Under typical viewing conditions, human observers effortlessly recognize materials and infer their physical, functional, and multisensory properties at a glance. Without touching materials, we can usually tell whether they would feel hard or soft, rough or smooth, wet or dry. We have vivid visual intuitions about how deformable materials like liquids or textiles respond to external forces and how surfaces like chrome, wax, or leather change appearance when formed into different shapes or viewed under different lighting. These achievements are impressive because the retinal image results from complex optical interactions between lighting, shape, and material, which cannot easily be disentangled. Here I argue that because of the diversity, mutability, and complexity of materials, they pose enormous challenges to vision science: What is material appearance, and how do we measure it? How are material properties estimated and represented? Resolving these questions causes us to scrutinize the basic assumptions of mid-level vision.


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Literature Cited

  1. Adams WJ, Elder JH. 2014. Effects of specular highlights on perceived surface convexity. PLOS Comput. Biol. 10:5e1003576 [Google Scholar]
  2. Adelson EH. 2001. On seeing stuff: the perception of materials by humans and machines. Proc. SPIE 4299:1 [Google Scholar]
  3. Adelson EH, Anandan P. 1990. Ordinal characteristics of transparency. Proc. AAAI-90 Workshop Qual. Vis., Boston, MA77–81
  4. Aliaga C, O'Sullivan C, Gutiérrez D, Tamstorf R. 2015. Sackcloth or silk? The impact of appearance versus dynamics on the perception of animated cloth Presented at ACM Symp. Appl. Percept., Sept. 13–14 Tübingen, Ger.:
  5. Anderson BL. 1997. A theory of illusory lightness and transparency in monocular and binocular images: the role of contour junctions. Perception 26:419–53 [Google Scholar]
  6. Anderson BL, Kim J. 2009. Image statistics do not explain the perception of gloss and lightness. J. Vis. 9:1110 [Google Scholar]
  7. Ashikhmin M, Shirley P. 2000. An anisotropic phong BRDF model. J. Graph. Tools 5:225–32 [Google Scholar]
  8. ASTM. 1997. Standard Test Methods for Measurement of Gloss of High-Gloss Surfaces by Goniophotometry (Standard No. E430–97) West Conshohocken, PA: Am. Soc. Test. Mater.
  9. Badami I. 2012. Material recognition: Bayesian inference or SVMs?. Proc. Semin. Comput. Graph. (CESCG) 2012, Smolenice, Slovak.133–40
  10. Bates CJ, Yildirim I, Tenenbaum JB, Battaglia PW. 2015. Humans predict liquid dynamics using probabilistic simulation. Proc. 37th Annu. Meet. Cogn. Sci. Soc., Pasadena, CA172–77
  11. Battaglia PW, Hamrick JB, Tenenbaum JB. 2013. Simulation as an engine of physical scene understanding. PNAS 110:4518327–32 [Google Scholar]
  12. Beck J, Ivry R. 1988. On the role of figural organization in perceptual transparency. Percept. Psychophys. 44:585–94 [Google Scholar]
  13. Beck J, Prazdny S. 1981. Highlights and the perception of glossiness. Percept. Psychophys. 30:4407–10 [Google Scholar]
  14. Bell S, Upchurch P, Snavely N, Bala K. 2015. Material recognition in the wild with the materials in context database. Proc. 2015 IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA3479–87
  15. Ben-Shahar O, Zucker S. 2001. On the perceptual organization of texture and shading flows: from a geometrical model to coherence computation. Proc. 2001 IEEE Conf. Comput. Vis. Pattern Recognit., Kauai, HI1048–55
  16. Blake A, Bülthoff H. 1990. Does the brain know the physics of specular reflection?. Nature 343:165–68 https://doi.org/10.1038/343165a0 [Crossref] [Google Scholar]
  17. Bouman KL, Xiao B, Battaglia P, Freeman WT. 2013. Estimating the material properties of fabrics from videos. Proc. 2013 IEEE Int. Conf. Comput. Vis., Sydney, Aust.1984–91
