparency and the cooperative computation of scene attributes. In Computational Models of Visual Processing, M.I.T. Press, Landy M and Movshon A., Eds.. Kersten, D. and Bülthoff, H. (1991) Transparency affects perception of structure from motion. Massachusetts Institute of Technology, Center for Biological Information Processing Tech. Memo, 36. Kersten, D., Bülthoff, H. H., Schwartz, B., & Kurtz, K. (1992). Interaction between transparency and structure from motion. Neural Computation, 4(4), Khang BG, Zaidi Q. (2002). Cues and strategies for color constancy: perceptual scission, image junctions and transformational color matching. Vision Res. 42(2): Erratum in: Vision Res (24): Khang, B.-G., & Zaidi, Q. (2002). Accuracy of color scission for spectral transparencies. Journal of Vision, 2(6), , doi: / Kanizsa, G. (1955). Condiziono ed effetti della transparenza fenomica. Revista di Psicologia. 49: 3—19. Koffka, K. (1935). Principles of Gestalt Psychology. Cleveland: Harcourt, Brace and World. Lindsey DT, Todd JT. (1996). On the relative contributions of motion energy and transparency to the perception of moving plaids. Vision Res. 36(2): Metelli, F. (1970). An algebraic development of the theory of perceptual transparency. Ergonomics, 13(1): Metelli, F. (1974a). Achromatic color conditions in the perception of transparency. In Eds. Macleod, R. B., & Pick, H. L. (Eds.) Perception (pp. 95—116). Ithaca, NY: Cornell University Press. Metelli F. (1974b). The perception of transparency. Scientific American, 230(4): Metelli, F. (1985). The simulation and perception of transparency. Psychological Research, 47(4): Metelli, F., Da Pos, O. and Cavedon, A. (1985). Balanced and unbalanced, complete and partial transparency. Perception & Psychophysics, 38(4): Masin SC. (1999). Color scission and phenomenal transparency. Percept Mot Skills. 89(3 Pt 1): Masin SC. (1999) Phenomenal transparency in achromatic checkerboards. Percept Mot Skills. 88(2): Masin SC. (1997). The luminance conditions of transparency. Perception. 26(1): Nakayama, K., & Shimojo, S. (1992). Experiencing and perceiving visual surfaces. Science. 257: Nakayama, K., Shimojo, S., & Ramachandran, V. (1990). Transparency: Relation to depth, subjective contours, luminance, and neon color spreading. Perception, 19: Plummer DJ, Ramachandran VS. (1993). Perception of transparency in stationary and moving images. Spat Vis. 7(2): Robilotto R, Khang BG & Zaidi Q. (2002). Sensory and physical determinants of perceived achromatic transparency. JOV. 2(5): Robilotto, R., & Zaidi, Q. (2004). Perceived transparency of neutral density filters across dissimilar backgrounds. Journal of Vision, 4(3), , doi: / Singh M, Anderson BL. (2002). Perceptual assignment of opacity to translucent surfaces: the role of image blur. Perception. 31(5): Singh M, Anderson BL. (2002). Toward a perceptual theory of transparency. Psychol Rev. 109(3): Somers, D. C., and Adelson, E. H. (1997). Junctions, Transparency, and Brightness. Investigative Ophthalmology and Vision Science (supplement), 38, S453. Stoner GR, Albright TD, Ramachandran VS. (1990). Transparency and coherence in human motion perception. Nature. 344(6262): Tommasi M. (1999) A ratio model of perceptual transparency. Percept Mot Skills. 89(3 Pt 1): Watanabe, T. & Cavanagh, P. (1992). The role of transparency in perceptual grouping and pattern recognition. Perception, 21, Watanabe, T. & Cavanagh, P. (1993). Transparent surfaces defined by implicit X junctions. Vision Research, 33: Watanabe, T. & Cavanagh, P. (1993). Surface decomposition accompanying the perception of transparency. Spatial Vision, 7,

36 36 Transparent Materials Metelli’s episcotister

37 37 Real transparent objects

38 38 Real transparent objects  … are not ideal infinitesimal films  … obey Fresnel’s equations:  Specular reflections  Refraction  … can appear vividly transparent without containing the image cues traditionally believed to be important for the perception of “transparency”.

