Using rendered images, most previous works have measured translucent appearance using a single task (e.g. But translucent appearance is difficult to be verbally described and is likely to be high dimensional. Humans can often report whether a surface is glossy or matte with verbal rating, even though perception of gloss is multidimensional ( Ferwerda, Pellacini, & Greenberg, 2001). Even though these materials can be all described as “translucent,” humans have no trouble visually discriminating them. skin versus wax) have different physical generative processes. Second, there are many different kinds of translucent materials in real life. Previous work shows that physical material properties, lighting, shape, and context all affect the appearance of translucent objects ( Chowdhury, Marlow, & Kim, 2017 Fleming & Bülthoff, 2005 Gigilashvili, Shi, Wang, Pedersen, Hardeberg, & Rushmeier, 2021 Gigilashvili, Thomas, Hardeberg, & Pedersen, 2021 Marlow & Anderson, 2021 Marlow, Kim, & Anderson, 2017 Motoyoshi, 2010 Nagai, Ono, Tani, Koida, Kitazaki, & Nakauchi, 2013 Sawayama, Dobashi, Okabe, Hosokawa, Koumura, Saarela, et al., 2019 Tamura, Higashi, & Nakauchi, 2018 Xiao et al., 2014 Xiao, Zhao, Gkioulekas, Bi, & Bala, 2020), and it still remains unknown how humans extract intrinsic translucent material properties from images. First, the physical process of translucency is complex, involving surface reflection and subsurface scattering (see Gkioulekas, Xiao, Zhao, Adelson, Zickler, & Bala, 2013 Xiao, Walter, Gkioulekas, Zickler, Adelson, & Bala, 2014 for detailed description of physical model of subsurface scattering and translucency perception). Translucency is challenging to study due to several reasons. Yet, in comparison to opaque materials, relatively little is understood about translucent appearance ( Fleming, 2017 Gigilashvili, Thomas, Hardeberg, & Pedersen, 2021). Translucent materials are ubiquitous in life, including skin, teeth, fruits, liquid, crystals, glass, plastics, and wax. Many materials in our daily life are translucent, which allow some of the light to penetrate the object, refract, and scatter multiple times throughout the body of the medium, before exiting from a different location on the surface. We also discuss individual differences in our results and highlight the importance of such consideration in material perception. Meanwhile, our analysis shows that mid-level semantic estimation of material attributes might be closely related to high-level material recognition. Our result demonstrates that color is informative about material property estimation and recognition. Last, converting images to grayscale alters the perceived material categories for some images such that observers tend to misjudge images of food as non-food and vice versa. Furthermore, ratings of see-throughness, glossiness, and glow could predict individual observers’ binary classification of images in both grayscale and color conditions. We also discover that converting images to grayscale substantially affects the distributions of attribute ratings for some images. Further, there are more disagreements among observers in the grayscale condition in comparison to that in the color condition. We find that observers’ agreements depend on the physical material properties of the objects such that translucent materials generate more interobserver disagreements. Two different groups of observers finish the three tasks with color or grayscale images. Specifically, we use three behavior tasks: binary classification of “translucent” versus “opaque,” semantic attribute rating of perceptual qualities (see-throughness, glossiness, softness, glow, and density), and material categorization. Here, we measure translucency perception with photographs of real-world objects. Previous work in translucency perception has mainly used monochromatic rendered images as stimuli, which are restricted by their diversity and realism. teeth, food, and wax), but our understanding of translucency perception is limited. Translucent materials are ubiquitous in nature (e.g.
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