Visual perception of materials and their properties

Top-down effects on translucency perception in relation to shape cues

It is well established that object shape perception significantly influences the perception of translucency. However, how object shape cues such as motion and binocular disparity affect the perception of translucency in rich environments, like virtual reality or real visual environments, remains unclear. This study aims to psychophysically measure the extent to which multiple object shape cues influence the perception of translucency. Additionally, we examined whether top-down factors, such as changes in cognitive attitude caused by the sequence of experiments, affect translucency perception. The results revealed that while motion and binocular disparity enhance translucency perception, this effect is confined to situations where shape cues are poor. Moreover, the effect became particularly pronounced when the experiments began with weak specular reflection stimuli, followed by the experiments using stimuli with specular reflection. In the case of translucent objects without specular reflection, strong shape information cannot be derived solely from shading patterns. These findings thus suggest that top-down factors related to shape modulate the influence of shape cues on translucency perception.

Visual cues of soft-tissue behaviour in minimal-invasive and robotic surgery

Minimal-invasive surgery (MIS) and robotic surgery (RS) offer multiple advantages over open surgery (Vajsbaher et al. in Cogn Syst Res 64:08, 2020). However, the lack of haptic feedback is still a limitation. Surgeons learn to adapt to this lack of haptic feedback using visual cues to make judgements about tissue deformation. Experienced robotic surgeons use the visual interpretation of tissue as a surrogate for tactile feedback. The aim of this review is to identify the visual cues that are consciously or unconsciously used by expert surgeons to manipulate soft tissue safely during Minimally Invasive Surgery (MIS) and Robotic Surgery (RS). We have conducted a comprehensive literature review with papers on visual cue identification and their application in education, as well as skill assessment and surgeon performance measurement with respect to visual feedback. To visualise our results, we provide an overview of the state-of-the-art in the form of a matrix across identified research features, where papers are clustered and grouped in a comparative way. The clustering of the papers showed explicitly that state-of-the-art research does not in particular study the direct effects of visual cues in relation to the manipulation of the tissue and training for that purpose, but is more concentrated on tissue identification. We identified a gap in the literature about the use of visual cues for educational design solutions, that aid the training of soft-tissue manipulation in MIS and in RS. There appears to be a need RS education to make visual cue identification more accessible and set it in the context of manipulation tasks.

Perceptual plausibility of exaggerated realistic motion

The informal heuristic practices of the fine arts have much to offer to our understanding of the appearance of phenomenological reality. One interesting example is the use of exaggeration to enhance the illusion of liveliness in both living and nonliving subjects. This further eases the uncomfortable sense that the motion is somehow uncanny - especially with inanimate objects. We performed a series of experiments to test the effects of exaggeration on the phenomenological perception of simple animated objects - bouncing balls. A physically plausible model of a bouncing ball was augmented with a frequently used form of exaggeration known as squash and stretch. Observers were shown a series of animated balls, depicted using systematic parameterizations of the exaggeration model, and asked to rate their plausibility. A range of rendering styles provided varying levels of information as to the type of ball. In all cases, balls with small amounts of exaggeration were seen as plausible as those without any exaggeration (e.g., with veridical motion). Furthermore, when the type of ball was not specified, observers tolerated a large amount of exaggeration before judging them as implausible. When the type of ball was indicated, observers narrowed the range of acceptable exaggeration somewhat but still tolerated exaggeration well beyond that which would be physically possible. We contend that, in this case, exaggeration acts to bridge the so-called uncanny valley for artificial depictions of physical reality.

Visual softness perception can be manipulated through exploratory procedures

Both visual and haptic softness perception have recently been shown to have multiple dimensions, such as deformability, granularity, fluidity, surface softness, and roughness. During haptic exploration, people adjust their hand motions (exploratory procedures, EPs) based on the material qualities of the object and the particular information they intend to acquire. Some of these EPs are also shown to be associated with perceived softness dimensions, for example, stroking a silk blouse or applying pressure to a pillow. Here, we aimed to investigate whether we can manipulate observers' judgments about softness attributes through exposure to videos of others performing various EPs on everyday soft materials. In two experiments, participants watched two videos of the same material: one with a corresponding EP and the other without correspondence; then, they judged these materials based on 12 softness-related adjectives (semantic differentiation method). The results of the second experiment suggested that when the EP is congruent with the dimension from which the material is chosen, the ratings for the adjectives from the same dimension are higher than the incongruent EP. This study provides evidence that participants can assess material properties from optic and mechanical cues without needing haptic signals. Additionally, our findings indicate that manipulating the hand motion can selectively facilitate material-related judgments.

Analysis of Gloss Unevenness and Bidirectional Reflectance Distribution Function in Specular Reflection

Gloss is associated significantly with material appearance, and observers often focus on gloss unevenness. Gloss unevenness is the intensity distribution of reflected light observed within a highlight area, that is, the variability. However, it cannot be analyzed easily because it exists only within the highlight area and varies in appearance across the reflection angles. In recent years, gloss has been analyzed in terms of the intensity of specular reflection and its angular spread, or the bidirectional reflectance distribution function (BRDF). In this study, we develop an apparatus to measure gloss unevenness that can alter the angle with an angular resolution of 0.02°. Additionally, we analyze the gloss unevenness and BRDF in terms of specular reflection. Using a high angular resolution, we measure and analyze high-gloss materials, such as mirrors and plastics, and glossy materials, such as photo-like inkjet paper and coated paper. Our results show that the magnitude of gloss unevenness is the largest at angles marginally off the center of the specular reflection angle. We discuss an approach for physically defining gloss unevenness based on the BRDF.

Glossiness perception and its pupillary response

Recent studies have revealed that pupillary response changes depend on perceptual factors such as subjective brightness caused by optical illusions and luminance. However, the manner in which the perceptual factor that is derived from the glossiness perception of object surfaces affects the pupillary response remains unclear. We investigated the relationship between the glossiness perception and pupillary response through a glossiness rating experiment that included recording the pupil diameter. We prepared general object images (original) and randomized images (shuffled) that comprised the same images with randomized small square regions as stimuli. The image features were controlled by matching the luminance histogram. The observers were asked to rate the perceived glossiness of the stimuli presented for 3,000 ms and the changes in their pupil diameters were recorded. Images with higher glossiness ratings constricted the pupil size more than those with lower glossiness ratings at the peak constriction of the pupillary responses during the stimulus duration. The linear mixed-effects model demonstrated that the glossiness rating, image category (original/shuffled), variance of the luminance histogram, and stimulus area were most effective in predicting the pupillary responses. These results suggest that the illusory brightness obtained by the image regions of high-glossiness objects, such as specular highlights, induce pupil constriction.