39 39 Real transparent objects  Questions:  What image cues do we use to tell that something is transparent?  How do we estimate and represent the refractive index of transparent bodies?

40 40 Refractive Index  Possibly the most important property that distinguishes real chunks of transparent stuff from Metelli-type materials  Snell’s Law: sin(  i ) sin(  t ) n2n2 n1n1 =

41 41 Refractive Index  Varying the refractive index can lead to the distinct impression that the object is made out a different material refractive index

42 42 Refractive Index

43 43 Refractive Index

44 44 Refractive Index

45 45 Refractive Index

46 46 Refractive Index

47 47 Refractive Index

48 48 Refractive Index

49 49 Refractive Index

50 50 Refractive Index

51 51 Refractive Index

52 52 Refraction and image distortion convexconcave Low RI

53 53 Refraction and image distortion convexconcave High RI

54 54 Displacement Field  Measures the way the transparent object displaces the position of refracted features in the image. d = p refracted - p actual  The perturbation of the texture caused by refraction can be captured by the displacement field.

55 55 vector field Displacement Field

56 56 Distortion Fields  But, to compute the displacement field the visual system would need to know the positions of non- displaced features on the backplane.  Let us assume, instead, that the visual system can estimate the relative distortions of the texture (compression or expansion of texture) D = div( d )

57 57 Distortion Fields distortion fieldimage  Red = magnification  Green = minification

58 58 Distance to backplane

59 59 Distance to backplane

60 60 Distance to backplane

61 61 Distance to backplane

62 62 Distance to backplane

63 63 Distance to backplane

64 64 Distance to backplane

65 65 Distance to backplane

66 66 Distance to backplane

67 67 Distance to backplane

68 68 Distance to backplane convexconcave near

69 69 Distance to backplane convexconcave far

70 70 Object thickness

71 71 Object thickness

72 72 Object thickness

73 73 Object thickness

74 74 Object thickness

75 75 Object thickness

76 76 Object thickness

77 77 Object thickness

78 78 Object thickness

79 79 Object thickness

80 80 Asymmetric Matching task  The distortions in the image depend not only on the RI but also on:  Geometry of object (curvatures, thickness)  Distance between object and background  Distance between viewer and object  So, if the observers base their judgments of RI primarily on the pattern of distortions (rather than correctly estimating the physics), then they should show systematic errors in their estimates when these irrelevant scene factors vary.

81 81 Asymmetric Matching task  Subject adjusts RI of standard probe stimulus to match the appearance of the other stimulus.  One scene variable (thickness, distance to back-plane) is clamped at a different value for test stimulus.  Measures ability of observer to ‘ignore’ or ‘discount’ the differences that are caused by irrelevant scene variables probe test

82 82 Asymmetric Matching task distance to backplanethickness of pebble Refractive index Perceived Refractive index

83 83

84 84  Observers don’t appear to estimate the physics correctly.  Instead, they probably use some heuristic image measurements (e.g. distortion fields) that are affected by refractive index, but also by other scene factors.  This can lead to illusions (mis-perceptions) Observations

85 85  Visual estimation of surface reflectance properties: gloss  Perception of materials that transmit light  Refraction  Sub-surface scattering  Exploiting the assumptions of the visual system to edit the material appearance of objects in photographs Outline

86 86 Image-based Material Editing Kahn, Reinhart, Fleming & Bülthoff, SIGGRAPH 06. inputoutput

87 87 Image based material editing  Goal: given single (HDR) photo as input, change appearance of object to completely different material  Physically accurate solution would require fully reconstructing illumination and 3D geometry.  Beyond state-of-the-art computer vision capabilities inputoutput

88 88 Image based material editing  Alternative: “Perceptually accurate” solution  Series of simple, highly approximate manipulations, each of which is provably incorrect, but whose ensemble effect is visually compelling. inputoutput

89 89 texture mapping output image bilateral filter hole filling environment depth map Processing Pipeline alpha matte segmentation

90 90 Alpha Mattes  By restricting our image modifications to the area within the boundary of the object, we can create the illusion of a transformed material.  Assumption: addition of new non-local effects (e.g. additional reflexes, caustics, etc.) is not crucial.  Approximate shadows and reflections are already in place in original scene

91 91 Automatic alpha-matting  Bayesian pixel classification methods  Chuang, Curless, Salesin & Szeliski (2001)  Apostoloff & Fitzgibbon (2004)  Methods using multiple images with different planes of focus  McGuire, Matusik, Pfister, Hughes & Durand (2005)  Kahn & Reinhard (2005) Although we extract our alpha-mattes manually (rotoscoping), there has been a recent wave of automatic / semi-automatic procedures.

