Radomír Vávra's research while affiliated with The Czech Academy of Sciences and other places

The aim of this article is to propose a model to automatically predict visual judgement of sparkle and graininess of special effect pigments used in industrial coatings. Many applications in the paint and coatings, printing and plastics industry rely on multi-angle color measurements with the aim of properly characterizing the appearance, i.e., the color and texture of the manufactured surfaces. However, when it comes to surfaces containing effect pigments, these methods are in many cases insufficient and it is particularly texture characterization methods that are needed. There are two attributes related to texture that are commonly used: (1) diffuse coarseness or graininess and (2) sparkle or glint impression. In this paper, we analyzed visual perception of both texture attributes using two different psychophysical studies of 38 samples painted with effect coatings including different effect pigments and 31 test persons. Our previous work has shown a good agreement between a study using physical samples with one that uses high-resolution photographs of these sample surfaces. We have also compared the perceived (1) graininess and (2) sparkle with the performance of two commercial instruments that are capable of capturing both attributes. Results have shown a good correlation between the instruments’ readings and the psychophysical studies. Finally, we implemented computational models predicting these texture attributes that have a high correlation with the instrument readings as well as the psychophysical data. By linear scaling of the predicted data using instruments readings, one can use the proposed model for the prediction of graininess and both static and dynamic sparkle values.

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.

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.

The aim of this article is to demonstrate a new way of measuring and understanding the appearance of pigment flake orientation and texture in special effect pigments for use in industrial coatings. We have used diffractive pigments and analyzed the relative orientation of the particles in the coating layers by evaluating their behavior in two common industry applications: solventborne and powder coatings. We have measured the interference color by taking readings with a high-resolution gonioreflectometer, in order to test the viability of automatic diffractive pigment evaluation. The results were analyzed using both psychophysical (i.e., human) and computational (i.e., mechanical) methods. Our later psychophysical and computational analysis of the visual differences that diffractive pigments present in both solventborne (1) and powder coating (2) systems for in-plane and out-of-plane geometries revealed that solventborne liquid paint systems better preserve the appearance of original diffraction gratings. This is due to enhanced orientation of the anisotropic pigment particles. The powder coating surfaces investigated, on the other hand, preserved higher intensity and thus visibility in randomly oriented solitary flakes, creating a greater sparkle contrast. We confirmed our findings by capturing and visualizing coating appearance by means of a bidirectional texture function. We then compared the diffractive pigment evaluation results with other state-of-the-art measuring device readings. We believe that our work provides valuable information on flake orientation and also compares pigment performance in a range of industrial coating systems, which may enable industrial companies to improve paint spraying processes.

Many representations and rendering techniques have been proposed for presenting material appearance in computer graphics. One outstanding problem is evaluating their accuracy. In this paper, we propose assessing accuracy by comparing human judgements of material attributes made when viewing a computer graphics rendering to those made when viewing a physical sample of the same material. We demonstrate this approach using 16 diverse physical material samples distributed to researchers at the MAM 2014 workshop. We performed two psychophysical experiments. In the first experiment, we examined how consistently subjects rate a set of twelve visual, tactile and subjective attributes of individual physical material specimens. In the second experiment, we asked subjects to assess the same attributes for identical materials rendered as BTFs under point-light and environment illuminations. By analyzing obtained data, we identified which material attributes and material types are judged consistently and to what extent the computer graphics representation conveyed the experience of viewing physical material appearance.

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 fourdimensional 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.

We present a practical adaptive method for acquisition of the anisotropic BRDF. It is based on a 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 which can be measured by continuous movements of a light source and a sensor. Such a sampling approach is advantageous especially for gonioreflectometer-based measurement devices where the mechanical travel of a light source and a sensor creates a significant time constraint. In order to evaluate our method, we perform adaptive measurements of three materials and we simulate adaptive measurements of ten others. We achieve a four-times lower reconstruction error in comparison with the regular non-adaptive BRDF measurements given the same count of measured samples. Our method is almost twice better than a previous adaptive method, and it requires from two-to five-times less samples to achieve the same results as alternative approaches.