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The internal structure of hair consists of three layers: the cuticle, the cortex, and the medulla, and there are multiple membrane structures inside the cuticle. Light incident on these subwavelength structures is subject to scattering and interference phenomena, and the reflected highlights vary depending on the viewing angle. This phenomenon of light coloration due to the microstructure is called structural coloring or photonic coloration. It shows a unique specular highlight and determines the magnitude of the specular highlight, which is not observable in the linear optics of CG. In the case of hairs, this structural coloring or photonic coloration occurs in addition to the simple surface reflections and the backscattering lights, which penetrate the hair and are absorbed by the melanin pigment. In the present report, we mainly discuss the effects of the structural coloring first caused by the multi-layered structure in the cuticle region on the hair surface, simulating the electromagnetic field using the NS-FDTD (Non-Standard Finite Difference in Time Domain) method. In addition, we also discuss the backscattering phenomena inside the hair fibers.

Specular highlight removal is a challenging task. We present a novel data-driven approach for automatic specular highlight removal from a single image. To this end, we build a new dataset of real-world images for specular highlight removal with corresponding ground-truth diffuse images. Based on the dataset, we also present a specular highlight removal network by introducing the detection of specular reflections information as guidance. The experimental evaluations indicate that the proposed approach outperforms recent state-of-the-art methods.

Specular highlight detection is a challenging problem, and has many applications such as shiny object detection and light source estimation. Although various highlight detection methods have been proposed, they fail to disambiguate bright material surfaces from highlights, and cannot handle non-white-balanced images. Moreover, at present, there is still no benchmark dataset for highlight detection. In this paper, we present a large-scale real-world highlight dataset containing a rich variety of material categories, with diverse highlight shapes and appearances, in which each image is with an annotated ground-truth mask. Based on the dataset, we develop a deep learning-based specular highlight detection network (SHDNet) leveraging multi-scale context contrasted features to accurately detect specular highlights of varying scales. In addition, we design a binary cross-entropy (BCE) loss and an intersection-over-union edge (IoUE) loss for our network. Compared with existing highlight detection methods, our method can accurately detect highlights of different sizes, while effectively excluding the non-highlight regions, such as bright materials, non-specular as well as colored lighting, and even light sources.

In this work, we present a method to turn still life paintings with global illumination effects into dynamic paintings with moving lights. Our method specifically focuses on still life images containing (1) glass objects, which can have specular highlights with Fresnel effect and (2) fruits, which can have reflection and subsurface scattering. Our goal is to preserve the original look-and-feel of the still-life paintings while allowing the user to move the light source anywhere in the scene, causing the shadows, diffuse shading, as well as reflections and specular highlights to move according to the new light position. We have provided a proof of concept based on an original digital painting. This method can be used to turn any similar still life painting into a dynamic painting.

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. 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. © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, DOI 10.1145/3380741

The appearance of objects is significantly affected by the illumination conditions in the environment. Particularly with objects that have strong reflectivity as they suffer from more dominant specular highlights, causing information loss and discontinuity in the image domain. Many computer vision algorithms are vulnerable to errors in the presence of specular highlights because they violate the image consistency assumption and hinder the performance of many vision tasks, such as object recognition, tracking and surface reconstruction [Artusi et al. 2011]. This is further complicated when we consider video sequences with free-moving cameras or dynamic objects, which is the focus of this work.

Developed by Epic Games, Elemental is the first real-time demonstration showcasing its next-generation Unreal Engine 4 technology. The demo presents rendering features utilizing DirectX 11 hardware. It takes per-pixel deferred shading to the next level: energy-conserving specular highlights from area lights and shadowed reflections from emissive materials creating area lights with shadowing, dynamic global illumination that affects both opaque and translucent materials, glossy surfaces that feature accurate reflections, subsurface scattering, and deferred decals. All lighting is complemented by the new GPU-accelerated particle simulation and the new post-processing pipeline.

The shape we perceive from a rendered surface depends on the true surface shape, but also on the surface reflectance, the illumination, the surface motion, and the viewer's position. Here we present an experiment that addresses how perceived shape depends on shading and specular reflections, where the latter include highlights [Blake and Bulthoff 1990] and environment mapped mirror reflections[Fleming et al. 2004].