92 92 Processing Pipeline texture mapping output image bilateral filter hole filling environment depth map alpha matte segmentation

93 93 Processing Pipeline texture mapping output image bilateral filter hole filling environment depth map alpha matte segmentation

94 94 How not to do shape-from-shading Try using the state-of-the-art algorithms and you will generally be disappointed! input reconstruction

95 95 Try using the state-of-the-art algorithms and you will generally be disappointed! input reconstruction How not to do shape-from-shading

96 96 Try using the state-of-the-art algorithms and you will generally be disappointed! input reconstruction How not to do shape-from-shading

97 97 What is the alternative ? We use a simple but suprisingly effective heuristic: Dark is Deep In other words … z(x, y) = L(x, y) initial depth estimate input Complicated shape from shading algorithm WHAT ?

98 98 Bilateral Filter  The ‘recovered depths’ are conditioned using a bilateral filter (Tomasi & Manduchi, 1998; Durand & Dorsey, 2002).  Simple non-linear edge-preserving filter with kernels in space and intensity domains. Note: 2 user parameters.

99 99 Bilateral Filter space intensity Normally use log L, but this is unbounded. We prefer to use a sigmoid (based on human vision)

100 100 Bilateral Filter: 3 main functions  1. De-noising depth-map  Intuition: depths are generally smoother than intensities in the real world.  2. Selectively enhance or remove textures for embossed effect

101 101 Bilateral Filter: 3 main functions  3. Shape-from-silhouette, like level-sets shape ‘inflation’ (e.g. Williams, 1998)  Intuition: values outside object are set to zero, so blurring across boundary makes recovered depths smooth and convex.

102 102 Forgiving case  Diffuse surface reflectance leads to clear shading pattern  Silhouette provides good constraints original reconstructed depths

103 103 Difficult case  Strong highlights create large spurious depth peaks  Silhouette is relatively uninformative original reconstructed depths

104 104 Light from the side  Shadows and intensity gradient leads to substantial distortions of the face original reconstructed depths

105 105 Importance of viewpoint correct viewpoint  Substantial errors in depth reconstruction are not visible in transformed image transformed image

106 106 Importance of viewpoint

107 107 Importance of viewpoint

108 108 Importance of viewpoint

109 109 Importance of viewpoint

110 110 Importance of viewpoint

111 111 Importance of viewpoint

112 112 Why does it work ?  Generic viewpoint assumption (Koenderink & van Doorn, 1979; Binford, 1981; Freeman, 1994)

113 113 Why does it work ?  Bas-relief ambiguity Belhumeur, Kriegman & Yuille (1999)  Affine transformed versions of scene are indistinguishable  Particularly important for objects illuminated from the side

114 114 Why does it work ? frontside

115 115 Why does it work ?  Masking effect of patterns that are subsequently placed on surface (e.g. highlights).

116 116 From depths to surface normals Note: 1 user parameter (number of iterations)  Take the derivates.  Reshape them recursively.  Cross product of derivates gives surface normal

117 117 Processing Pipeline texture mapping output image bilateral filter hole filling environment depth map alpha matte segmentation

118 118 Processing Pipeline texture mapping output image bilateral filter hole filling environment depth map alpha matte segmentation

119 119 Re-texturing the object  Use recovered surface normals as indices into a texture map  The most important trick: blend original intensities back into image, for correct shading and highlights

120 120 Re-texturing the object  Use recovered surface normals as indices into a texture map  The most important trick: blend original intensities back into image, for correct shading and highlights

121 121 Re-texturing the object  The most important trick: blend original intensities back into image, for correct shading and highlights  TextureShop (Fang & Hart, 2004) tacitly uses the same trick:

122 122 Other material transformations  To create the illusion of transparent or translucent materials, we map a modified version of the background image onto the surface.  To apply arbitrary BRDFs, we use  the recovered surface normals, and  an approximate reconstruction of the environment to evaluate the BRDF.