Probing the Link Between Vision and Language in Material Perception Using Psychophysics and Unsupervised Learning

We can visually discriminate and recognize a wide range of materials. Meanwhile, we use language to express our subjective understanding of visual input and communicate relevant information about the materials. Here, we investigate the relationship between visual judgment and language expression in material perception to understand how visual features relate to semantic representations. We use deep generative networks to construct an expandable image space to systematically create materials of well-defined and ambiguous categories. From such a space, we sampled diverse stimuli and compared the representations of materials from two behavioral tasks: visual material similarity judgments and free-form verbal descriptions. Our findings reveal a moderate but significant correlation between vision and language on a categorical level. However, analyzing the representations with an unsupervised alignment method, we discover structural differences that arise at the image-to-image level, especially among materials morphed between known categories. Moreover, visual judgments exhibit more individual differences compared to verbal descriptions. Our results show that while verbal descriptions capture material qualities on the coarse level, they may not fully convey the visual features that characterize the material's optical properties. Analyzing the image representation of materials obtained from various pre-trained data-rich deep neural networks, we find that human visual judgments' similarity structures align more closely with those of the text-guided visual-semantic model than purely vision-based models. Our findings suggest that while semantic representations facilitate material categorization, non-semantic visual features also play a significant role in discriminating materials at a finer level. This work illustrates the need to consider the vision-language relationship in building a comprehensive model for material perception. Moreover, we propose a novel framework for quantitatively evaluating the alignment and misalignment between representations from different modalities, leveraging information from human behaviors and computational models.

Peripheral material perception

Humans can rapidly identify materials, such as wood or leather, even within a complex visual scene. Given a single image, one can easily identify the underlying "stuff," even though a given material can have highly variable appearance; fabric comes in unlimited variations of shape, pattern, color, and smoothness, yet we have little trouble categorizing it as fabric. What visual cues do we use to determine material identity? Prior research suggests that simple "texture" features of an image, such as the power spectrum, capture information about material properties and identity. Few studies, however, have tested richer and biologically motivated models of texture. We compared baseline material classification performance to performance with synthetic textures generated from the Portilla-Simoncelli model and several common image degradations. The textures retain statistical information but are otherwise random. We found that performance with textures and most degradations was well below baseline, suggesting insufficient information to support foveal material perception. Interestingly, modern research suggests that peripheral vision might use a statistical, texture-like representation. In a second set of experiments, we found that peripheral performance is more closely predicted by texture and other image degradations. These findings delineate the nature of peripheral material classification.

materialmodifier: An R package of photo editing effects for material perception research

In this paper, we introduce an R package that performs automated photo editing effects. Specifically, it is an R implementation of an image-processing algorithm proposed by Boyadzhiev et al. (2015). The software allows the user to manipulate the appearance of objects in photographs, such as emphasizing facial blemishes and wrinkles, smoothing the skin, or enhancing the gloss of fruit. It provides a reproducible method to quantitatively control specific surface properties of objects (e.g., gloss and roughness), which is useful for researchers interested in topics related to material perception, from basic mechanisms of perception to the aesthetic evaluation of faces and objects. We describe the functionality, usage, and algorithm of the method, report on the findings of a behavioral evaluation experiment, and discuss its usefulness and limitations for psychological research. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier .

Effect of material properties on emotion: a virtual reality study

Introduction

Designers know that part of the appreciation of a product comes from the properties of its materials. These materials define the object's appearance and produce emotional reactions that can influence the act of purchase. Although known and observed as important, the affective level of a material remains difficult to assess. While many studies have been conducted regarding material colors, here we focus on two material properties that drive how light is reflected by the object: its metalness and smoothness. In this context, this work aims to study the influence of these properties on the induced emotional response.

Method

We conducted a perceptual user study in virtual reality, allowing participants to visualize and manipulate a neutral object - a mug. We generated 16 material effects by varying it metalness and smoothness characteristics. The emotional reactions produced by the 16 mugs were evaluated on a panel of 29 people using James Russel's circumplex model, for an emotional measurement through two dimensions: arousal (from low to high) and valence (from negative to positive). This scale, used here through VR users' declarative statements allowed us to order their emotional preferences between all the virtual mugs.

Result

Statistical results show significant positive effects of both metalness and smoothness on arousal and valence. Using image processing features, we show that this positive effect is linked to the increasing strength (i.e., sharpness and contrast) of the specular reflections induced by these material properties.

Discussion

The present work is the first to establish this strong relationship between specular reflections induced by material properties and aroused emotions.

Sustainable materials: a linking bridge between material perception, affordance, and aesthetics

The perception of material properties, which refers to the way in which individuals perceive and interpret materials through their sensory experiences, plays a crucial role in our interaction with the environment. Affordance, on the other hand, refers to the potential actions and uses that materials offer to users. In turn, the perception of the affordances is modulated by the aesthetic appreciation that individuals experience when interacting with the environment. Although material perception, affordances, and aesthetic appreciation are recognized as essential to fostering sustainability in society, only a few studies have investigated this subject matter systematically and their reciprocal influences. This scarcity is partially due to the challenges offered by the complexity of combining interdisciplinary topics that explore interactions between various disciplines, such as psychophysics, neurophysiology, affective science, aesthetics, and social and environmental sciences. Outlining the main findings across disciplines, this review highlights the pivotal role of material perception in shaping sustainable behaviors. It establishes connections between material perception, affordance, aesthetics, and sustainability, emphasizing the need for interdisciplinary research and integrated approaches in environmental psychology. This integration is essential as it can provide insight into how to foster sustainable and durable changes.

Visual perception: Contours that crack the ambiguity conundrum

A new study shows how the brain exploits the parts of images where surfaces curve out of view to recover both the three-dimensional shape and material properties of objects. This sheds light on a long-standing 'chicken-and-egg' problem in perception research.

Color and gloss constancy under diverse lighting environments

When we look at an object, we simultaneously see how glossy or matte it is, how light or dark, and what color. Yet, at each point on the object's surface, both diffuse and specular reflections are mixed in different proportions, resulting in substantial spatial chromatic and luminance variations. To further complicate matters, this pattern changes radically when the object is viewed under different lighting conditions. The purpose of this study was to simultaneously measure our ability to judge color and gloss using an image set capturing diverse object and illuminant properties. Participants adjusted the hue, lightness, chroma, and specular reflectance of a reference object so that it appeared to be made of the same material as a test object. Critically, the two objects were presented under different lighting environments. We found that hue matches were highly accurate, except for under a chromatically atypical illuminant. Chroma and lightness constancy were generally poor, but these failures correlated well with simple image statistics. Gloss constancy was particularly poor, and these failures were only partially explained by reflection contrast. Importantly, across all measures, participants were highly consistent with one another in their deviations from constancy. Although color and gloss constancy hold well in simple conditions, the variety of lighting and shape in the real world presents significant challenges to our visual system's ability to judge intrinsic material properties.

Material category of visual objects computed from specular image structure

Recognizing materials and their properties visually is vital for successful interactions with our environment, from avoiding slippery floors to handling fragile objects. Yet there is no simple mapping of retinal image intensities to physical properties. Here, we investigated what image information drives material perception by collecting human psychophysical judgements about complex glossy objects. Variations in specular image structure-produced either by manipulating reflectance properties or visual features directly-caused categorical shifts in material appearance, suggesting that specular reflections provide diagnostic information about a wide range of material classes. Perceived material category appeared to mediate cues for surface gloss, providing evidence against a purely feedforward view of neural processing. Our results suggest that the image structure that triggers our perception of surface gloss plays a direct role in visual categorization, and that the perception and neural processing of stimulus properties should be studied in the context of recognition, not in isolation.

Effects of face masks on language comprehension in bilingual children

Due to the COVID-19 pandemic, many children receive language input through face coverings. The impact of face coverings for children's abilities to understand language remains unclear. Past research with monolingual children suggests that hearing words through surgical masks does not disrupt word recognition, but hearing words through transparent face shields proves more challenging. In this study, we investigated effects of different face coverings (surgical masks and transparent face shields) on language comprehension in bilingual children. Three-year-old English-Mandarin bilingual children (N = 28) heard familiar words in both English and Mandarin spoken through transparent face shields, surgical masks, and without masks. When tested in English, children recognized words presented without a mask and through a surgical mask, but did not recognize words presented with transparent face shields, replicating past findings with monolingual children. In contrast, when tested in Mandarin, children recognized words presented without a mask, through a surgical mask, and through a transparent face shield. Results are discussed in terms of specific properties of English and Mandarin that may elicit different effects for transparent face shields. Overall, the present findings suggest that face coverings, and in particular, surgical masks do not disrupt spoken word recognition in young bilingual children.

Unmet expectations about material properties delay perceptual decisions

Based on our expectations about material properties, we can implicitly predict an object's future states, e.g., a wine glass falling down will break when it hits the ground. How these expectations affect relatively low-level perceptual decisions, however, has not been systematically studied previously. To seek an answer to this question, we conducted a behavioral experiment using animations of various familiar objects (e.g., key, wine glass, etc.) freely falling and hitting the ground. During a training session, participants first built expectations about the dynamic properties of those objects. Half of the participants (N = 28) built expectations consistent with their daily lives (e.g., a key bounces rigidly), whereas the other half learned an atypical behavior (e.g., a key wobbles). This was followed by experimental sessions, in which expectations were unmet in 20% of the trials. In both training and experimental sessions, the participant's task was to report whether the objects broke or not upon hitting the ground. Critically, a specific object always remained intact or broke - only the manner in which it did so differed. For example, a key could wobble or remain rigid but never break. We found that participants' reaction times were longer when expectations were unmet, not only for typical material behavior but also when those expectations were atypical and learned during the training session. Furthermore, we found an interplay between long-term and newly learned expectations. Overall, our results show that expectations about material properties can impact relatively low-level perceptual decision-making processes.

Perceiving the representative surface color of real-world materials

Natural surfaces such as soil, grass, and skin usually involve far more complex and heterogenous structures than the perfectly uniform surfaces assumed in studies on color and material perception. Despite this, we can easily perceive the representative color of these surfaces. Here, we investigated the visual mechanisms underlying the perception of representative surface color using 120 natural images of diverse materials and their statistically synthesized images. Our matching experiments indicated that the perceived representative color revealed was not significantly different from the Portilla-Simoncelli-synthesized images or phase-randomized images except for one sample, even though the perceived shape and material properties were greatly impaired in the synthetic stimuli. The results also showed that the matched representative colors were predictable from the saturation-enhanced color of the brightest point in the image, excluding the high-intensity outliers. The results support the notion that humans judge the representative color and lightness of real-world surfaces depending on simple image measurements.

Texture statistics involved in specular highlight exclusion for object lightness perception

The human visual system estimates the physical properties of objects, such as their lightness. Previous studies on the lightness perception of glossy three-dimensional objects have suggested that specular highlights are detected and excluded in lightness perception. However, only a few studies have attempted to elucidate the mechanisms underlying this exclusion. This study aimed to elucidate the image features that contribute to the highlight exclusion of lightness perception. We used Portilla-Simoncelli texture statistics (PS statistics), an image feature set similar to the representation in the early visual cortex, to explore their relationships with highlight exclusion for lightness perception. In experiment 1, computer graphics images of bumpy plastic plates with various physical parameters were used as stimuli, and the lightness perception on them was measured using a lightness matching task. We then calculated the highlight exclusion index, which represented the degree of highlight exclusion. Finally, we evaluated the correlation between the highlight exclusion index and the four PS statistic subsets. In experiment 2, an image synthesis algorithm was used to create images in which either the PS statistic subset was manipulated. The highlight exclusion indexes of the synthesized images were then measured. The results revealed that the PS statistic subset consisting of lowest-order image features, such as moment statistics of luminance, acts as a necessary condition for highlight exclusion, whereas the other three subsets consisting of higher order features are not crucial. These results suggest that the low-order image features are the most important among the features in PS statistics for highlight exclusion, even though image features higher order than those in PS statistics must be directly involved.

