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Jiri Filip

Publications and source records attributed to Jiri Filip.

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How Dark is Dark? A Reflectance and Scattering Analysis of Black Materials

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.

physics.optics

Material Fingerprinting: Identifying and Predicting Perceptual Attributes of Material Appearance

The world is abundant with diverse materials, each possessing unique surface appearances that play a crucial role in our daily perception and understanding of their properties. Despite advancements in technology enabling the capture and realistic reproduction of material appearances for visualization and quality control, the interoperability of material property information across various measurement representations and software platforms remains a complex challenge. A key to overcoming this challenge lies in the automatic identification of materials' perceptual features, enabling intuitive differentiation of properties stored in disparate material data representations. We reasoned that for many practical purposes, a compact representation of the perceptual appearance is more useful than an exhaustive physical description.This paper introduces a novel approach to material identification by encoding perceptual features obtained from dynamic visual stimuli. We conducted a psychophysical experiment to select and validate 16 particularly significant perceptual attributes obtained from videos of 347 materials. We then gathered attribute ratings from over twenty participants for each material, creating a 'material fingerprint' that encodes the unique perceptual properties of each material. Finally, we trained a multi-layer perceptron model to predict the relationship between statistical and deep learning image features and their corresponding perceptual properties. We demonstrate the model's performance in material retrieval and filtering according to individual attributes. This model represents a significant step towards simplifying the sharing and understanding of material properties in diverse digital environments regardless of their digital representation, enhancing both the accuracy and efficiency of material identification.

cs.CV