123 123 Processing Pipeline texture mapping output image bilateral filter hole filling environment depth map alpha matte segmentation

124 124 Processing Pipeline texture mapping output image bilateral filter hole filling environment depth map alpha matte segmentation

125 125 Hole filling the easy way  A number of sophisticated algorithms exist for object removal (e.g. Bertalmio et al. 2000; Drori et al. 2003; Sun et al )  Crude but fast alternative: cut and paste!

126 126 Hole filling the easy way  A number of sophisticated algorithms exist for object removal (e.g. Bertalmio et al. 2000; Drori et al. 2003; Sun et al )  Crude but fast alternative: cut and paste!

127 127 Hole filling the easy way  A number of sophisticated algorithms exist for object removal (e.g. Bertalmio et al. 2000; Drori et al. 2003; Sun et al )  Crude but fast alternative: cut and paste!

128 128 Fake transparency  The human visual system appears does not recognize transparency by correctly using inverse optics.  Instead, it seems to rely on the consistency of the image statistics and patterns of distortion.

129 129 Fake translucency  To create frosted glass, simply blur the background before mapping it onto the surface

130 130 Fake translucency  For translucency, blur and colour correct  Reinhard et al. (2001) colour transfer

131 131 Fake translucency

132 132 Fake translucency

133 133 Environment map  Background image is used to generate full HDR light probe for image-based lighting

134 134 Arbitrary BRDFs  Given surface normals and complete HDR light probe, we can evaluate empirical or parametric BRDFs, as in standard image based lighting (local effects only).  We used Matusik’s BRDFs. original blue metallic nickel

135 135 Arbitrary BRDFs  Given surface normals and complete HDR light probe, we can evaluate empirical or parametric BRDFs, as in standard image based lighting (local effects only).  We used Matusik’s BRDFs. original nickel

136 136 How important is HDR ?  HDR makes the procedure much more stable  Depth estimates are superior  Easier to identify (and selectively manipulate) highlights

137 137 How important is HDR ?  HDR makes the procedure much more stable  Depth estimates are superior  Easier to identify (and selectively manipulate) highlights originalboostedsuppressed

138 138 General Principles  Complementary heuristics. Normally, errors accumulate as one approximation is added to another. The key to our approach is choosing heuristics such that the errors of one approximation visually compensate for the errors of another.  Exploit visual tolerance for ambiguities to achieve solutions that are perceptually acceptable even though they are physically wrong.

139 139 Using shape to infer material properties

140 140 Using shape to infer material properties Photo: Ted Adelson

141 141 Thank You Funding: DFG FL 624/1-1 Some renderings generated using Henrik Wann Jensen’s DALI Co-authors: Ted Adelson, Ron Dror, Frank Jäkel, Larry Maloney, Erum Kahn, Erik Reinhard, Heinrich Bülthoff

142 142 Stability across extrinsic scene variables Physical RI Perceptual scale distance to background pebble thickness

143 143 Contrast as a cue to RI Refractive index Variance Mean

144 144 Contrast as a cue to RI Scene variable Variance Mean thickness backplane distance view distance

145 145 Variability across subjects distance to backplane thickness of pebble Subject 1 Subject 2

146 146 Binocular cues  Matched features generally do not specify the correct stereo depth of the surface: specularities and features visible through object  Disparity field can contain large spurious vertical disparities and unmatchable features, leading to rivalry  Extent of rivalry could in fact be a cue to RI (cf. binocular ‘lustre’).

147 147 Binocular cues  Matched features generally do not specify the correct stereo depth of the surface: specularities and features visible through object  Disparity field can contain large spurious vertical disparities and unmatchable features, leading to rivalry  Extent of rivalry could in fact be a cue to RI (cf. binocular ‘lustre’).