Effects of specular roughness on the perception of color and opacity

Previous studies have shown that information concerning object shape is important for the perception of translucency. This study aims to explore how the perception of semi-opaque objects is influenced by surface gloss. We varied specular roughness, specular amplitude, and the simulated direction of a light source used to illuminate a globally convex bumpy object. We found that perceived lightness and roughness increased as specular roughness was increased. Declines in perceived saturation were observed but were far smaller in magnitude with these increases in specular roughness. There were inverse correlations found between perceived gloss and perceived lightness, perceived transmittance and perceived saturation, and between perceived roughness and perceived gloss. Positive correlations were found between perceived transmittance and glossiness, and between perceived roughness and perceived lightness. These findings suggest that specular reflections influence the perception of transmittance and color attributes, and not just perceived gloss. We also performed follow-up modeling of image data to find that perceived saturation and lightness could be explained by the reliance on different image regions with greater chroma and lower lightness, respectively. We also found systematic effects of lighting direction on perceived transmittance that indicate there are complex perceptual interactions that require further consideration.

Unsupervised learning reveals interpretable latent representations for translucency perception

Humans constantly assess the appearance of materials to plan actions, such as stepping on icy roads without slipping. Visual inference of materials is important but challenging because a given material can appear dramatically different in various scenes. This problem especially stands out for translucent materials, whose appearance strongly depends on lighting, geometry, and viewpoint. Despite this, humans can still distinguish between different materials, and it remains unsolved how to systematically discover visual features pertinent to material inference from natural images. Here, we develop an unsupervised style-based image generation model to identify perceptually relevant dimensions for translucent material appearances from photographs. We find our model, with its layer-wise latent representation, can synthesize images of diverse and realistic materials. Importantly, without supervision, human-understandable scene attributes, including the object's shape, material, and body color, spontaneously emerge in the model's layer-wise latent space in a scale-specific manner. By embedding an image into the learned latent space, we can manipulate specific layers' latent code to modify the appearance of the object in the image. Specifically, we find that manipulation on the early-layers (coarse spatial scale) transforms the object's shape, while manipulation on the later-layers (fine spatial scale) modifies its body color. The middle-layers of the latent space selectively encode translucency features and manipulation of such layers coherently modifies the translucency appearance, without changing the object's shape or body color. Moreover, we find the middle-layers of the latent space can successfully predict human translucency ratings, suggesting that translucent impressions are established in mid-to-low spatial scale features. This layer-wise latent representation allows us to systematically discover perceptually relevant image features for human translucency perception. Together, our findings reveal that learning the scale-specific statistical structure of natural images might be crucial for humans to efficiently represent material properties across contexts.

New Approaches to 3D Vision

New approaches to 3D vision are enabling new advances in artificial intelligence and autonomous vehicles, a better understanding of how animals navigate the 3D world, and new insights into human perception in virtual and augmented reality. Whilst traditional approaches to 3D vision in computer vision (SLAM: simultaneous localization and mapping), animal navigation (cognitive maps), and human vision (optimal cue integration) start from the assumption that the aim of 3D vision is to provide an accurate 3D model of the world, the new approaches to 3D vision explored in this issue challenge this assumption. Instead, they investigate the possibility that computer vision, animal navigation, and human vision can rely on partial or distorted models or no model at all. This issue also highlights the implications for artificial intelligence, autonomous vehicles, human perception in virtual and augmented reality, and the treatment of visual disorders, all of which are explored by individual articles. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Perceiving the shape and material properties of 3D surfaces

Our visual experience of the world relies on the interaction of light with the different substances, surfaces, and objects in our environment. These optical interactions generate images that contain a conflated mixture of different scene variables, which our visual system must somehow disentangle to extract information about the shape and material properties of the world. Such problems have historically been considered to be ill-posed, but recent work suggests that there are complex patterns of covariation in light that co-specify the 3D shape and material properties of surfaces. This work provides new insights into how the visual system acquired the ability to solve problems that have historically been considered intractable.

Functional recursion of orientation cues in figure-ground separation

Visual texture is an important cue to figure-ground organization. While processing of texture differences is a prerequisite for the use of this cue to extract figure-ground organization, these stages are distinct processes. One potential indicator of this distinction is the possibility that texture statistics play a different role in the figure vs. in the ground. To determine whether this is the case, we probed figure-ground processing with a family of local image statistics that specified textures that varied in the strength and spatial scale of structure, and the extent to which features are oriented. For image statistics that generated approximately isotropic textures, the threshold for identification of figure-ground structure was determined by the difference in correlation strength in figure vs. ground, independent of whether the correlations were present in figure, ground, or both. However, for image statistics with strong orientation content, thresholds were up to two times higher for correlations in the ground, vs. the figure. This held equally for texture-defined objects with convex or concave boundaries, indicating that these threshold differences are driven by border ownership, not boundary shape. Similar threshold differences were found for presentation times ranging from 125 to 500 ms. These findings identify a qualitative difference in how texture is used for figure-ground analysis, vs. texture discrimination. Additionally, it reveals a functional recursion: texture differences are needed to identify tentative boundaries and consequent scene organization into figure and ground, but then scene organization modifies sensitivity to texture differences according to the figure-ground assignment.

Identifying specular highlights: Insights from deep learning

Specular highlights are the most important image feature for surface gloss perception. Yet, recognizing whether a bright patch in an image is due to specular reflection or some other cause (e.g., texture marking) is challenging, and it remains unclear how the visual system reliably identifies highlights. There is currently no image-computable model that emulates human highlight identification, so here we sought to develop a neural network that reproduces observers' characteristic successes and failures. We rendered 179,085 images of glossy, undulating, textured surfaces. Given such images as input, a feedforward convolutional neural network was trained to output an image containing only the specular reflectance component. Participants viewed such images and reported whether or not specific pixels were highlights. The queried pixels were carefully selected to distinguish between ground truth and a simple thresholding of image intensity. The neural network outperformed the simple thresholding model-and ground truth-at predicting human responses. We then used a genetic algorithm to selectively delete connections within the neural network to identify variants of the network that approximated human judgments even more closely. The best resulting network shared 68% of the variance with human judgments-more than the unpruned network. As a first step toward interpreting the network, we then used representational similarity analysis to compare its inner representations to a wide variety of hand-engineered image features. We find that the network learns representations that are similar not only to directly image-computable predictors but also to more complex predictors such as intrinsic or geometric factors, as well as some indications of photo-geometrical constraints learned by the network. However, our network fails to replicate human response patterns to violations of photo-geometric constraints (rotated highlights) as described by other authors.

