Jiří Filip

Black materials play a critical role in applications such as image registration, camera calibration, stray light suppression, and visual design. Although many such materials appear similarly dark under diffuse illumination, their reflectance behavior can differ substantially as a function of viewing and lighting geometry. Ultra-black materials achieve exceptional light attenuation but are often constrained by cost and mechanical fragility, motivating the evaluation of more robust and accessible alternatives. In this study, we employ a gonimetric measurement system to capture the isotropic bidirectional reflectance distribution function (BRDF) of a range of black materials, including the ultra-black reference Vantablack, commercially available alternatives such as Musou black and black velvet, and standard matte black coatings. We analyze their reflectance characteristics in terms of diffuse and specular scattering, as well as total integrated scatter, to quantify angular-dependent reflection. In addition, we compare their perceptual appearance using physically based rendering driven by the measured BRDFs and a psychophysical evaluation of perceived darkness. Together, these analyses provide a comprehensive assessment of black materials that links reflectance properties to visual appearance and perceptual performance, enabling informed material selection for optical applications.

Black materials play a critical role in applications such as image registration, camera calibration, stray light suppression, and visual design. Although many such materials appear similarly dark under diffuse illumination, their reflectance behavior can differ substantially as a function of viewing and lighting geometry. Ultra-black materials achieve exceptional light attenuation but are often constrained by cost and mechanical fragility, motivating the evaluation of more robust and accessible alternatives. In this study, we employ a gonimetric measurement system to capture the isotropic bidirectional reflectance distribution function of a range of black materials, including the ultra-black reference Vantablack, commercially available alternatives such as Musou Black and black velvet, and standard matte black coatings. We analyze their reflectance characteristics in terms of diffuse and specular scattering, as well as total integrated scatter, to quantify angular-dependent reflection. In addition, we compare their perceptual appearance using physically based rendering driven by the measured BRDFs and a psychophysical evaluation of perceived darkness. Together, these analyses provide a comprehensive assessment of black materials that links reflectance properties to visual appearance and perceptual performance, enabling informed material selection for optical applications.

Digital representation of materials is crucial in fields such as virtual reality, industrial design and quality control. However, predicting human perception of materials from image data is challenging due to the complexity of material appearances and the intricacies of human vision. This study introduces a perceptual representation termed the ‘visual fingerprint’, linking image-based measurements of materials to intuitive, human-understandable attributes. We conducted psychophysical studies using standardized video sequences of 347 diverse real-world materials, including fabrics and wood, selected to encompass a broad spectrum of textures, colours and reflective properties. Sixteen key appearance attributes were identified, and over 110 000 human ratings were collected to map perceptual attributes across material categories. By integrating CLIP-derived image features with a multi-layer perceptron model, we developed a predictive framework for material perception. Our results demonstrate that human judgements of appearance and similarity can be accurately predicted using only two images of a material. This work offers a practical and interpretable approach to material representation, enabling intuitive comparisons and retrievals in applications where material appearance is crucial. The proposed material fingerprint and its prediction directly from image data represent a significant step towards simplifying the understanding and interoperability of material properties in diverse digital environments.

Materials exhibit an extraordinary range of visual appearances. Characterizing and quantifying appearance is important not only for basic research on perceptual mechanisms but also for computer graphics and a wide range of industrial applications. Although methods exist for capturing and representing the optical properties of materials and how they vary across surfaces (Haindl & Filip, 2013), the representations are typically very high-dimensional, and how these representations relate to subjective perceptual impressions of material appearance remains poorly understood. Here, we used a data-driven approach to characterizing the perceived appearance characteristics of 30 samples of wood veneer using a "visual fingerprint" that describes each sample as a multidimensional feature vector, with each dimension capturing a different aspect of the appearance. Fifty-six crowd-sourced participants viewed triplets of movies depicting different wood samples as the sample rotated. Their task was to report which of the two match samples was subjectively most similar to the test sample. In another online experiment, 45 participants rated 10 wood-related appearance characteristics for each of the samples. The results reveal a consistent embedding of the samples across both experiments and a set of nine perceptual dimensions capturing aspects including the roughness, directionality, and spatial scale of the surface patterns. We also showed that a weighted linear combination of 11 image statistics, inspired by the rating characteristics, predicts perceptual dimensions well.

Materials exhibit an extraordinary range of visual appearances. Characterising and quantifying appearance is important not only for basic research on perceptual mechanisms, but also for computer graphics and a wide range of industrial applications. While methods exist for capturing and representing the optical properties of materials and how they vary across surfaces (Haindl & Filip., 2013), the representations are typically very high-dimensional, and how these representations relate to subjective perceptual impressions of material appearance remains poorly understood. Here, we used a data-driven approach to characterising the perceived appearance characteristics of 30 samples of wood veneer using a ‘visual fingerprint’ that describes each sample as a multidimensional feature vector, with each dimension capturing a different aspect of the appearance. Fifty-six crowd-sourced participants viewed triplets of movies depicting different wood samples as the sample rotated. Their task was to report which of the two match samples was subjectively most similar to the test sample. In another online experiment 45 participants rated ten wood-related appearance characteristics for each of the samples. The results reveal a consistent embedding of the samples across both experiments and a set of 9 perceptual dimensions capturing aspects including the roughness, directionality and spatial scale of the surface patterns. We also showed that a weighted linear combination of eleven image statistics, inspired by the rating characteristics, predicts perceptual dimensions well.

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.