148 148 Refraction vs. Reflection glassmirror  Both reflection and refraction work by mapping features from the surroundings into the image  But: different mapping rules

149 149 displacement field Distortion Fields  Measures the way the transparent object displaces the position of refracted features in the image.

150 150 Real transparent objects  … are not ideal infinitesimal films  … obey Fresnel’s equations:  Specular reflections  Refraction  … can appear vividly transparent without containing the image cues traditionally believed to be important for the perception of “transparency”.

151 151 Real transparent objects  Questions:  What image cues do we use to tell that something is transparent?  How do we estimate and represent the refractive index of transparent bodies?

152 152 The visual brain  More than a third of the brain contains cells that process visual signals.  Literally billions of neurons are dedicated to the task of seeing. Why? Is vision really so complicated?

153 153

154 154 Statistics of typical Illumination (Dror et al. 2004)  Properties based on raw luminance values  High dynamic range  Pixel histogram heavily skewed towards low intensities  Quasi-local and Non-local properties  Nearby pixels are correlated in intensity (roughly 1/f amplitude spectrum)  Distributions of wavelet coefficients are highly kurtotic (i.e. significant wavelet coefficients are sparse)  Approximate scale invariance (i.e. distributions of wavelet coefficients are similar at different scales)  Global and Non-stationary properties  Dominant direction of illumination  Presence of recognizable objects such objects and trees  Cardinal axes (due to ground plane and perpendicular structures erected thereupon).

155 155 What is the perceptual scale of refractive index ? ? ? Physical RI Perceived RI

156 156 Maximum Likelihood Difference Scaling A B

157 157 Maximum Likelihood Difference Scaling  nCk image permutations:  n=10  k=4  nCk=210 quadruples estimate scale that best accounts for similarity judgments Refractive Index A B

158 158 Maximum Likelihood Difference Scaling Physical RI Perceptual scale

159 159 Maximum Likelihood Difference Scaling Physical RI Perceptual scale RMS image differences perceptual scale

160 160 Physical RI Distortion Magnitude Distortion Fields

161 161 Physical RI Perceptual scale RMS image differences perceptual scale Distortion Fields mean distortion

162 162 Surface Reflectance Matching TestMatch METHOD

163 163 Ward model  Specular Reflectance  matte to glossy  Roughness  crisp to blurred  Axes rescaled to form a psychophysically uniform space  (Pellacini et al. 2000) Roughness Specular Reflectance

164 164 Real-world Illuminations Illuminations downloaded from: http//graphics3.isi.edu/~debevec/Probes Beach BuildingCampus Eucalyptus Uffizi St. Peter’s KitchenGrace

165 165 Match Illumination Galileo

166 166 Artificial Illuminations Single point source Multiple point sources Extended source Gaussian White Noise Gaussian Pink Noise

167 167 Subject: RF. (110 observations) Illumination: “St. Peter’s”. Specular reflectance Surface roughness Subject: MS. (110 observations) Illumination: “Grace”. Subject: RA. (110 observations) Illumination: “Eucalyptus”. Specular reflectance Surface roughness Match matte shiny matteshiny Test Match matte shiny Match matte shiny matteshiny Test matteshiny Test smoothrough Test smoothrough Test smoothrough Test Match smooth rough Match smooth rough Match smooth rough Subjects can match surface reflectance

168 168 Specular reflectance Surface roughness roughsmooth matteshiny Data pooled across all subjects and all real-world illuminations Test Match Test Match Subjects can match surface reflectance

169 169 Real-world vs Artificial Illumination error Illumination map Specular reflectance Roughness

170 170 Noise is unlike real-world illumination Uffizi White Noise Pink Noise match Specular contrast Least accurate of all real-world illuminations Specular contrast

171 171 What are the relevant statistics? RealExtended1/f Noise

172 172 Real-world Illuminations BeachCampus Single point source Gaussian Pink Noise

173 173 Material-ism

174 174 Materials in Computer Graphics Nvidia advanced skin rendering demo  Hollywood and the games industry know that photorealism means getting materials right No subsurface scattering With subsurface scattering

175 175 Edibility

176 176 Edibility

177 177 Edibility

178 178 Edibility

179 179 algae


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