Learning-Based Inverse Bi-Scale Material Fitting From Tabular BRDFs

Relating small-scale structures to large-scale appearance is a key element in material appearance design. Bi-scale material design requires finding small-scale structures - meso-scale geometry and micro-scale BRDFs - that produce a desired large-scale appearance expressed as a macro-scale BRDF. The adjustment of small-scale geometry and reflectances to achieve a desired appearance can become a tedious trial-and-error process. We present a learning-based solution to fit a target macro-scale BRDF with a combination of a meso-scale geometry and micro-scale BRDF. We confront challenges in representation at both scales. At the large scale we need macro-scale BRDFs that are both compact and expressive. At the small scale we need diverse combinations of geometric patterns and potentially spatially varying micro-BRDFs. For large-scale macro-BRDFs, we propose a novel 2D subset of a tabular BRDF representation that well preserves important appearance features for learning. For small-scale details, we represent geometries and BRDFs in different categories with different physical parameters to define multiple independent continuous search spaces. To build the mapping between large-scale macro-BRDFs and small-scale details, we propose an end-to-end model that takes the subset BRDF as input and performs classification and parameter estimation on small-scale details to find an accurate reconstruction. Compared with other fitting methods, our learning-based solution provides higher reconstruction accuracy and covers a wider gamut of appearance.

Scale ambiguities in material recognition

Many natural materials have complex, multi-scale structures. Consequently, the inferred identity of a surface can vary with the assumed spatial scale of the scene: a plowed field seen from afar can resemble corduroy seen up close. We investigated this 'material-scale ambiguity' using 87 photographs of diverse materials (e.g., water, sand, stone, metal, and wood). Across two experiments, separate groups of participants (N = 72 adults) provided judgements of the material category depicted in each image, either with or without manipulations of apparent distance (by verbal instructions, or adding objects of familiar size). Our results demonstrate that these manipulations can cause identical images to be assigned to completely different material categories, depending on the assumed scale. Under challenging conditions, therefore, the categorization of materials is susceptible to simple manipulations of apparent distance, revealing a striking example of top-down effects in the interpretation of image features.

The Role of Specular Reflections and Illumination in the Perception of Thickness in Solid Transparent Objects

Specular reflections and refractive distortions are complex image properties of solid transparent objects, but despite this complexity, we readily perceive the 3D shapes of these objects (e.g., glass and clear plastic). We have found in past work that relevant sources of scene complexity have differential effects on 3D shape perception, with specular reflections increasing perceived thickness, and refractive distortions decreasing perceived thickness. In an object with both elements, such as glass, the two optical properties may complement each other to support reliable perception of 3D shape. We investigated the relative dominance of specular reflection and refractive distortions in the perception of shape. Surprisingly, the ratio of specular reflection to refractive component was almost equal to that of ordinary glass and ice, which promote correct percepts of 3D shape. The results were also explained by the variance in local RMS contrast in stimulus images but may depend on overall luminance and contrast of the surrounding light field.

Distinguishing mirror from glass: A "big data" approach to material perception

Distinguishing mirror from glass is a challenging visual inference, because both materials derive their appearance from their surroundings, yet we rarely experience difficulties in telling them apart. Very few studies have investigated how the visual system distinguishes reflections from refractions and to date, there is no image-computable model that emulates human judgments. Here we sought to develop a deep neural network that reproduces the patterns of visual judgments human observers make. To do this, we trained thousands of convolutional neural networks on more than 750,000 simulated mirror and glass objects, and compared their performance with human judgments, as well as alternative classifiers based on "hand-engineered" image features. For randomly chosen images, all classifiers and humans performed with high accuracy, and therefore correlated highly with one another. However, to assess how similar models are to humans, it is not sufficient to compare accuracy or correlation on random images. A good model should also predict the characteristic errors that humans make. We, therefore, painstakingly assembled a diagnostic image set for which humans make systematic errors, allowing us to isolate signatures of human-like performance. A large-scale, systematic search through feedforward neural architectures revealed that relatively shallow (three-layer) networks predicted human judgments better than any other models we tested. This is the first image-computable model that emulates human errors and succeeds in distinguishing mirror from glass, and hints that mid-level visual processing might be particularly important for the task.

Quantification of Visual Texture and Presentation of Intermediate Visual Texture by Spatial Mixing

We proposed a method to display an intermediate visual texture by spatial mixing. In addition to color information, the visual texture is an important element that characterizes the nature of an object’s surface. While the system to display various color information has well matured in engineering, there is no method to reproduce visual textures in ambient light. In our method, the matte and glossy surfaces are used as “primary visual textures”, and an intermediate visual texture is displayed by spatially mixing the primary visual textures. In this paper, we first quantified the visual texture of an object's surface based on measured intensities of scattered and reflected lights. Next, based on the quantification, we evaluated spatially mixed surfaces consisting of two primary visual textures, an acrylic plate and a holed sheet of drawing paper, by changing the area proportion of the two primary visual textures. Finally, a sensory evaluation showed significant differences between each intermediate visual texture, and the results corresponded to a trend in the optical evaluation. This study illustrates that visual textures could be quantified based on the intensity of scattered and reflected light and reveals the applicability of our method to the display for intermediate visual texture.

Perceptual judgments for the softness of materials under indentation

Humans can judge the softness of elastic materials through only visual cues. However, factors contributing to the judgment of visual softness are not yet fully understood. We conducted a psychophysical experiment to determine which factors and motion features contribute to the apparent softness of materials. Observers watched video clips in which materials were indented from the top surface to a certain depth, and reported the apparent softness of the materials. The depth and speed of indentation were systematically manipulated. As physical characteristics of materials, compliance was also controlled. It was found that higher indentation speeds resulted in larger softness rating scores and the variation with the indentation speed was successfully explained by the image motion speed. The indentation depth had a powerful effect on the softness rating scores and the variation with the indentation depth was consistently explained by motion features related to overall deformation. Higher material compliance resulted in higher softness rating scores and these variation with the material compliance can be explained also by overall deformation. We conclude that the brain makes visual judgments about the softness of materials under indentation on the basis of the motion speed and deformation magnitude.

