News from Material Appearance 2020

10 février 2020 par GDR APPAMAT
The conference "Material Appearance 2020", hosted by the Electronic Imaging Symposium in Burlingame, CA, USA on the 27th and 28th January 2020, was rather exciting for the number of different topics adressed, including measurement and modelization issues, physical and digital methods, and a variety of materials and appearance attributes. A short summary by Mathieu Hebert, chair of the conference, is delivered here.
  • 3D scanning: Scanning the 3D shape of objects at very high resolution is of high importance in the cultural heritage domain. Martin Ritz (invited speaker) presented various generations of scanners at Fraunhofer IGD, Germany. He insisted on the necessity to acquire, in addition to the 3D shape, the optical behaviour of the surface in order to achieve good rendering when the reconstructed object is diplayed on digital support. Issues related to texture mapping are also addressed. Impressive images of metallized objects were shown.
  • Multispectral image devices are still developed, while trying to improve the acquisition performences and accuracy, as shown by Axel Clouet from CEA-LETI, France, who also announced the creation of a spectral databased for labelled objects.
From measurement to prediction
  • BRDF: The microfacet model on which rely most BRDF models for light scattering by rough surfaces is usually based on one roughness parameter, the standard deviation of facet slope. In the case of very glossy surfaces, this parameter is difficult to estimate from BRDF measurements because of the apparatus function of the instrument, unless a very high precision goniophotometer is used. Shoji Tominaga from NTNU, Norway, explained that it is preferrable to use a glossmeter to obtain more accurate roughness parameter estimation, and btter matching between BRDF model and measurement.
  • BTF: Naoki Tada from Chiba University, Japan, showed how CNN can be used to predict the variation of bi-direction texture function (BTF) according to the position of the light source. The neural network is trained and used for various texture categories, separate from each other.
  • Renewing: Is it possible, from a given picture of old car or building, to virtually renew the object in the image as if it was new? Runa Takahashi from Yokohama National University, Japan showed that this can be acheived thanks to neural networks (especially GANs), by removing dust and stains form the object, reinforcing the color saturation, and increasing slightly the gloss contrast. Convincing results were shown.
Visual attributes
  • Sparkle: It is possible to record the dynamic light signal at the origin of the sensation of sparckle into one image? A clever idea in this direction has been defended by Shuuhei Watanabe from Ricoh and Chiba University, Japan. A line scan spectral camera captures a spectral image of the surface while a collimated light source is rotating around it: each pixel column in the image therefore corresponds to one illumination direction. Statistical properties of the image are then analyzed and correlation with visual sparkle assessment is performed.
  • Translucency: How do caustics help observers to assess the sensation of translucency? Davit Gigilashvili from NTNU, Norway, laureate of the Best student paper award of the conference, simulated various traslucent objects with and without causics and showed that the caustics clearly provide information on the object’s translucency and their presence increase the sensation of translucency in comparison to the case where they are not visible. Jon Hardeberg, NTNU, Norway, (invited) recalls that
  • A pine resin coating on wood, used in ancien Norvegian culture, has the interesting property to imitate gold. The question addressed by Oleksii Sidorov from NTNU, Norway, is the ageing of these objects. BRDF measurements were performed and compared on samples artificially aged.
  • The use of AI technics for human skin is definitely a growing research domain. Assessing the color of skin into color pictures is a crucial issue for the e-market of cosmetic products, but remains a challenge in absence of color calibration of the camera. When a color target is displayed in the fireld of the camera, color calibration is possible, but it is known that its performence is better if the color of lighting is known. Robin Kips from L’Oréal, France, shows that the color temperature of the lighting can be estimated by using neural networks, which sensibly increases the color calibration accuracy, therefore the one of the skin color estimation. Regarding skin health, Ahmed Mohammed from NTNU, Norway, explains how neural network can improve skin oxygenation analysis from multispectral images.
  • Paint: For who wants to develop prediction models for the spectral reflectance or the color of paint mixtures, the challenge is often to make good, uniform samples where the amounts of the different primary paints is precisely known. Vahid Babaei et Azadeh A. Shahmirzadi from Max Planck Institute, Germany, produced two color charts based on oil paints and watercolor and created a free database, given as supplemetary material of their paper.
  • 3D printing: Ingeborg Tastl from HP Labs, USA, presented the recent progresses and remaining challenges in material appearance reproduction by 3D printing, and exposed a 3D color gamut.

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