MAM2020: Eurographics Workshop on Material Appearance Modeling

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https://diglib.eg.org/handle/10.2312/2632928

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    On the Nature of Perceptual Translucency
    (The Eurographics Association, 2020) Gigilashvili, Davit; Thomas, Jean-Baptiste; Hardeberg, Jon Yngve; Pedersen, Marius; Klein, Reinhard and Rushmeier, Holly
    Translucency is an appearance attribute used to characterize materials with some degree of subsurface light transport. Although translucency as a radiative transfer inside the medium is relatively well understood, translucency as a perceptual attribute leaves much room for interpretation. Our understanding of the translucency perception mechanisms of the human visual system remains limited. No agreement exists on how to quantify perceived translucency, how to compare translucency of multiple objects and materials, how translucency relates to transparency and opacity, and what are the perceptual dimensions of it. We highlight the challenges in perception research arisen by these ambiguities and argue for the need for standardization.
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    Bonn Appearance Benchmark
    (The Eurographics Association, 2020) Merzbach, Sebastian; Klein, Reinhard; Klein, Reinhard and Rushmeier, Holly
    There is a general shortage of standardized comparisons in the field of appearance modeling. We therefore introduce a benchmark for assessing the quality of reflectance models on a dataset of high quality material measurements obtained with a commercial appearance scanner. The dataset currently consists of 56 fabric materials which are measured as radiometrically calibrated HDR images together with a precise surface geometry. We pose a public challenge to attract further participation and spark new research. Participants evaluate their models on provided directional light and view sampling to recreate the appearance of a set of unseen images per material. The results are automatically evaluated under various image metrics and ranked in a public leaderboard. Our benchmark provides standardized testing and thus enables fair comparisons between related works. We also release baseline SVBRDF material fits.
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    MAM - Eurographics 2020 Workshop on Material Appearance Modeling: Frontmatter
    (The Eurographics Association, 2020) Klein, Reinhard; Rushmeier, Holly; Klein, Reinhard and Rushmeier, Holly
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    A Genetic Algorithm Based Heterogeneous Subsurface Scattering Representation
    (The Eurographics Association, 2020) Kurt, Murat; Klein, Reinhard and Rushmeier, Holly
    In this paper, we present a novel heterogeneous subsurface scattering (sss) representation, which is based on a combination of Singular Value Decomposition (SVD) and genetic optimization techniques. To find the best transformation that is applied to measured subsurface scattering data, we use a genetic optimization framework, which tries various transformations to the measured heterogeneous subsurface scattering data to find the fittest one. After we apply the best transformation, we compactly represent measured subsurface scattering data by separately applying the SVD per-color channel of the transformed profiles. In order to get a compact and accurate representation, we apply the SVD on the model errors, iteratively. We validate our approach on a range of optically thick, real-world translucent materials. It's shown that our genetic algorithm based heterogeneous subsurface scattering representation achieves greater visual accuracy than alternative techniques for the same level of compression.
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    An Adaptive Metric for BRDF Appearance Matching
    (The Eurographics Association, 2020) Bieron, James; Peers, Pieter; Klein, Reinhard and Rushmeier, Holly
    Image-based BRDF matching is a special case of inverse rendering, where the parameters of a BRDF model are optimized based on a photograph of a homogeneous material under natural lighting. Using a perceptual image metric, directly optimizing the difference between a rendering and a reference image can provide a close visual match between the model and reference material. However, perceptual image metrics rely on image-features and thus require full resolution renderings that can be costly to produce especially when embedded in a non-linear search procedure for the optimal BRDF parameters. Using a pixel-based metric, such as the squared difference, can approximate the image error from a small subset of pixels. Unfortunately, pixel-based metrics are often a poor approximation of human perception of the material's appearance. We show that comparable quality results to a perceptual metric can be obtained using an adaptive pixel-based metric that is optimized based on the appearance similarity of the material. As the core of our adaptive metric is pixel-based, our method is amendable to imagesubsampling, thereby greatly reducing the computational cost.
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    The Problem of Entangled Material Properties in SVBRDF Recovery
    (The Eurographics Association, 2020) Saryazdi, Soroush; Murphy, Christian; Mudur, Sudhir; Klein, Reinhard and Rushmeier, Holly
    SVBRDF (spatially varying bidirectional reflectance distribution function) recovery is concerned with deriving the material properties of an object from one or more images. This problem is particularly challenging when the images are casual rather than calibrated captures. It makes the problem highly under specified, since an object can look quite different from different angles and from different light directions. Yet many solutions have been attempted under varying assumptions, and the most promising solutions to date are those which use supervised deep learning techniques. The network is first trained with a large number of synthetically created images of surfaces, usually planar, with known values for material properties and then asked to predict the properties for image(s) of a new object. While the results obtained are impressive as shown through renders of the input object using recovered material properties, there is a problem in the accuracy of the recovered properties. Material properties get entangled, specifically the diffuse and specular reflectance behaviors. Such inaccuracies would hinder various down stream applications which use these properties. In this position paper we present this property entanglement problem. First, we demonstrate the problem through various property map outputs obtained by running a state of the deep learning solution. Next we analyse the present solutions, and argue that the main reason for this entanglement is the way the loss function is defined when training the network. Lastly, we propose potential directions that could be pursued to alleviate this problem.
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    A Taxonomy of Bidirectional Scattering Distribution Function Lobes for Rendering Engineers
    (The Eurographics Association, 2020) McGuire, Morgan; Dorsey, Julie; Haines, Eric; Hughes, John F.; Marschner, Steve; Pharr, Matt; Shirley, Peter; Klein, Reinhard and Rushmeier, Holly
    We propose a taxonomy and terminology for rendering engineers to use in describing the main categories of mathematical lobes that are combined to implement bidirectional scattering distribution functions (BSDFs). Bringing consistent language to this area will increase clarity in API names, textbooks, and scholarly publications. We developed this taxonomy and terminology for consistency across our own upcoming works. The taxonomy corresponds to the major BSDF implementation branches in a renderer, rather than surface appearance, and is consistent with physical considerations. The terminology aligns as closely as possible with previous work in rendering and adjacent fields, while resolving inconsistencies among them. The taxonomy is not intended for art direction, machine vision research, optics, material/lighting engineering, or other areas where the critical distinctions between materials differ from those needed by a renderer.
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    Improving Spectral Upsampling with Fluorescence
    (The Eurographics Association, 2020) König, Lars; Jung, Alisa; Dachsbacher, Carsten; Klein, Reinhard and Rushmeier, Holly
    Modern photorealistic rendering simulates spectral behaviour of light. Since many assets are still created in different RGB color spaces, spectral upsampling of the RGB colors to a spectral representation is required to use them in a spectral renderer. Limiting the upsampled spectra to physically valid and natural, i.e. smooth, spectra results in a more realistic image, but decreases the size of the gamut of colors that can be recreated. In order to upsample wide gamut color spaces with colors outside the gamut of physically valid reflectance spectra, a previous approach added fluorescence to create accurate and physically valid representations. We extend this approach to increase the realism and accuarcy while considering memory and computation time.