Abstract Coatings are used today for products, ranging from automotive production to electronics and everyday use items. Product design is taking on an increasingly important role, where effect pigments come to the fore, offering a coated surface extra optical characteristics. Individual effect pigments have strong anisotropic, azimuthaly‐dependent behaviour, typically suppressed by a coating application process, randomly orienting pigment particles resulting in isotropic appearance. One exception is a pigment that allows control of the azimuthal orientation of flakes using a magnetic field. We investigate visual texture effects due to such an orientation in a framework allowing efficient capturing, modelling and editing of its appearance. We captured spatially‐varying BRDFs of four coatings containing magnetic effect pigments. As per‐pixel non‐linear fitting cannot preserve coating sparkle effects, we suggest a novel method of anisotropy modelling based on images shifting in an angular domain. The model can be utilized for a fast transfer of desired anisotropy to any isotropic effect coating, while preserving important spatially‐varying visual features of the original coating. The anisotropic behaviour was fitted by a parametric model allowing for editing of coating appearance. This framework allows exploration of anisotropic effect coatings and their appearance transfer to standard effect coatings in a virtual environment.

Various industries are striving for novel, more reliable but still efficient approaches to coatings characterization. Majority of industrial applications use portable instruments for characterization of effect coatings. They typically capture a limited set of in-plane geometries and have limited ability to reliably characterize gonio-apparent behavior typical for such coatings. The instruments rely mostly on color and reflectance characteristics without using a texture information across the coating plane. In this paper, we propose image-based method that counts numbers of effective pigments and their active area. First, we captured appearance of eight effect coatings featuring four different pigment materials, in in-plane and out-of-plane geometries. We used a gonioreflectometer for fixed viewing and varying illumination angles. Our analysis has shown that the proposed method is able to clearly distinguish pigment materials and coating applications in both in-plane and out-of-plane geometries. Finally, we show an application of our method to analysis of spatial non-uniformity, i.e. cloudiness or mottling, across a coated panel.

Paint manufacturers strive to introduce unique visual effects to coatings in order to visually communicate functional properties of products using value-added, customized design. However, these effects often feature complex, angularly dependent, spatially-varying behavior, thus representing a challenge in digital reproduction. In this paper we analyze several approaches to capturing spatially-varying appearances of effect coatings. We compare a baseline approach based on a bidirectional texture function (BTF) with four variants of half-difference parameterization. Through a psychophysical study, we determine minimal sampling along individual dimensions of this parameterization. We conclude that, compared to BTF, bivariate representations better preserve visual fidelity of effect coatings, better characterizing near-specular behavior and significantly the restricting number of images which must be captured.

Material appearance is often represented by a bidirectional reflectance distribution function (BRDF). Although the concept of the BRDF is widely used in computer graphics and related applications, the number of actual captured BRDFs is limited due to a time and resources demanding measurement process. Several BRDF databases have already been provided publicly, yet subjective properties of underlying captured material samples, apart from single photographs, remain unavailable for users. In this article, we analyzed material samples, used in the creation of the UTIA BRDF database, in a psychophysical study with nine subjects and assessed its 12 visual, tactile, and subjective attributes. Further, we evaluated the relationship between the attributes and six material categories. We consider the presented perceptual analysis as valuable and complementary information to the database; that could aid users to select appropriate materials for their applications.

BRDF continues to be used as a fundamental tool for representing material appearance in computer graphics. In this paper we present a practical adaptive method for acquisition of anisotropic BRDF, based on sparse adaptive measurement of the complete four-dimensional BRDF space by means of one-dimensional slices, which form a sparse four-dimensional structure in the BRDF space, and can be measured by continuous movements of a light source and sensor. Such a sampling approach is advantageous especially for gonioreflectometer-based measurement devices where the mechanical travel of a light source and a sensor imposes a significant time constraint. In order to evaluate our method, we have performed adaptive measurements of three materials and we simulated adaptive measurements of thirteen others. This method has one quarter the reconstruction error of that resulting from regular non-adaptive BRDF measurements using the same number of measured samples. Our method is almost twice as good as a previous adaptive method, and it requires from two to five times fewer samples to achieve the same results as alternative approaches.

Abstract One of the most accurate yet still practical representation of material appearance is the Bidirectional Texture Function (BTF). The BTF can be viewed as an extension of Bidirectional Reflectance Distribution Function (BRDF) for additional spatial information that includes local visual effects such as shadowing, interreflection, subsurface‐scattering, etc. However, the shift from BRDF to BTF represents not only a huge leap in respect to the realism of material reproduction, but also related high memory and computational costs stemming from the storage and processing of massive BTF data. In this work, we argue that each opaque material, regardless of its surface structure, can be safely substituted by a BRDF without the introduction of a significant perceptual error when viewed from an appropriate distance. Therefore, we ran a set of psychophysical studies over 25 materials to determine so‐called critical viewing distances, i.e. the minimal distances at which the material spatial structure (texture) cannot be visually discerned. Our analysis determined such typical distances typical for several material categories often used in interior design applications. Furthermore, we propose a combination of computational features that can predict such distances without the need for a psychophysical study. We show that our work can significantly reduce rendering costs in applications that process complex virtual scenes.