We introduce Linear Efficient Antialiased Normal (LEAN) Mapping, a method for real-time filtering of specular highlights in bump and normal maps. The method evaluates bumps as part of a shading computation in the tangent space of the polygonal surface rather than in the tangent space of the individual bumps. By operating in a common tangent space, we are able to store information on the distribution of bump normals in a linearly-filterable form compatible with standard MIP and anisotropic filtering hardware. The necessary textures can be computed in a preprocess or generated in real-time on the GPU for time-varying normal maps. The method effectively captures the bloom in highlight shape as bumps become too small to see, and will even transform bump ridges into anisotropic shading. Unlike even more expensive methods, several layers can be combined cheaply during surface rendering, with per-pixel blending. Though the method is based on a modified Ward shading model, we show how to map between its parameters and those of a standard Blinn-Phong model for compatibility with existing art assets and pipelines, and demonstrate that both models produce equivalent results at the largest MIP levels.

Measurement of spatially varying BRDFs of real world materials has been an active area of research in computer graphics with image-based measurements being the preferred approach in practice. In order to restrict the total number of measurements, existing techniques typically trade spatial variation for angular variation of the surface BRDF [Marschner et al. 1999]. Recently, Ma et al. [2007] introduced a technique for estimating high quality specular normals and albedo (Fig. 1, (a) & (b) respectively) of a specular object using polarized first order spherical gradient illumination conditions. In this work, we extend this technique to estimate per pixel specular roughness using polarized second order spherical gradients as a measure of the variance about the mean (reflection vector). We demonstrate that for isotropic BRDFs, only three spherical gradient illumination patterns related to the second order spherical harmonics are sufficient for a robust estimate of per pixel specular roughness (Fig. 1, (c)). Thus, we go further than previous work on image-based measurement of specular BRDFs that typically obtain sparse estimates of the spatial variation. Our technique also provides a direct estimate of the per pixel specular roughness and hence has the added advantage of not requiring any off-line numerical optimization that is typical of the measure and fit approach to BRDF modeling.

Separation of the diffuse and specular components of observed reflectance has been an active area of research in computer graphics and vision, with major applications in reflectance modeling and scene analysis. Traditionally, researchers have investigated diffusespecular separation under point or directional illumination conditions while employing polarization and, in the case of dielectric materials, color space analysis techniques. Recently, Ma et al. [2007] introduced a technique for estimating high quality diffuse and specular normals and albedo maps (see Fig. 1, (a) & (d)) of a specular object using polarized spherical gradient illumination. However, the employed polarization technique imposes view-point restriction, and results in insufficient light levels for performance capture with high speed acquisition. Hence, in this work, we look into an alternate diffuse-specular separation technique for spherical gradients based on a data-driven reflectance model. Traditional separation techniques based on color space analysis focus on removing specular reflections from the observation for scene analysis [Mallick et al. 2005]. In contrast, we focus on obtaining high quality estimates of both the diffuse and the specular reflectance components.

Real world materials exhibit characteristic surface reflectance, such as glossy or specular highlights, and anisotropy that need to be modeled accurately for realistic rendering applications. The surface reflectance of a material is formalized by the notion of the Bidirectional Reflectance Distribution Function (BRDF). The acquisition of real world BRDF data, particularly with image-based techniques, has been a very active area of research over the last few years. Independent of the acquisition process, the acquired data is generally not used directly due to its large size, the noise present in the measurement process, and missing data for certain incident and exitant directions. Instead, the data is usually either fitted to an analytical model or projected into a suitable basis as a post-process. Recently, Ghosh et al. [2007] have proposed an alternative approach where the BRDF data is optically projected into a suitable basis function directly during the capture process. This speeds up acquisition time to one or two minutes compared to a few hours required by traditional approaches. They develop a set of basis functions for this purpose that are similar to the spherical harmonics basis and are orthonormal over the zone of directions that can be simultaneously covered with their optical setup.

Three-dimensional shape may be perceived from static images. Contours, shading, texture gradients, perspective and occlusion are well-studied cues to this percept. When looking at a picture of a specular object, such as a silver vase, one additional cue is potentially available: a deformed picture of the reflected environment is seen in the surface of the object and the amount and type of deformation depend on its shape. Can specular reflections be used as a visual cue for shape perception? Our experiments show that our subjects are very poor at judging the shape of mirror surfaces in absence of other visual cues. However, for a considerable subset of the stimuli, subjects are highly consistent in their (most often wrong) perception. This observation leads us to the hypothesis that our subjects rather than 'computing' a percept from each image based on geometrical considerations, may be associating a shape to each pattern in a stereotypical way, akin to pattern-matching. This behavior is reasonable since, as suggested by our ideal observer analysis, the information available from specular reflections is ambiguous when the surrounding world is (partially) unknown.