  18. Cant JS, Goodale MA. 2007. Attention to form or surface properties modulates different regions of human occipitotemporal cortex. Cereb. Cortex 17:713–31 https://doi.org/10.1093/cercor/bhk022 [Crossref] [Google Scholar]
  19. Cant JS, Goodale MA. 2011. Scratching beneath the surface: new insights into the functional properties of the lateral occipital area and parahippocampal place area. J. Neurosci. 31:8248–58 https://doi.org/10.1523/JNEUROSCI.6113-10.2011 [Crossref] [Google Scholar]
  20. Chadwick AC, Kentridge RW. 2015. The perception of gloss: a review. Vis. Res. 1092015:221–35 https://doi.org/10.1016/j.visres.2014.10.026 [Crossref] [Google Scholar]
  21. Cichy RM, Khosla A, Pantazis D, Torralba A, Oliva A. 2016. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci. Rep. 6:27755 [Google Scholar]
  22. Cook RL, Torrance KE. 1982. A reflectance model for computer graphics. ACM Trans. Graph. 1:17–24 [Google Scholar]
  23. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. 2009. ImageNet: a large-scale hierarchical image database. Proc. 2009 IEEE Conf. Comput. Vis. Pattern Recognit., Miami, FL248–55
  24. Denil M, Agrawal P, Kulkarni TD, Erez T, Battaglia PW, de Freitas N. 2016. Learning to perform physics experiments via deep reinforcement learning. arXiv 1611.01843
  25. Doerschner K, Boyaci H, Maloney LT. 2010. Estimating the glossiness transfer function induced by illumination change and testing its transitivity. J. Vis. 10:48 https://doi.org/10.1167/10.4.8 [Crossref] [Google Scholar]
  26. Doerschner K, Fleming RW, Yilmaz O, Schrater PR, Hartung B, Kersten D. 2011. Visual motion and the perception of surface material. Curr. Biol. 21:231–7 [Google Scholar]
  27. Ernst MO, Bülthoff HH. 2004. Merging the senses into a robust percept. Trends Cogn. Sci. 8:4162–69 [Google Scholar]
  28. Fabre-Thorpe M, Delorme A, Marlot C, Thorpe S. 2001. A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. J. Cogn. Neurosci. 13:2171–80 [Google Scholar]
  29. Faul F, Ekroll V. 2011. On the filter approach to perceptual transparency. J. Vis. 11:77 [Google Scholar]
  30. Ferrero A, Bayon S. 2015. The measurement of sparkle. Metrologia 52:317–23 [Google Scholar]
  31. Fleming RW. 2012. Human perception: visual heuristics in the perception of glossiness. Curr. Biol. 22:20R865–66 [Google Scholar]
  32. Fleming RW. 2014. Visual perception of materials and their properties. Vis. Res. 94:62–75 [Google Scholar]
  33. Fleming RW, Bülthoff HH. 2005. Low-level image cues in the perception of translucent materials. ACM Trans. Appl. Percept. 2:3346–82 [Google Scholar]
  34. Fleming RW, Dror RO, Adelson EH. 2003. Real-world illumination and the perception of surface reflectance properties. J. Vis. 3:53 [Google Scholar]
  35. Fleming RW, Gegenfurtner KR, Nishida S. 2015. Visual perception of materials: the science of stuff. Vis. Res. 109:123–24 [Google Scholar]
  36. Fleming RW, Jäkel F, Maloney LT. 2011. Visual perception of thick transparent materials. Psychol. Sci. 22:6812–20 [Google Scholar]
  37. Fleming RW, Torralba A, Adelson EH. 2004. Specular reflections and the perception of shape. J. Vis. 4:910 https://doi.org/10.1167/4.9.10 [Crossref] [Google Scholar]
  38. Fleming RW, Torralba A, Adelson EH. 2009. Shape from sheen MIT Comput. Sci. Artif. Intell. Lab. Tech. Rep. MIT-CSAIL-TR-2009–051 MIT Cambridge, MA:
  39. Fleming RW, Wiebel C, Gegenfurtner K. 2013. Perceptual qualities and material classes. J. Vis. 13:89 https://doi.org/10.1167/13.8.9 [Crossref] [Google Scholar]
  40. Gatys LA, Ecker AS, Bethge M. 2014. Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks. arXiv 1505.07376 [cs, q-bio]
  41. Gatys LA, Ecker AS, Bethge M. 2015. A neural algorithm of artistic style. arXiv 1508.06576v2 [cs.CV]