Surface reflectance of real-world materials is now widely represented by the bidirectional reflectance distribution function (BRDF) and also by spatially varying representations such as SVBRDF and the bidirectional texture function (BTF). The raw surface reflectance measurements are typically compressed or fitted by analytical models, that always introduce a certain loss of accuracy. For its evaluation we need a distance function between a reference surface reflectance and its approximate version. Although some of the past techniques tried to reflect the perceptual sensitivity of human vision, they have neither optimized illumination and viewing conditions nor surface shape. In this paper, we suggest a new image-based methodology for comparing different anisotropic BRDFs. We use optimization techniques to generate a novel surface which has extensive coverage of incoming and outgoing light directions, while preserving its features and frequencies that are important for material appearance judgments. A single rendered image of such a surface along with simultaneously optimized lighting and viewing directions leads to the computation of a meaningful BRDF difference, by means of standard image difference predictors. A psychophysical experiments revealed that our surface provides richer information on material properties than the standard surfaces often used in computer graphics, e.g., sphere or blob.

Abstract BRDFs are commonly used for material appearance representation in applications ranging from gaming and the movie industry, to product design and specification. Most applications rely on isotropic BRDFs due to their better availability as a result of their easier acquisition process. On the other hand, anisotropic BRDF due to their structure‐dependent anisotropic highlights, are more challenging to measure and process. This paper thus leverages the measurement process of anisotropic BRDF by representing such BRDF by the collection of isotropic BRDFs. Our method relies on an anisotropic BRDF database decomposition into training isotropic slices forming a linear basis, where appropriate sparse samples are identified using numerical optimization. When an unknown anisotropic BRDF is measured, these samples are repeatably captured in a small set of azimuthal directions. All collected samples are then used for an entire measured BRDF reconstruction from a linear isotropic basis. Typically, below 100 samples are sufficient for the capturing of main visual features of complex anisotropic materials, and we provide a minimal directional samples to be regularly measured at each sample rotation. We conclude, that even simple setups relying on five bidirectional samples (maximum of five stationary sensors/lights) in combination with eight rotations (rotation stage for specimen) can yield a promising reconstruction of anisotropic behavior. Next, we outline extension of the proposed approach to adaptive sampling of anisotropic BRDF to gain even better performance. Finally, we show that our method allows using standard geometries, including industrial multi‐angle reflectometers, for the fast measurement of anisotropic BRDFs.

The majority of the materials we encounter in the real-world have variable reflectance when rotated along a surface normal. This view and illumination azimuthally-variable behavior is known as visual anisotropy. Such behavior can be represented by a four-dimensional anisotropic BRDF that characterizes the anisotropic appearance of homogeneous materials. Unfortunately, most past research has been devoted to simplistic three dimensional isotropic BRDFs. In this paper, we analyze and categorize basic types of BRDF anisotropy, use a psychophysical study to assess at which conditions can isotropic appearance be used without loss of details in material appearance. To this end, we tested the human impression of material anisotropy on various shapes and under two illuminations. We conclude that subjects sensitivity to anisotropy declines with increasing complexity of 3D geometry and increasing uniformity of illumination environment. Finally, we derive and perceptually validate a computationally efficient measure of material visual anisotropy.

The development of novel analytical BRDF models, as well as adaptive BRDF sampling approaches, rely on the appropriate BRDF measurement of real materials. The quality of measurements is even more critical when it comes to accurately representing anisotropic materials where the character of anisotropy is unknown (locations of anisotropic highlights, their width, shape, etc.). As currently there is a lack of dense yet noise-free BRDF anisotropic datasets, we introduce such unique measurements of three anisotropic fabric materials. In this paper we discuss a method of dense BRDF data acquisition, post processing, missing values interpolation, and analyze properties of the datasets. Our results are compared with photographs, dense data fitted and generated by two state-of-the art anisotropic BRDF models, and alternative measurements available.

