Binocular cues12/17/2023 ![]() This approach is analogous to processes that alter the interpretation of scene lightness based on scene layout ( 14– 16). Thus, ancillary markers could indicate that a specular reflection model applies and therefore that disparities should be interpreted using “knowledge of the physics” of specular reflection. In particular, there are believed to be several monocular cues that indicate surface specularity such as ( i) the distribution of image intensities, ( ii) the elongation of image features ( 10, 11), and ( iii) patterns of motion ( 12) and color ( 13). The process of identifying a surface as specular could exploit ancillary markers ( 4) (i.e., nonstereoscopic information that the surface is shiny), which alter the interpretation of disparities. On this basis, once a disparity is identified as originating from specular reflection, the brain could apply specific computations to infer the true surface from the reflection. First, it has been suggested that the brain “knows” the physics of specular reflections ( 1). ![]() ![]() How does human vision recover the depth of these specular objects?īroadly speaking, there are two general approaches that the visual system might use to identify and overcome the spurious binocular information from specular reflections. Most models of biological vision place heavy weight on binocular disparity cues, whereas artificial systems often rely almost exclusively upon them. However, for more complex shapes, such as a polished metal kettle, we rarely encounter problems judging shape. 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. 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. 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. Unlike shading or texture markings, the positions of reflections relative to a specular (shiny) surface depend on the observer’s viewpoint. 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. Shiny objects such as sports cars, jewelry, and consumer electronics can be beautiful to look at. 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. 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. 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. We presented participants with binocular views of specular objects and asked them to report perceived depths by adjusting probe dots. 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. One possibility is that the brain uses monocular cues to identify that a surface is specular and modifies its interpretation of the disparities accordingly. Here, we address the question of how the visual system identifies the disparity information created by specular reflections. 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. This works well for matte surfaces because disparities indicate true surface locations. To exploit it, the brain matches features from the two eyes’ views and measures their interocular disparity. Binocular stereopsis is a powerful visual depth cue.
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