Crystal or jelly? Effect of color on the perception of translucent materials with photographs of real-world objects

Translucent materials are ubiquitous in nature (e.g. teeth, food, and wax), but our understanding of translucency perception is limited. Previous work in translucency perception has mainly used monochromatic rendered images as stimuli, which are restricted by their diversity and realism. Here, we measure translucency perception with photographs of real-world objects. 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. Two different groups of observers finish the three tasks with color or grayscale images. We find that observers' agreements depend on the physical material properties of the objects such that translucent materials generate more interobserver disagreements. Further, there are more disagreements among observers in the grayscale condition in comparison to that in the color condition. We also discover that converting images to grayscale substantially affects the distributions of attribute ratings for some images. Furthermore, ratings of see-throughness, glossiness, and glow could predict individual observers' binary classification of images in both grayscale and color conditions. 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. Our result demonstrates that color is informative about material property estimation and recognition. Meanwhile, our analysis shows that mid-level semantic estimation of material attributes might be closely related to high-level material recognition. We also discuss individual differences in our results and highlight the importance of such consideration in material perception.

Effect of wall covering materials on the perception of cafe environments

Purpose

This study aims to determine the effects of wall covering materials (wood, concrete and metal) used indoors on participants’ perceptual evaluations. The differences among participants’ perceptual evaluations regarding indoor physical environmental factors by occupation and gender were examined.

Design/methodology/approach

Cafes were selected as research environments. Virtual experimental spaces using three different wall covering materials were modelled and participants’ assessment of the physical environmental factors of these virtual spaces was measured through a detailed questionnaire.

Findings

Cafes using light-coloured wall covering materials were perceived more favourably than cafes using dark-coloured wall covering materials, and cafes with light-coloured wooden wall coverings were considered as a warmer material than cafes using concrete and metal. Participants who received design education (architect, interior architect) perceived physical environmental factors of cafes more negatively than those who did not receive design education (lawyer, economist, accountant, etc.). Male participants evaluated the physical environmental factors of cafes more positively than female participants for all adjective pairs. Except for two adjective pairs, no significant difference was found among the evaluations according to genders for the other adjective pairs.

Originality/value

This study revealed new results about customers’ choices of wall covering materials and offered designers new alternatives for materials that can be used in the design of cafes.

Effects of visual and visual-haptic perception of material rigidity on reaching and grasping in the course of development

The development of material property perception for grasping objects is not well explored during early childhood. Therefore, we investigated infants', 3-year-old children's, and adults' unimanual grasping behavior and reaching kinematics for objects of different rigidity using a 3D motion capture system. In Experiment 1, 11-month-old infants and for purposes of comparison adults, and in Experiment 2, 3-year old children were encouraged to lift relatively heavy objects with one of two handles differing in rigidity after visual (Condition 1) and visual-haptic exploration (Condition 2). Experiment 1 revealed that 11-months-olds, after visual object exploration, showed no significant material preference, and thus did not consider the material to facilitate grasping. After visual-haptic object exploration and when grasping the contralateral handles, infants showed an unexpected preference for the soft handles, which were harder to use to lift the object. In contrast, adults generally grasped the rigid handle exploiting their knowledge about efficient and functional grasping in both conditions. Reaching kinematics were barely affected by rigidity, but rather by condition and age. Experiment 2 revealed that 3-year-olds no longer exhibit a preference for grasping soft handles, but still no adult-like preference for rigid handles in both conditions. This suggests that material rigidity plays a minor role in infants' grasping behavior when only visual material information is available. Also, 3-year-olds seem to be on an intermediate level in the development from (1) preferring the pleasant sensation of a soft fabric, to (2) preferring the efficient rigid handle.

Gloss perception: Searching for a deep neural network that behaves like humans

The visual computations underlying human gloss perception remain poorly understood, and to date there is no image-computable model that reproduces human gloss judgments independent of shape and viewing conditions. Such a model could provide a powerful platform for testing hypotheses about the detailed workings of surface perception. Here, we made use of recent developments in artificial neural networks to test how well we could recreate human responses in a high-gloss versus low-gloss discrimination task. We rendered >70,000 scenes depicting familiar objects made of either mirror-like or near-matte textured materials. We trained numerous classifiers to distinguish the two materials in our images-ranging from linear classifiers using simple pixel statistics to convolutional neural networks (CNNs) with up to 12 layers-and compared their classifications with human judgments. To determine which classifiers made the same kinds of errors as humans, we painstakingly identified a set of 60 images in which human judgments are consistently decoupled from ground truth. We then conducted a Bayesian hyperparameter search to identify which out of several thousand CNNs most resembled humans. We found that, although architecture has only a relatively weak effect, high correlations with humans are somewhat more typical in networks of shallower to intermediate depths (three to five layers). We also trained deep convolutional generative adversarial networks (DCGANs) of different depths to recreate images based on our high- and low-gloss database. Responses from human observers show that two layers in a DCGAN can recreate gloss recognizably for human observers. Together, our results indicate that human gloss classification can best be explained by computations resembling early to mid-level vision.

Colorants in coatings

The aim of this chapter is to provide a compact overview of colorants and their use in coatings including a brief introduction to paint technology and its raw materials. In addition, it will focus on individual colorants by collecting information from the available literature mainly for their use in coatings. Publications on colorants in coatings applications are in many cases standard works that cover the wider aspects of color chemistry and paint technology and are explicitly recommended for a more detailed study of the subject [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. Articles or information on paint formulation using coatings which contain colorants are rare [19]. This formulation expertise is often company property as it is the result of many years of effort built up over very long series of practical “trial-and-error” optimization tests and, more recently, supported by design of experiment and laboratory process automation [20, 21]. Therefore, it is protected by rigorous secrecy agreements. Formulations are in many ways part of a paint manufacturer’s capital, because of their use in automotive coatings, coil coatings, powder coatings, and specialist knowledge is indispensable to ensure their successful industrial use [22]. An important source to learn about the use of pigments in different coating formulations are guidance or starting formulations offered by pigment, additive, and resin manufacturers. These are available upon request from the technical service unit of these companies. Coating formulations can also be found scattered in books on coating and formulation technology [4, 5, 18, 23,24,25,26,27]. This overview can in no way claim to be complete, as the literature and relevant journals in this field are far too extensive. Nevertheless, it remains the author’s hope that the reader will gain a comprehensive insight into the fascinating field of colorants for coatings, including its literature and current research activities and last but not least its scientific attractiveness and industrial relevance.