  42. Gerbino W. 1994. Achromatic transparency. Lightness, Brightness and Transparency AL Gilchrist 215–55 Hove, UK: Erlbaum [Google Scholar]
  43. Gkioulekas I, Walter B, Adelson EH, Bala K, Zickler T. 2015. On the appearance of translucent edges. Proc. 2015 IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA5528–36
  44. Hamrick JB, Battaglia PW, Griffiths TL, Tenenbaum JB. 2016. Inferring mass in complex scenes by mental simulation. Cognition 157:61–76 [Google Scholar]
  45. He K, Zhang X, Ren S, Sun J. 2015. Deep residual learning for image recognition. arXiv 1512:03385 [Google Scholar]
  46. Helmholtz H. 1867 (1962). Helmholtz's Treatise on Physiological Optics III JPC Southall Mineola, NY: Dover
  47. Ho Y-X, Landy MS, Maloney LT. 2008. Conjoint measurement of gloss and surface texture. Psychol. Sci. 19:2196–204 [Google Scholar]
  48. Horn BK, Brooks MJ. 1986. The variational approach to shape from shading. Comput. Vis. Graph. Image Process. 33:2174–208 [Google Scholar]
  49. Huggins PS, Chen HF, Belhumeur PN, Zucker SW. 2001. Finding folds: on the appearance and identification of occlusion. Proc. 2001 IEEE Conf. Comput. Vis. Pattern Recognit., Kauai, HI718–25 [Google Scholar]
  50. Hunter RS. 1937. Methods of determining gloss. J. Res. Natl. Bur. Stand. 18:119–41 [Google Scholar]
  51. Hunter RS, Harold RW. 1987. The Measurement of Appearance New York: Wiley, 2nd ed..
  52. Ikeuchi K, Horn BK. 1981. Numerical shape from shading and occluding boundaries. Artif. Intell. 17:1–3141–84 [Google Scholar]
  53. Ingersoll LR. 1921. The Glarimeter: an instrument for measuring the gloss of paper. J. Opt. Soc. Am. 5:3213–15 https://doi.org/10.1364/josa.5.000213 [Crossref] [Google Scholar]
  54. Jacobs RHA, Baumgartner E, Gegenfurtner KR. 2014. The representation of material categories in the brain. Front. Psychol. 5:1461664–78 [Google Scholar]
  55. Jensen HW. 2001. A practical model of sub-surface transport. Proc. SIGGRAPH 2001, 28th Annu. Conf. Comput. Graph. Interact. Tech., Los Angeles, CA511–18
  56. Judd DB. 1937. Gloss and glossiness (a tentative outline of concepts, definitions and terminology). Am. Dyestuff Rep. 26:234–35 [Google Scholar]
  57. Kawabe T, Maruya K, Fleming RW, Nishida S. 2015. Seeing liquids from visual motion. Vis. Res. 109:125–38 [Google Scholar]
  58. Kerrigan IS, Adams WJ. 2013. Highlights, disparity, and perceived gloss with convex and concave surfaces. J. Vis. 13:19 https://doi.org/10.1167/13.1.9 [Crossref] [Google Scholar]
  59. Kim J, Anderson BL. 2010. Image statistics and the perception of surface gloss and lightness. J. Vis. 10:93 [Google Scholar]
  60. Kim J, Marlow P, Anderson BL. 2011. The perception of gloss depends on highlight congruence with surface shading. J. Vis. 11:94 [Google Scholar]
  61. Koenderink J, Pont S. 2003. The secret of velvety skin. Mach. Vis. Appl. 14:4260–68 [Google Scholar]
  62. Koenderink JJ, van Doorn AJ. 1980. Photometric invariants related to solid shape. J. Mod. Opt. 27:7981–96 [Google Scholar]
  63. Krizhevsky A, Sutskever I, Hinton GE. 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 F Pereira, C Burges, L Bottou, K Weinberger 1097–105 La Jolla, CA: NIPS [Google Scholar]
  64. Lafortune EPF, Foo S-C, Torrance KE, Greenberg DP. 1997. Non-linear approximation of reflectance functions. Proc. SIGGRAPH 97, 24th Annu. Conf. Comput. Graph. Interact. Tech., Los Angeles, CA117–26 [Google Scholar]
  65. Landy MS, Maloney LT, Johnston EB, Young M. 1995. Measurement and modeling of depth cue combination: in defense of weak fusion. Vis. Res. 35:3389–412 [Google Scholar]
  66. Lin T-S, Maji S. 2015. Visualizing and understanding deep texture representations. arXiv 1511.05197 [cs.CV]
  67. Liu C, Sharan L, Rosenholtz R, Adelson EH. 2010. Exploring features in a Bayesian framework for material recognition. Proc. 2010 IEEE Conf. Comput. Vis. Pattern Recognit., San Francisco, CA239–46 https://doi.org/10.1109/CVPR.2010.5540207 [Crossref]
