A photo-realistic representation of material appearance can be achieved by means of bidirectional texture function (BTF) capturing a material’s appearance for varying illumination, viewing directions, and spatial pixel coordinates. BTF captures many non-local effects in material structure such as inter-reflections, occlusions, shadowing, or scattering. The acquisition of BTF data is usually time and resource-intensive due to the high dimensionality of BTF data. This results in expensive, complex measurement setups and/or excessively long measurement times. We propose an approximate BTF acquisition setup based on a simple, affordable mechanical gantry containing a consumer camera and two LED lights. It captures a very limited subset of material surface images by shooting several video sequences. A psychophysical study comparing captured and reconstructed data with the reference BTFs of seven tested materials revealed that results of our method show a promising visual quality. Speed of the setup has been demonstrated on measurement of human skin and measurement and modeling of a glue dessication time-varying process. As it allows for fast, inexpensive, acquisition of approximate BTFs, this method can be beneficial to visualization applications demanding less accuracy, where BTF utilization has previously been limited.

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.

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.