Real-world illumination and the perception of surface reflectance properties

Fleming, Roland W.; Dror, Ron O.; Adelson, Edward H.

doi:10.1167/3.5.3

Cited by 390 publications

(432 citation statements)

References 23 publications

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“…Shown are ratings for two light probes (those eliciting on average highest and lowest shininess ratings) as well the average data (across all light probes and observers). Differences in relative apparent shininess for different light probes is consistent with previous research [31]. In the experiment observers rated apparent shininess of all 36 light probe-shape combinations.…”

Section: Resultssupporting

confidence: 89%

Rapid classification of specular and diffuse reflection from image velocities

Doerschner

Kersten

Schrater

2011

Pattern Recognition

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a b s t r a c tWe propose a method for rapidly classifying surface reflectance directly from the output of spatiotemporal filters applied to an image sequence of rotating objects. Using image data from only a single frame, we compute histograms of image velocities and classify these as being generated by a specular or a diffusely reflecting object. Exploiting characteristics of material-specific image velocities we show that our classification approach can predict the reflectance of novel 3D objects, as well as human perception.

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Section: Resultssupporting

confidence: 89%

Rapid classification of specular and diffuse reflection from image velocities

Doerschner

Kersten

Schrater

2011

Pattern Recognition

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“…between surfaces free to differ in orientation and location in the kinds of scenes used in the experiments presented here. Equally, it is of interest to assess how judgments of material properties Fleming et al, 2003! vary in such scenes, and conversely, how observers' estimates of spatial characteristics of the light field direction, diffuseness!…”

mentioning

confidence: 99%

Surface color perception in three-dimensional scenes

et al. 2006

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Researchers studying surface color perception have typically used stimuli that consist of a small number of matte patches~real or simulated! embedded in a plane perpendicular to the line of sight~a "Mondrian," Land & McCann, 1971!. Reliable estimation of the color of a matte surface is a difficult if not impossible computational problem in such limited scenes~Maloney, 1999!. In more realistic, three-dimensional scenes the difficulty of the problem increases, in part, because the effective illumination incident on the surface~the light field! now depends on surface orientation and location. We review recent work in multiple laboratories that examines~1! the degree to which the human visual system discounts the light field in judging matte surface lightness and color and~2! what illuminant cues the visual system uses in estimating the flow of light in a scene.

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“…Stimuli were rendered in Matlab under natural illumination [Eucalyptus light probe (38)] where the illumination was treated as arriving from infinite distance (7,39). Specifically, we mapped the illumination map onto the surface of the object using the surface normal vectors and the physical law of specular reflection.…”

Section: Methodsmentioning

confidence: 99%

Specular reflections and the estimation of shape from binocular disparity

Muryy

Welchman

Blake

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Binocular stereopsis is a powerful visual depth cue. To exploit it, the brain matches features from the two eyes' views and measures their interocular disparity. This works well for matte surfaces because disparities indicate true surface locations. However, specular (glossy) surfaces are problematic because highlights and reflections are displaced from the true surface in depth, leading to information that conflicts with other cues to 3D shape. Here, we address the question of how the visual system identifies the disparity information created by specular reflections. One possibility is that the brain uses monocular cues to identify that a surface is specular and modifies its interpretation of the disparities accordingly. However, by characterizing the behavior of specular disparities we show that the disparity signals themselves provide key information ("intrinsic markers") that enable potentially misleading disparities to be identified and rejected. We presented participants with binocular views of specular objects and asked them to report perceived depths by adjusting probe dots. For simple surfaces-which do not exhibit intrinsic indicators that the disparities are "wrong"-participants incorrectly treat disparities at face value, leading to erroneous judgments. When surfaces are more complex we find the visual system also errs where the signals are reliable, but rejects and interpolates across areas with large vertical disparities and horizontal disparity gradients. This suggests a general mechanism in which the visual system assesses the origin and utility of sensory signals based on intrinsic markers of their reliability.psychophysics | perception | gloss | texture | computational analysis S hiny objects such as sports cars, jewelry, and consumer electronics can be beautiful to look at. However, such objects pose a difficult challenge to the visual system: if all (or most) of the light reaching the eye comes from the reflections of other nearby objects, how does the viewer discern the object itself? This problem becomes more acute when viewing with two eyes. Unlike shading or texture markings, the positions of reflections relative to a specular (shiny) surface depend on the observer's viewpoint. This means that when the surface is viewed binocularly (i.e., from two viewpoints at the same time), corresponding reflections fall on different surface locations. In consequence, the binocular disparities created by specular reflections indicate depth positions displaced from the object's physical surface (1, 2) and the 3D shape specified by disparity can be radically different from the true shape of the object. For special cases, such as an ideal planar mirror, the visual system could not, even in principle, estimate the true depths of the surface from the reflections. However, for more complex shapes, such as a polished metal kettle, we rarely encounter problems judging shape. Most models of biological vision place heavy weight on binocular disparity cues, whereas artificial systems often rely almost exclusively ...

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Real-world illumination and the perception of surface reflectance properties

Cited by 390 publications

References 23 publications

Rapid classification of specular and diffuse reflection from image velocities

Rapid classification of specular and diffuse reflection from image velocities

Surface color perception in three-dimensional scenes

Specular reflections and the estimation of shape from binocular disparity

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