  68. Mahendran A, Vedaldi A. 2015. Understanding deep image representations by inverting them. Proc. 2015 IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA5188–96
  69. Maloney LT, Yang JN. 2003. Maximum likelihood difference scaling. J. Vis. 3:85 [Google Scholar]
  70. Marlow PJ, Anderson BL. 2013. Generative constraints on image cues for perceived gloss. J. Vis. 13:142 [Google Scholar]
  71. Marlow PJ, Anderson BL. 2015. Material properties derived from three-dimensional shape representations. Vis. Res. 115:199–208 [Google Scholar]
  72. Marlow PJ, Anderson BL. 2016. Motion and texture shape cues modulate perceived material properties. J. Vis. 16:15 https://doi.org/10.1167/16.1.5 [Crossref] [Google Scholar]
  73. Marlow PJ, Kim J, Anderson BL. 2011. The role of brightness and orientation congruence in the perception of surface gloss. J. Vis. 11:91–12 [Google Scholar]
  74. Marlow PJ, Kim J, Anderson BL. 2012. The perception and misperception of specular reflectance. Curr. Biol. 22:1909–13 [Google Scholar]
  75. Marlow PJ, Kim J, Anderson B. 2016. Coupled computations of 3D shape and translucency. J. Vis. 16:12947 https://doi.org/10.1167/16.12.947 [Crossref] [Google Scholar]
  76. Marlow PJ, Todorovic D, Anderson BL. 2015. Coupled computations of 3D shape and material. Curr. Biol. 25:6R221–22 [Google Scholar]
  77. Matusik W, Pfister H, Brand M, McMillan L. 2003. A data-driven reflectance model. ACM Trans. Graph. 22:3759–69 https://doi.org/10.1145/1201775.882343 [Crossref] [Google Scholar]
  78. Metelli F. 1970. An algebraic development of the theory of perceptual transparency. Ergonomics 13:59–66 [Google Scholar]
  79. Metelli F. 1974a. Achromatic color conditions in the perception of transparency. Perception RB Macleod, HL Pick 95–116 Ithaca, NY: Cornell Univ. Press [Google Scholar]
  80. Metelli F. 1974b. The perception of transparency. Sci. Am. 230:90–98 [Google Scholar]
  81. Mori M. 1970. Bukimi no tani [The uncanny valley]. Energy 7:33–35 [Google Scholar]
  82. Motoyoshi I, Matoba H. 2012. Variability in constancy of the perceived surface reflectance across different illumination statistics. Vis. Res. 53:130–39 https://doi.org/10.1016/j.visres.2011.11.010 [Crossref] [Google Scholar]
  83. Motoyoshi I, Nishida S, Adelson EH. 2005. Luminance re-mapping for the control of apparent material. Proc. Second Symp. Appl. Percept. Graph. Vis., La Coruña, Spain165
  84. Motoyoshi I, Nishida SY, Sharan L, Adelson EH. 2007. Image statistics and the perception of surface qualities. Nature 447:7141206–9 [Google Scholar]
  85. Muryy AA, Fleming RW, Welchman AE. 2014. Key characteristics of specular stereo. J. Vis. 14:1414 https://doi.org/10.1167/14.14.14 [Crossref] [Google Scholar]
  86. Muryy AA, Fleming RW, Welchman AE. 2016. ‘Proto-rivalry’: how the binocular brain identifies gloss. Proc. R. Soc. B 283:20160383 https://doi.org/10.1098/rspb.2016.0383 [Crossref] [Google Scholar]
  87. Muryy A, Welchman AE, Blake A, Fleming RW. 2013. Specular reflections and the estimation of shape from binocular disparity. PNAS 110:62413–18 https://doi.org/10.1073/pnas.1212417110 [Crossref] [Google Scholar]
  88. Ngan A, Durand F, Matusik W. 2005. Experimental analysis of BRDF models. Proc. Sixteenth Eurographics Conf. Rendering Tech., Konstanz, Ger.117–26
  89. Ngan A, Durand F, Matusik W. 2006. Image-driven navigation of analytical BRDF models. Proc. Seventeenth Eurographics Conf. Rendering Tech., Cyprus399–407
  90. Nicodemus F. 1965. Directional reflectance and emissivity of an opaque surface. Appl. Opt. 4:7767–75 [Google Scholar]
  91. Nicodemus FE, Richmond JC, Hsia JJ, Ginsberg IW, Limperis T. 1977. Geometrical considerations and nomenclature for reflectance Washington, DC: Natl. Bur. Stand.