Abstract BRDFs are commonly used to represent given materials’ appearance in computer graphics and related fields. Although, in the recent past, BRDFs have been extensively measured, compressed, and fitted by a variety of analytical models, most research has been primarily focused on simplified isotropic BRDFs. In this paper, we present a unique database of 150 BRDFs representing a wide range of materials; the majority exhibiting anisotropic behavior. Since time‐consuming BRDF measurement represents a major obstacle in the digital material appearance reproduction pipeline, we tested several approaches estimating a very limited set of samples capable of high quality appearance reconstruction. Initially, we aligned all measured BRDFs according to the location of the anisotropic highlights. Then we propose an adaptive sampling method based on analysis of the measured BRDFs. For each BRDF, a unique sampling pattern was computed, given a predefined count of samples. Further, template‐based methods are introduced based on reusing of the precomputed sampling patterns. This approach enables a more efficient measurement of unknown BRDFs while preserving the visual fidelity for the majority of tested materials. Our method exhibits better performance and stability than competing sparse sampling approaches; especially for higher numbers of samples.

In this paper we introduce unique publicly available dense an isotropic BRDF data measurements. We use this dense data as a reference for performance evaluation of the proposed BRDF sparse angular sampling and interpolation approach. The method is based on sampling of BRDF subspaces at fixed elevations by means of several adaptively-represented, uniformly distributed, perpendicular slices. Although this proposed method requires only a sparse sampling of material, the interpolation provides a very accurate reconstruction, visually and computationally comparable to densely measured reference. Due to the simple slices measurement and method's robustness it allows for a highly accurate acquisition of BRDFs. This in comparison with standard uniform angular sampling, is considerably faster yet uses far less samples.

Multidimensional visual texture is the appropriate paradigm for physically correct material visual properties representation. The course will present recent advances in texture modelling methodology as applied in computer vision, pattern recognition, computer graphics, and virtual/augmented reality applications. Contrary to previous courses on material appearance, we will focus on materials whose nature allows the exploitation of texture modeling approaches. This topic is introduced in the wider and complete context of pattern recognition and image processing. It comprehends modeling of multi-spectral images and videos which can be accomplished either with multi-dimensional mathematical models or sophisticated sampling methods from the original measurements. The key aspects of the topic, i.e., different multi-dimensional data models with their corresponding benefits and drawbacks, optimal model selection, parameter estimation and model synthesis techniques, are discussed. These methods produce compact parametric sets that not only faithfully reproduce material appearance, but are also vital for visual scene analysis, e.g. texture segmentation, classification, and retrieval. Special attention is devoted to recent advanced trends towards Bidirectional Texture Function (BTF) modeling, used for materials that do not obey Lambertian law, and whose reflectance has non-trivial illumination and viewing direction dependency. BTFs represent the best known effectively applicable textural representation of most real-world materials' visual properties. The techniques covered include efficient Markov random field-based algorithms, intelligent sampling algorithms, spatially-varying reflectance models and challenges with their possible implementation on GPU. The course also deals with proper data measurement, visualization of texture models in virtual scenes, visual quality evaluation feedback, as well as description of key industrial and research applications. We will discuss options as to which type of material representation is appropriate for required application, what are its limits and possible modelling options, and what the biggest challenges in realistic modelling of materials are.

Illumination and view dependent texture provide ample information on the appearance of real materials at the cost of enormous data storage requirements. Hence, past research focused mainly on compression and modelling of these data, however, few papers have explicitly addressed the way in which humans perceive these compressed data. We analyzed human gaze information to determine appropriate texture statistics. These statistics were then exploited in a pilot illumination and view direction dependent data compression algorithm. Our results showed that taking into account local texture variance can increase compression of current methods more than twofold, while preserving original realistic appearance and allowing fast data reconstruction.

Abstract The Bidirectional Texture Function (BTF) is becoming widely used for accurate representation of real‐world material appearance. In this paper a novel BTF compression model is proposed. The model resamples input BTF data into a parametrization, allowing decomposition of individual view and illumination dependent texels into a set of multi‐dimensional conditional probability density functions. These functions are compressed in turn using a novel multi‐level vector quantization algorithm. The result of this algorithm is a set of index and scale code‐books for individual dimensions. BTF reconstruction from the model is then based on fast chained indexing into the nested stored code‐books. In the proposed model, luminance and chromaticity are treated separately to achieve further compression. The proposed model achieves low distortion and compression ratios 1:233–1:2040, depending on BTF sample variability. These results compare well with several other BTF compression methods with predefined compression ratios, usually smaller than 1:200. We carried out a psychophysical experiment comparing our method with LPCA method. BTF synthesis from the model was implemented on a standard GPU, yielded interactive framerates. The proposed method allows the fast importance sampling required by eye‐path tracing algorithms in image synthesis.