Conceptual representation of real-world surface material: Early integration with linguistic-labels indicated in the N400-component

Research in perception in the visual and auditory domains has traditionally focused on investigating highly controlled artificial stimulus material. However, a key feature of our perceptual system is the ease with which the input of a wide set of naturalistic co-occurring information is dealt with. This study investigated whether, during perception of real-world surface material, a conceptual representation is built that has the potential to interact with a linguistic description of the material directly. Short sentences were presented (e.g., This surface is smooth) followed by a matching or mismatching picture of a real-world surface material. The results showed early cross-modal integration effects during material surface perception in an N400-like potential, originating approximately 280 ms after stimulus presentation. Overall, these findings suggest a rather early influence of linguistic information on material perception, suggesting that in line with object representation, real-world materials are represented in the brain in a format that allows interaction with non-visual information.

Translucency perception: A review

Translucency is an optical and a perceptual phenomenon that characterizes subsurface light transport through objects and materials. Translucency as an optical property of a material relates to the radiative transfer inside and through this medium, and translucency as a perceptual phenomenon describes the visual sensation experienced by humans when observing a given material under given conditions. The knowledge about the visual mechanisms of the translucency perception remains limited. Accurate prediction of the appearance of the translucent objects can have a significant commercial impact in the fields such as three-dimensional printing. However, little is known how the optical properties of a material relate to a perception evoked in humans. This article overviews the knowledge status about the visual perception of translucency and highlights the applications of the translucency perception research. Furthermore, this review summarizes current knowledge gaps, fundamental challenges and existing ambiguities with a goal to facilitate translucency perception research in the future.

Structure and function of neural circuit related to gloss perception in the macaque inferior temporal cortex: a case report

The inferior temporal (IT) cortex of the macaque monkey plays a pivotal role in the visual recognition of objects. In the IT cortex, a feature-selective network formed by connecting subregions specialized for common visual features seems to be a basic strategy for processing biologically important visual features. Gloss perception plays an important role in the judgment of materials and conditions of objects and is a biologically significant visual function. In the present study, we attempted to determine whether a neural circuit specialized for processing information related to gloss perception exists in the IT cortex in one monkey. We injected retrograde tracer into a gloss-selective subregion in the IT cortex where gloss-selective neurons were clustered in the neural recording experiment, and anatomically examined its neural connections. We observed that retrogradely labeled neurons were densely accumulated in multiple locations in the posterior and anterior IT cortices. Based on the results of this case study, we will discuss the possibility that, together with the injection site, the sites with a dense cluster of labeled neurons form feature-selective neural circuits for the processing of gloss information in the IT cortex.

Perception of material appearance: A comparison between painted and rendered images

Painters are masters in replicating the visual appearance of materials. While the perception of material appearance is not yet fully understood, painters seem to have acquired an implicit understanding of the key visual cues that we need to accurately perceive material properties. In this study, we directly compare the perception of material properties in paintings and in renderings by collecting professional realistic paintings of rendered materials. From both type of images, we collect human judgments of material properties and compute a variety of image features that are known to reflect material properties. Our study reveals that, despite important visual differences between the two types of depiction, material properties in paintings and renderings are perceived very similarly and are linked to the same image features. This suggests that we use similar visual cues independently of the medium and that the presence of such cues is sufficient to provide a good appearance perception of the materials.

Unsupervised learning predicts human perception and misperception of gloss

Reflectance, lighting and geometry combine in complex ways to create images. How do we disentangle these to perceive individual properties, such as surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this hypothesis, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgements. The networks spontaneously cluster images according to distal properties such as reflectance and illumination, despite receiving no explicit information about these properties. Intriguingly, the resulting representations also predict the specific patterns of ‘successes’ and ‘errors’ in human perception. Linearly decoding specular reflectance from the model’s internal code predicts human gloss perception better than ground truth, supervised networks or control models, and it predicts, on an image-by-image basis, illusions of gloss perception caused by interactions between material, shape and lighting. Unsupervised learning may underlie many perceptual dimensions in vision and beyond.

Storrs et al. train unsupervised generative neural networks on glossy surfaces and show how gloss perception in humans may emerge in an unsupervised fashion from learning to model statistical structure.

Infants recognize words spoken through opaque masks but not through clear masks

COVID-19 has modified numerous aspects of children's social environments. Many children are now spoken to through a mask. There is little empirical evidence attesting to the effects of masked language input on language processing. In addition, not much is known about the effects of clear masks (i.e., transparent face shields) versus opaque masks on language comprehension in children. In the current study, 2-year-old infants were tested on their ability to recognize familiar spoken words in three conditions: words presented with no mask, words presented through a clear mask, and words presented through an opaque mask. Infants were able to recognize familiar words presented without a mask and when hearing words through opaque masks, but not when hearing words through clear masks. Findings suggest that the ability of infants to recover spoken language input through masks varies depending on the surface properties of the mask.

The effect of visual distractors on visual working memory for surface roughness in the human brain

Research has confirmed that the visual working memory representation of objects' roughness is robust against illumination changes in the human ventral visual cortex and intraparietal sulcus, but not yet against visual distractors during memory maintenance. Thus, this study investigated the effects of visual distractors on roughness-related brain regions during the maintenance phase using multi-voxel pattern analysis (MVPA). We conducted an fMRI experiment in which participants were asked to memorize a sphere's roughness against visual distractors, presented during the delay period in random trials. Region of interest-based MVPA showed no contribution of the ventral visual cortex and intraparietal sulcus to the roughness memory, regardless of behavioral performance. Post hoc searchlight MVPA revealed an above-chance decoding performance level in the brain regions presumably related to haptic processing when no visual distractors were shown. In contrast, when visual distractors appeared in the delay period, decoding performance exceeded the chance level in the ventral visual cortex. These results suggest that when visual distractors are presented during the memory phase, both visual and haptic processing are related to visual working memory for roughness, and the weighting of modality changes depending on the presence of visual distractors.