In this paper we introduce a new perceptual metric for efficient, high quality, global illumination rendering. The metric is based on a rendering-by-components framework in which the direct, and indirect diffuse, glossy, and specular light transport paths are separately computed and then composited to produce an image. The metric predicts the perceptual importances of the computationally expensive indirect illumination components with respect to image quality. To develop the metric we conducted a series of psychophysical experiments in which we measured and modeled the perceptual importances of the components. An important property of this new metric is that it predicts component importances from inexpensive estimates of the reflectance properties of a scene, and therefore adds negligible overhead to the rendering process. This perceptual metric should enable the development of an important new class of efficient global-illumination rendering systems that can intelligently allocate limited computational resources, to provide high quality images at interactive rates.

Shading is a technique that is used in computer graphics to make faceted objects appear smooth and more realistic. In the research presented in this thesis we have investigated how shading can be generated as efficiently as possible without sacrificing quality.In the classical approach to high quality shading proposed by Phong, the illumination equation is computed per pixel using an interpolated normal. The normals at the vertices are bi-linearly interpolated over the polygon to obtain a normal per pixel. Correct shading requires normalization of these normals, which is computationally demanding involving a square root. In our research we have shown how this normalization can be eliminated through the use of spherical interpolation and the Chebyshev recurrence formula, reducing the calculation to a few single arithmetic operations per pixel.Still a substantial setup operation is needed for each scanline. We have studied how also this can be made more efficient, with some limited progress so far. An alternative approach is to do the most of the setup on polygon level and incrementally compute the setup needed per scanline. In particular, we have studied quadratic shading approaches, i.e. fitting second degree surfaces to the polygons. The most successful approach has been through what we have called X-shading, where the setup is calculated by using an efficient approximation for the mid-edge normals. This setup is about four times faster than previously known methods.In the process of studying shading methods we have also made some contributions to improving bump-mapping and simulation of different kinds of light sources.The developed methods will be of interest in future generations of computer graphics software and hardware systems, ranging from high end systems to generate realistic movies and 3D games, to handheld devices such as mobile phones with graphics displays.

Light scattering from hair is normally simulated in computer graphics using Kajiya and Kay's classic phenomenological model. We have made new measurements of scattering from individual hair fibers that exhibit visually significant effects not predicted by Kajiya and Kay's model. Our measurements go beyond previous hair measurements by examining out-of-plane scattering, and together with this previous work they show a multiple specular highlight and variation in scattering with rotation about the fiber axis. We explain the sources of these effects using a model of a hair fiber as a transparent elliptical cylinder with an absorbing interior and a surface covered with tilted scales. Based on an analytical scattering function for a circular cylinder, we propose a practical shading model for hair that qualitatively matches the scattering behavior shown in the measurements. In a comparison between a photograph and rendered images, we demonstrate the new model's ability to match the appearance of real hair.

This sketch presents fractal art work created by ray tracing the specular highlights of point lights on the inside surface of a hollow sphere. The sphere has a mirror surface on the inside that contributes no colour to the images, but there is spread in the local specular reflection. The resulting images consist entirely of specular highlights and their reflections. I call this painting with light because, although recursive ray tracing is used, no objects are visible.

Complex specular surfaces under sharp point lighting show a fascinating glinty appearance, but rendering it is an unsolved problem. Using Monte Carlo pixel sampling for this purpose is impractical: the energy is concentrated in tiny highlights that take up a minuscule fraction of the pixel. We instead compute an accurate solution using a completely different deterministic approach. Our method considers the true distribution of normals on a surface patch seen through a single pixel, which can be highly complex. We show how to evaluate this distribution efficiently, assuming a Gaussian pixel footprint and Gaussian intrinsic roughness. We also take advantage of hierarchical pruning of position-normal space to rapidly find tex-els that might contribute to a given normal distribution evaluation. Our results show complex, temporally varying glints from materials such as bumpy plastics, brushed and scratched metals, metallic paint and ocean waves. Copyright © ACM.