An ever-growing number of real-world computer vision applications require classification, segmentation, retrieval, or realistic rendering of genuine materials. However, the appearance of real materials dramatically changes with illumination and viewing variations. Thus, the only reliable representation of material visual properties requires capturing of its reflectance in as wide range of light and camera position combinations as possible. This is a principle of the recent most advanced texture representation, the Bidirectional Texture Function (BTF). Multispectral BTF is a seven-dimensional function that depends on view and illumination directions as well as on planar texture coordinates. BTF is typically obtained by measurement of thousands of images covering many combinations of illumination and viewing angles. However, the large size of such measurements has prohibited their practical exploitation in any sensible application until recently. During the last few years, the first BTF measurement, compression, modeling, and rendering methods have emerged. In this paper, we categorize, critically survey, and psychophysically compare such approaches, which were published in this newly arising and important computer vision and graphics area.

The use of illumination and view-dependent texture information is recently the best way to capture the appearance of real-world materials accurately. One example is the Bidirectional Texture Function. The main disadvantage of these data is their massive size. In this article, we employ perceptually-based methods to allow more efficient handling of these data. In the first step, we analyse different uniform resampling by means of a psychophysical study with 11 subjects, comparing original data with rendering of a uniformly resampled version over the hemisphere of illumination and view-dependent textural measurements. We have found that down-sampling in view and illumination azimuthal angles is less apparent than in elevation angles and that illumination directions can be down-sampled more than view directions without loss of visual accuracy. In the second step, we analyzed subjects gaze fixation during the experiment. The gaze analysis confirmed results from the experiment and revealed that subjects were fixating at locations aligned with direction of main gradient in rendered stimuli. As this gradient was mostly aligned with illumination gradient, we conclude that subjects were observing materials mainly in direction of illumination gradient. Our results provide interesting insights in human perception of real materials and show promising consequences for development of more efficient compression and rendering algorithms using these kind of massive data.

With significantly increasing number of archived movie sequences a need of their automatic indexation and annotation is raising. Robust and fast temporal segmentation of video sequences is one of the challenging research topics in this area. In this paper we propose a new temporal segmentation method of the video sequences based on PCA approach. Contrary to standard approaches based on histogram or motion field analysis the proposed method does not require any such a complex analysis. The method starts with sparse greyscale sampling and eigen-analysis of input sequence. A sum of absolute derivatives of temporal mixing coefficients of main eigen-images is then used as cuts detection feature, while dissolve transitions are detected by means of coefficients’ specific behaviour. The functionality of the method was successfully tested on number of sequences ranging from artificial set of similar dynamic textures to professional documentary movies. Although, the results may not be unexpected, we believe that proposed method provides novel, very fast and reliable way of movie cuts detection.

Bidirectional Texture Functions (BTF) are commonly thought to provide the most realistic perceptual experience of materials from rendered images. The key to providing efficient compression of BTFs is the decision as to how much of the data should be preserved. We use psychophysical experiments to show that this decision depends critically upon the material concerned. Furthermore, we develop a BTF derived metric that enables us to automatically set a material's compression parameters in such a way as to provide users with a predefined perceptual quality. We investigate the correlation of three different BTF metrics with psychophysically derived data. Eight materials were presented to eleven naive observers who were asked to judge the perceived quality of BTF renderings as the amount of preserved data was varied. The metric showing the highest correlation with the thresholds set by the observers was the mean variance of individual BTF images. This metric was then used to automatically determine the material-specific compression parameters used in a vector quantisation scheme. The results were successfully validated in an experiment with six additional materials and eighteen observers. We show that using the psychophysically reduced BTF data significantly improves performance of a PCA-based compression method. On average, we were able to increase the compression ratios, and decrease processing times, by a factor of four without any differences being perceived.

The recent advanced representation for realistic real-world materials in virtual reality applications is the Bidirectional Texture Function (BTF) which describes rough texture appearance for varying illumination and viewing conditions. Such a function can be represented by thousands of measurements (images) per material sample. The resulting BTF size excludes its direct rendering in graphical applications and some compression of these huge BTF data spaces is obviously inevitable. In this paper we present a novel, fast probabilistic model-based algorithm for realistic BTF modeling allowing an extreme compression with the possibility of a fast hardware implementation. Its ultimate aim is to create a visual impression of the same material without a pixel-wise correspondence to the original measurements. The analytical step of the algorithm starts with a BTF space segmentation and a range map estimation by photometric stereo of the BTF surface, followed by the spectral and spatial factorization of selected sub-space color texture images. Single mono-spectral band-limited factors are independently modeled by their dedicated spatial probabilistic model. During rendering, the sub-space images of arbitrary size are synthesized and both color (possibly multi-spectral) and range information is combined in a bump-mapping filter according to the view and illumination directions. The presented model offers a huge BTF compression ratio unattainable by any alternative sampling-based BTF synthesis method. Simultaneously this model can be used to reconstruct missing parts of the BTF measurement space.