Effects of Highlights on Gloss Perception

The perception of a glossy surface in a static monochromatic image can occur when a bright highlight is embedded in a compatible context of shading and a bounding contour. Some images naturally give rise to the impression that a surface has a uniform reflectance, characteristic of a shiny object, even though the highlight may only cover a small portion of the surface. Nonetheless, an observer may adopt an attitude of scrutiny in viewing a glossy surface, whereby the impression of gloss is partial and nonuniform at image regions outside of a higlight. Using a rating scale and small probe points to indicate image locations, differential perception of gloss within a single object is investigate in the present study. Observers' gloss ratings are not uniform across the surface, but decrease as a function of distance from highlight. When, by design, the distance from a highlight is uncoupled from the luminance value at corresponding probe points, the decrease in rated gloss correlates more with the distance than with the luminance change. Experiments also indicate that gloss ratings change as a function of estimated surface distance, rather than as a function of image distance. Surface continuity affects gloss ratings, suggesting that apprehension of 3D surface structure is crucial for gloss perception.Air Force Office of Scientific Research (F49620-98-1-0108), Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409), National Science Foundation (IIS-97-20333); Office of Naval Research (N00014-95-1-0657, N00014-01-1-0624); Whitaker Foundation (RG-99-0186

Colorimetric Accuracy of Color Reproductions in the 3D Scenes

Color is a complex phenomenon that depends on the object, the observer and the light source, while the color reproduction additionally includes the surrounding conditions and it depends on the device that can either capture, display or print the reproduction. Colour accuracy is very important for digital reproduction of artistic work where colour represents essence of art. In our research, color reproductions of renderings of computer generated scenes, including an object, background and lights were assessed, and even more, colorimetric accuracy in renderings was analyzed. With assumption that shading in 3D computer generated scenes plays an important role in visual perception of rendered color, the colors were analyzed on renderings of shaded objects. It was determined that colorimetric accuracy depends on the position on rendered objects, either illuminated or shaded. Furthermore, it was discovered that color lightness and hue, light intensity and background influenced ΔRGB values

Measurement, modeling and perception of painted surfaces : A Multi-scale analysis of the touch-up problem

Real-world surfaces typically have geometric features at a range of spatial scales. At the microscale, opaque surfaces are often characterized by bidirectional reflectance distribution functions (BRDF), which describes how a surface scatters incident light. At the mesoscale, surfaces often exhibit visible texture - stochastic or patterned arrangements of geometric features that provide visual information about surface properties such as roughness, smoothness, softness, etc. These textures also affect how light is scattered by the surface, but the effects are at a different spatial scale than those captured by the BRDF. Through this research, we investigate how microscale and mesoscale surface properties interact to contribute to overall surface appearance. This behavior is also the cause of the well-known touch-up problem in the paint industry, where two regions coated with exactly the same paint, look different in color, gloss and/or texture because of differences in application methods. At first, samples were created by applying latex paint to standard wallboard surfaces. Two application methods- spraying and rolling were used. The BRDF and texture properties of the samples were measured, which revealed differences at both the microscale and mesoscale. This data was then used as input for a physically-based image synthesis algorithm, to generate realistic images of the surfaces under different viewing conditions. In order to understand the factors that govern touch-up visibility, psychophysical tests were conducted using calibrated, digital photographs of the samples as stimuli. Images were presented in pairs and a two alternative forced choice design was used for the experiments. These judgments were then used as data for a Thurstonian scaling analysis to produce psychophysical scales of visibility, which helped determine the effect of paint formulation, application methods, and viewing and illumination conditions on the touch-up problem. The results can be used as base data towards development of a psychophysical model that relates physical differences in paint formulation and application methods to visual differences in surface appearance

Human cortical areas involved in perception of surface glossiness

AbstractGlossiness is the visual appearance of an object's surface as defined by its surface reflectance properties. Despite its ecological importance, little is known about the neural substrates underlying its perception. In this study, we performed the first human neuroimaging experiments that directly investigated where the processing of glossiness resides in the visual cortex. First, we investigated the cortical regions that were more activated by observing high glossiness compared with low glossiness, where the effects of simple luminance and luminance contrast were dissociated by controlling the illumination conditions (Experiment 1). As cortical regions that may be related to the processing of glossiness, V2, V3, hV4, VO-1, VO-2, collateral sulcus (CoS), LO-1, and V3A/B were identified, which also showed significant correlation with the perceived level of glossiness. This result is consistent with the recent monkey studies that identified selective neural response to glossiness in the ventral visual pathway, except for V3A/B in the dorsal visual pathway, whose involvement in the processing of glossiness could be specific to the human visual system. Second, we investigated the cortical regions that were modulated by selective attention to glossiness (Experiment 2). The visual areas that showed higher activation to attention to glossiness than that to either form or orientation were identified as right hV4, right VO-2, and right V3A/B, which were commonly identified in Experiment 1. The results indicate that these commonly identified visual areas in the human visual cortex may play important roles in glossiness perception

Three perceptual dimensions for specular and diffuse reflection

Previous research investigated the perceptual dimensionality of achromatic reflection of opaque surfaces, by using either simple analytic models of reflection, or measured reflection properties of a limited sample of materials. Here we aim to extend this work to a broader range of simulated materials. In a first experiment, we used sparse multidimensional scaling techniques to represent a set of rendered stimuli in a perceptual space that is consistent with participants’ similarity judgments.Participants were presented with one reference object and four comparisons, rendered with different material properties.They were asked to rank the comparisons according to their similarity to the reference, resulting in an efficient collection of a large number of similarity judgments. In order to interpret the space individuated by multidimensional scaling, we ran a second experiment in which observers were asked to rate our experimental stimuli according to a list of 30 adjectives referring to their surface reflectance properties. Our results suggest that perception of achromatic reflection is based on at least three dimensions, which we labelled “Lightness”, “Gloss” and “Metallicity”, in accordance with the rating results. These dimensions are characterized by a relatively simple relationship with the parameters of the physically based rendering model used to generate our stimuli, indicating that they correspond to different physical properties of the rendered materials. Specifically,“Lightness” relates to diffuse reflections, “Gloss” to the presence of high contrast sharp specular highlights and “Metallicity” to spread out specular reflections

A Similarity Measure for Material Appearance

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction