Key characteristics of specular stereo

The Perceptual Magic of Binocular Rivalry

Binocular rivalry (BR) refers to the spontaneous, unpredictable fluctuations in visual awareness provoked by dissimilar stimulation of the two eyes. Reports of the phenomenon date back several centuries, but interest in BR has exploded in recent years as researchers in diverse disciplines—psychology, neuroscience, medicine, philosophy—have found reasons to study it. New ideas about BR have emerged, sparking controversies about its neural bases, which may be resolved thanks to new methodological developments. This essay provides a synopsis of some key empirically determined aspects of BR as well as an overview of theoretical developments in this field. Work published during the past decade or so is emphasized (and explicitly referenced); earlier key findings are mentioned and referenced in the annotated bibliography included in the Supplemental Material.

Binocularity and visual search-Revisited

Binocular rivalry is a phenomenon of visual competition in which perception alternates between two monocular images. When two eye's images only differ in luminance, observers may perceive shininess, a form of rivalry called binocular luster. Does dichoptic information guide attention in visual search? Wolfe and Franzel (Perception & Psychophysics, 44(1), 81-93, 1988) reported that rivalry could guide attention only weakly, but that luster (shininess) "popped out," producing very shallow Reaction Time (RT) × Set Size functions. In this study, we have revisited the topic with new and improved stimuli. By using a checkerboard pattern in rivalry experiments, we found that search for rivalry can be more efficient (16 ms/item) than standard, rivalrous grating (30 ms/item). The checkerboard may reduce distracting orientation signals that masked the salience of rivalry between simple orthogonal gratings. Lustrous stimuli did not pop out when potential contrast and luminance artifacts were reduced. However, search efficiency was substantially improved when luster was added to the search target. Both rivalry and luster tasks can produce search asymmetries, as is characteristic of guiding features in search. These results suggest that interocular differences that produce rivalry or luster can guide attention, but these effects are relatively weak and can be hidden by other features like luminance and orientation in visual search tasks.

Material Perception

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.

"What Not" Detectors Help the Brain See in Depth

Binocular stereopsis is one of the primary cues for three-dimensional (3D) vision in species ranging from insects to primates. Understanding how the brain extracts depth from two different retinal images represents a tractable challenge in sensory neuroscience that has so far evaded full explanation. Central to current thinking is the idea that the brain needs to identify matching features in the two retinal images (i.e., solving the “stereoscopic correspondence problem”) so that the depth of objects in the world can be triangulated. Although intuitive, this approach fails to account for key physiological and perceptual observations. We show that formulating the problem to identify “correct matches” is suboptimal and propose an alternative, based on optimal information encoding, that mixes disparity detection with “proscription”: exploiting dissimilar features to provide evidence against unlikely interpretations. We demonstrate the role of these “what not” responses in a neural network optimized to extract depth in natural images. The network combines information for and against the likely depth structure of the viewed scene, naturally reproducing key characteristics of both neural responses and perceptual interpretations. We capture the encoding and readout computations of the network in simple analytical form and derive a binocular likelihood model that provides a unified account of long-standing puzzles in 3D vision at the physiological and perceptual levels. We suggest that marrying detection with proscription provides an effective coding strategy for sensory estimation that may be useful for diverse feature domains (e.g., motion) and multisensory integration.

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The brain uses “what not” detectors to facilitate 3D vision

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Binocular mismatches are used to drive suppression of incompatible depths

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Proscription accounts for depth perception without binocular correspondence

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A simple analytical model captures perceptual and neural responses

'Proto-rivalry': how the binocular brain identifies gloss

Visually identifying glossy surfaces can be crucial for survival (e.g. ice patches on a road), yet estimating gloss is computationally challenging for both human and machine vision. Here, we demonstrate that human gloss perception exploits some surprisingly simple binocular fusion signals, which are likely available early in the visual cortex. In particular, we show that the unusual disparity gradients and vertical offsets produced by reflections create distinctive ‘proto-rivalrous’ (barely fusible) image regions that are a critical indicator of gloss. We find that manipulating the gradients and vertical components of binocular disparities yields predictable changes in material appearance. Removing or occluding proto-rivalrous signals makes surfaces look matte, while artificially adding such signals to images makes them appear glossy. This suggests that the human visual system has internalized the idiosyncratic binocular fusion characteristics of glossy surfaces, providing a straightforward means of estimating surface attributes using low-level image signals.

Differential processing of binocular and monocular gloss cues in human visual cortex

The visual impression of an object's surface reflectance (“gloss”) relies on a range of visual cues, both monocular and binocular. Whereas previous imaging work has identified processing within ventral visual areas as important for monocular cues, little is known about cortical areas involved in processing binocular cues. Here, we used human functional MRI (fMRI) to test for brain areas selectively involved in the processing of binocular cues. We manipulated stereoscopic information to create four conditions that differed in their disparity structure and in the impression of surface gloss that they evoked. We performed multivoxel pattern analysis to find areas whose fMRI responses allow classes of stimuli to be distinguished based on their depth structure vs. material appearance. We show that higher dorsal areas play a role in processing binocular gloss information, in addition to known ventral areas involved in material processing, with ventral area lateral occipital responding to both object shape and surface material properties. Moreover, we tested for similarities between the representation of gloss from binocular cues and monocular cues. Specifically, we tested for transfer in the decoding performance of an algorithm trained on glossy vs. matte objects defined by either binocular or by monocular cues. We found transfer effects from monocular to binocular cues in dorsal visual area V3B/kinetic occipital (KO), suggesting a shared representation of the two cues in this area. These results indicate the involvement of mid- to high-level visual circuitry in the estimation of surface material properties, with V3B/KO potentially playing a role in integrating monocular and binocular cues.