  92. Norman JF, Todd JT, Orban GA. 2004. Perception of three-dimensional shape from specular highlights, deformations of shading, and other types of visual information. Psychol. Sci. 15:565–70 [Google Scholar]
  93. Nusseck M, Fleming RW, Langarde J, Bardy B, Bülthoff HH. 2007. Perception and prediction of simple object interactions. Proc. 4th Symp. Appl. Percept. Graph. Vis., Tübingen, Ger.27–34
  94. Okazawa G, Tajima S, Komatsu H. 2015. Image statistics underlying natural texture selectivity of neurons in macaque V4. PNAS 112:E351–60 https://doi.org/10.1073/pnas.1415146112 [Crossref] [Google Scholar]
  95. Okazawa G, Tajima S, Komatsu H. 2016. Gradual development of visual texture-selective properties between macaque areas V2 and V4. Cereb. Cortex 2016:1–14 https://doi.org/10.1093/cercor/bhw282 [Crossref] [Google Scholar]
  96. Olkkonen M, Brainard DH. 2010. Perceived glossiness and lightness under real-world illumination. J. Vis. 10:95 https://doi.org/10.1167/10.9.5 [Crossref] [Google Scholar]
  97. Olkkonen M, Brainard DH. 2011. Joint effects of illumination geometry and object shape in the perception of surface reflectance. i-Perception 2:91014 [Google Scholar]
  98. Paulun VC, Gegenfurtner KR, Goodale MA, Fleming RW. 2016. Effects of material properties and object orientation on precision grip kinematics. Exp. Brain Res. 234:2253–65 [Google Scholar]
  99. Paulun VC, Kawabe T, Nishida S, Fleming RW. 2015. Seeing liquids from static snapshots. Vis. Res. 115:163–74 https://doi.org/10.1016/j.visres.2015.01.023 [Crossref] [Google Scholar]
  100. Paulun VC, Schmidt F, van Assen JJR, Fleming RW. 2017. Shape, motion and optical cues to stiffness of elastic objects. J. Vis. 17:120 [Google Scholar]
  101. Pont SC, Koenderink JJ, van Doorn AJ, Wijntjes MWA, te Pas SF. 2012. Mixing material modes. Proc. SPIE 8291:82910D https://doi.org/10.1117/12.916450 [Crossref] [Google Scholar]
  102. Pont SC, te Pas SF. 2006. Material-illumination ambiguities and the perception of solid objects. Perception 35:101331 [Google Scholar]
  103. Read JCA, Phillipson GP, Glennerster A. 2009. Latitude and longitude vertical disparities. J. Vis. 9:1311 [Google Scholar]
  104. Robilotto R, Zaidi Q. 2004. Perceived transparency of neutral density filters across dissimilar backgrounds. J. Vis. 4:35 [Google Scholar]
  105. Schlüter N, Faul F. 2014. Are optical distortions used as a cue for material properties of thick transparent objects. J. Vis. 14:142 https://doi.org/10.1167/14.14.2 [Crossref] [Google Scholar]
  106. Schwartz G, Nishino K. 2013. Visual material traits: recognizing per-pixel material context. Proc. 2013 IEEE Int. Conf. Comput. Vis. Workshop, Sydney, Aust.883–90
  107. Schwartz G, Nishino K. 2015. Automatically discovering local visual material attributes. Proc. 2015 IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA3565–73
  108. Sharan L, Liu C, Rosenholtz R, Adelson EH. 2013. Recognizing materials using perceptually inspired features. Int. J. Comput. Vis. 103:3348–71 [Google Scholar]
  109. Sharan L, Rosenholtz R, Adelson EH. 2009. Material perception: What can you see in a brief glance. ? J. Vis. 9:812 https://doi.org/10.1167/9.8.784 [Crossref] [Google Scholar]
  110. Sharan L, Rosenholtz R, Adelson EH. 2014. Accuracy and speed of material categorization in real-world images. J. Vis. 14:912 https://doi.org/10.1167/14.9.12 [Crossref] [Google Scholar]