Lifelong Visual-Tactile Cross-Modal Learning for Robotic Material Perception

The material attribute of an object's surface is critical to enable robots to perform dexterous manipulations or actively interact with their surrounding objects. Tactile sensing has shown great advantages in capturing material properties of an object's surface. However, the conventional classification method based on tactile information may not be suitable to estimate or infer material properties, particularly during interacting with unfamiliar objects in unstructured environments. Moreover, it is difficult to intuitively obtain material properties from tactile data as the tactile signals about material properties are typically dynamic time sequences. In this article, a visual-tactile cross-modal learning framework is proposed for robotic material perception. In particular, we address visual-tactile cross-modal learning in the lifelong learning setting, which is beneficial to incrementally improve the ability of robotic cross-modal material perception. To this end, we proposed a novel lifelong cross-modal learning model. Experimental results on the three publicly available data sets demonstrate the effectiveness of the proposed method.

The joint role of geometry and illumination on material recognition

Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly nonlinear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly nonlinear models and humans may use similar high-level factors for material recognition tasks.

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Tactile perception is a rich source of information for robotic grasping: it allows a robot to identify a grasped object and assess the stability of a grasp, among other things. However, the tactile sensor must come into contact with the target object in order to produce readings. As a result, tactile data can only be attained if a real contact is made. We propose to overcome this restriction by employing a method that models the behaviour of a tactile sensor using 3D vision and grasp information as a stimulus. Our system regresses the quantified tactile response that would be experienced if this grasp were performed on the object. We experiment with 16 items and 4 tactile data modalities to show that our proposal learns this task with low error.

Dynamic dot displays reveal material motion network in the human brain

There is growing research interest in the neural mechanisms underlying the recognition of material categories and properties. This research field, however, is relatively more recent and limited compared to investigations of the neural mechanisms underlying object and scene category recognition. Motion is particularly important for the perception of non-rigid materials, but the neural basis of non-rigid material motion remains unexplored. Using fMRI, we investigated which brain regions respond preferentially to material motion versus other types of motion. We introduce a new database of stimuli - dynamic dot materials - that are animations of moving dots that induce vivid percepts of various materials in motion, e.g. flapping cloth, liquid waves, wobbling jelly. Control stimuli were scrambled versions of these same animations and rigid three-dimensional rotating dots. Results showed that isolating material motion properties with dynamic dots (in contrast with other kinds of motion) activates a network of cortical regions in both ventral and dorsal visual pathways, including areas normally associated with the processing of surface properties and shape, and extending to somatosensory and premotor cortices. We suggest that such a widespread preference for material motion is due to strong associations between stimulus properties. For example viewing dots moving in a specific pattern not only elicits percepts of material motion; one perceives a flexible, non-rigid shape, identifies the object as a cloth flapping in the wind, infers the object's weight under gravity, and anticipates how it would feel to reach out and touch the material. These results are a first important step in mapping out the cortical architecture and dynamics in material-related motion processing.

Mid-level vision

In this primer, Anderson provides an overview of some central topics in mid-level vision, highlighting some recent advances in our understanding of how the human visual system identifies different environmental sources of optical structure.

Softness and weight from shape: Material properties inferred from local shape features

Object shape is an important cue to material identity and for the estimation of material properties. Shape features can affect material perception at different levels: at a microscale (surface roughness), mesoscale (textures and local object shape), or megascale (global object shape) level. Examples for local shape features include ripples in drapery, clots in viscous liquids, or spiraling creases in twisted objects. Here, we set out to test the role of such shape features on judgments of material properties softness and weight. For this, we created a large number of novel stimuli with varying surface shape features. We show that those features have distinct effects on softness and weight ratings depending on their type, as well as amplitude and frequency, for example, increasing numbers and pointedness of spikes makes objects appear harder and heavier. By also asking participants to name familiar objects, materials, and transformations they associate with our stimuli, we can show that softness and weight judgments do not merely follow from semantic associations between particular stimuli and real-world object shapes. Rather, softness and weight are estimated from surface shape, presumably based on learned heuristics about the relationship between a particular expression of surface features and material properties. In line with this, we show that correlations between perceived softness or weight and surface curvature vary depending on the type of surface feature. We conclude that local shape features have to be considered when testing the effects of shape on the perception of material properties such as softness and weight.

Expectations affect the perception of material properties

Many objects that we encounter have typical material qualities: spoons are hard, pillows are soft, and Jell-O dessert is wobbly. Over a lifetime of experiences, strong associations between an object and its typical material properties may be formed, and these associations not only include how glossy, rough, or pink an object is, but also how it behaves under force: we expect knocked over vases to shatter, popped bike tires to deflate, and gooey grilled cheese to hang between two slices of bread when pulled apart. Here we ask how such rich visual priors affect the visual perception of material qualities and present a particularly striking example of expectation violation. In a cue conflict design, we pair computer-rendered familiar objects with surprising material behaviors (a linen curtain shattering, a porcelain teacup wrinkling, etc.) and find that material qualities are not solely estimated from the object's kinematics (i.e., its physical [atypical] motion while shattering, wrinkling, wobbling etc.); rather, material appearance is sometimes “pulled” toward the “native” motion, shape, and optical properties that are associated with this object. Our results, in addition to patterns we find in response time data, suggest that visual priors about materials can set up high-level expectations about complex future states of an object and show how these priors modulate material appearance.

Contrasting a Misinterpretation of the Reverse Contrast

The reverse contrast is a perceptual phenomenon in which the effect of the classical simultaneous lightness contrast is reversed. In classic simultaneous lightness contrast configurations, a gray surrounded by black is perceived lighter than an identical gray surrounded by white, but in the reverse contrast configurations, the perceptual outcome is the opposite: a gray surrounded by black appears darker than the same gray surrounded by white. The explanation provided for the reverse contrast (by different authors) is the belongingness of the gray targets to a more complex configuration. Different configurations show the occurrence of these phenomena; however, the factors determining this effect are not always the same. In particular, some configurations are based on both belongingness and assimilation, while one configuration is based only on belongingness. The evidence that different factors determine the reverse contrast is crucial for future research dealing with achromatic color perception and, in particular, with lightness induction phenomena.