Bidirectional Texture Function (BTF) is one of the methods to reproduce realistic images in Computer Graphics (CG). This is a technique that can be applied to texture mapping with changing lighting and viewing directions and can reproduce realistic appearance by a simple and high-speed processing. However, in the BTF method, a large amount of texture data is generally measured and stored in advance. In this paper, in order to address the problems related to the measurement time and the texture data size in the BTF reproduction, we a method to generate a BTF image dataset using deep learning. We recovery texture images under various azimuth lighting conditions from a single texture image. For achieving this goal, we applied the U-Net to our BTF recovery. The restored and original texture images are compared using SSIM. It will be confirmed that the reproducibility of fabric and wood textures is high.

Caustics projected onto the surface carry very interesting information regarding the material they are cast by. It has been observed in previous studies that caustics could be a widely used cue for translucency assessment by human subjects. We hypothesize that changing the reflectance properties of the surface an object is placed on, and removal of the caustic pattern might impact perceived translucency of the material. We conducted psychophysical experiments to investigate the correlation among caustics, environment colors and translucency perception, and found very interesting indications that materials appear less translucent under the conditions where caustics are absent.

This work takes a step towards understanding fundamental aspects of appearance change in cultural heritage. Particularly, we concentrate on the case study of the Hedal Madonna – a polychrome wood sculpture dated to the mid-1200s, and an important object of ecclesiastical art and Norwegian heritage. It is covered with a layered coating that gives rise to complex reflective properties and gives the sculpture a unique appearance. We studied the goniometric, spectral, and chromatic properties of mock-ups manufactured according to medieval techniques, and also carried out accelerated aging. We compared the properties of aged and original mock-ups, and found non-trivial changes in polychrome appearance. While the color of the mock-ups did not change significantly, we observed a noticeable change in their glossiness, presumably caused by structural degradation of the surface. We also found a difference in the goniometric properties of the polychrome material’s reflectance as a function of angle. Reflectance distributions that were originally symmetric with respect to angle became asymmetric. These findings will help to understand the original appearance of the Hedal Madonna, as well as aiding the design of appropriate conservation conditions for both the original statue and its recent reconstruction.

Estimating skin color from an uncontrolled facial image is a challenging task. Many factors such as illumination, camera and shading variations directly affect the appearance of skin color in the image. Furthermore, using a color calibration target in order to correct the image pixels leads to a complex user experience. We propose a skin color estimation method from images in the wild, taken with unknown camera, under an unknown lighting, and without a calibration target. While prior methods relied on explicit intermediate steps of color correction of image pixels and skin region segmentation, we propose an end-to-end color regression model named LabNet, in which color correction and skin region segmentation are implicitly learnt by the model. Our method is based on a convolutional neural network trained on a dataset of smartphone images, labeled with L*a*b* measures of skin colors. We compare our method with standard skin color estimation approaches and found that our method over-perform these models while removing the need of color calibration target.

In this paper we present a set of multispectral images covering the visible and near-infrared spectral range (400 nm to 1050 nm). This dataset intends to provide spectral reflectance images containing daily life objects, usable for silicon image sensor simulations. All images were taken with our acquisition bench and a particular attention was brought to processings in order to provide calibrated reflectance data. ReDFISh (Reflectance Dataset For Image sensor Simulation) is available at: http://dx.doi.org/10.18709/perscido.2020.01.ds289.

We publish two carefully prepared and spectrally measured datasets of paint swatches. The main advantage of these datasets is that a diverse set of paint mixtures are manually prepared the way an artist may create them. The ratio of paints in each mixture is also published. The first set has 286 swatches made from 8 tubes of Old Holland oil paints in different combinations. The second set has 397 swatches made from 9 tubes of Schmincke watercolor paints. We provide exact details about the preparation of our swatches. We analyze the colorimetric and spectral properties of the two datasets in order to show the spread of the colorimetric gamut and the intrinsic, spectral dimensionality of the datasets. The dataset will be available on http://cam.mpi-inf.mpg.de/ and http://www.azadehasadi.com/.

Designers need to specify the colors for their 3D objects in form of sRGB values, but, given the limitations of the color 3D printing process, they have no idea how those colors chosen on a screen will look once printed in 3D. In addition, HP Inc. wants to showcase the color capabilities of our 3D color printing systems in an effective way. This paper describes an aesthetically pleasing tool to effectively showcase the color capabilities of our color 3D printing systems. It is also a reference color system that enables designers 1) to select colors that are achievable with our printing systems, 2) to interactively composite color palettes for their 3D design and 3) to get the desired printed color in a time and cost-efficient way that minimizes iterations. The system itself consists of a series of subobjects where each sub-object shows how a color looks like when manufactured in different surface orientations. It can be disassembled and used for compositing color palettes for 3D objects, and it is also designed to be manufactured and cleaned fully assembled, showcasing the power of 3D printing.

The quantification of material appearance is important in product design. In particular, the sparkle impression of metallic paint used mainly for automobiles varies with the observation angle. Although several evaluation methods and multi-angle measurement devices have been proposed for the impression, it is necessary to add more light sources or cameras to the devices to increase the number of evaluation angles. The present study constructed a device that evaluates the multi-angle sparkle impression in one shot and developed a method for quantifying the impression. The device comprises a line spectral camera, light source, and motorized rotation stage. The quantification method is based on spatial frequency characteristics. It was confirmed that the evaluation value obtained from the image recorded by the constructed device correlates closely with a subjective score. Furthermore, the evaluation value is significantly correlated with that obtained using a commercially available evaluation device.