  111. Singh M, Anderson BL. 2002. Toward a perceptual theory of transparency. Psychol. Rev. 109:492–519 [Google Scholar]
  112. Thorpe S, Fize D, Marlot C. 1996. Speed of processing in the human visual system. Nature 381:6582520–22 [Google Scholar]
  113. Todd JT, Norman JF, Koenderink JJ, Kappers AML. 1997. Effects of texture, illumination and surface reflectance on stereoscopic shape perception. Perception 26:806–22 [Google Scholar]
  114. Todd JT, Norman JF, Mingolla E. 2004. Lightness constancy in the presence of specular highlights. Psychol. Sci. 15:133–39 https://doi.org/10.1111/j.0963-7214.2004.01501006.x [Crossref] [Google Scholar]
  115. Twardy CR, Bingham GP. 2002. Causation, causal perception, and conservation laws. Percept. Psychophys. 64:6956–68 https://doi.org/10.3758/BF03196799 [Crossref] [Google Scholar]
  116. van Assen JJ, Barla P, Fleming RW. 2016. Cues underlying liquid constancy. J. Vis. 16:12946 https://doi.org/10.1167/16.12.946 [Crossref] [Google Scholar]
  117. van Assen JJR, Fleming RW. 2016. Influence of optical material properties on the perception of liquids. J. Vis. 16:1512 [Google Scholar]
  118. Vangorp P, Laurijssen J, Dutré P. 2007. The influence of shape on the perception of material reflectance. ACM Trans. Graph. 26:77 [Google Scholar]
  119. Vergne R, Barla P, Fleming RW, Granier X. 2012. Surface flows for image-based shading design. ACM Trans. Graph. 31:494 https://doi.org/10.1145/2185520.2185590 [Crossref] [Google Scholar]
  120. Ward GJ. 1992. Measuring and modeling anisotropic reflection. ACM SIGGRAPH Comput. Graph. 26:2265–72 [Google Scholar]
  121. Warren WH, Kim EE, Husney R. 1987. The way the ball bounces: visual and auditory perception of elasticity and control of the bounce pass. Perception 16:309–36 [Google Scholar]
  122. Weir PL, MacKenzie CL, Marteniuk RG, Cargoe SL. 1991. Is object texture a constraint on human prehension? Kinematic evidence. J. Mot. Behav. 23:205–10 [Google Scholar]
  123. Wendt G, Faul F, Ekroll V, Mausfeld R. 2010. Disparity, motion, and color information improve gloss constancy performance. J. Vis. 10:97 [Google Scholar]
  124. Wendt G, Faul F, Mausfeld R. 2008. Highlight disparity contributes to the authenticity and strength of perceived glossiness. J. Vis. 8:114 [Google Scholar]
  125. Wenyan B, Xiao B. 2016. Perceptual constancy of mechanical properties of cloth under variation of external force. Proc. ACM Symp. Appl. Percept., Anaheim, CA19–23
  126. Wichmann FA, Drewes J, Rosas P, Gegenfurtner KR. 2010. Animal detection in natural scenes: critical features revisited. J. Vis. 10:46 [Google Scholar]
  127. Wiebel C, Toscani M, Gegenfurtner KR. 2015. Statistical correlates of perceived gloss in natural images. Vis. Res. 115:B175–87 [Google Scholar]
  128. Wieschollek P, Lensch HPA. 2016. Transfer learning for material classification using convolutional networks. arXiv 1609.06188v1 [cs.CV]
  129. Xiao B, Brainard DH. 2008. Surface gloss and color perception of 3D objects. Vis. Neurosci. 25:3371 [Google Scholar]
  130. Xiao B, Gkioulekas I, Adelson EH, Zickler T, Bala K. 2014. Looking against the light: how perception of translucency depends on lighting direction. J. Vis. 14:317 [Google Scholar]
  131. Zhang F, de Ridder H, Fleming RW, Pont S. 2016. MatMix 1.0: using optical mixing to probe visual material perception. J. Vis. 16:611 https://doi.org/10.1167/16.6.11 [Crossref] [Google Scholar]

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