Browsing by Author "Klein, Reinhard"

Now showing 1 - 4 of 4
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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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    Capturing Anisotropic SVBRDFs
    (The Eurographics Association, 2021) Kaltheuner, Julian; Bode, Lukas; Klein, Reinhard; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    In this work, we adapt and improve recent isotropic material estimation efforts to estimate spatially varying anisotropic materials with an additional Fresnel term using a variable set of input images and are able to handle any resolution. We combine an initial estimation network with an auto-encoder to fine-tune the decoding of latent embedded appearance parameters on the input images to produce finely detailed SVBRDFs. For this purpose, the training must be adapted so that the determination is possible on the basis of a small number of images that still capture as much reflective behavior of materials as possible. The resulting appearance parameters are capable of capturing and reconstructing complex spatially varying features in detail, but place increased demands on the input images.
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    Learned Fitting of Spatially Varying BRDFs
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Merzbach, Sebastian; Hermann, Max; Rump, Martin; Klein, Reinhard; Boubekeur, Tamy and Sen, Pradeep
    The use of spatially varying reflectance models (SVBRDF) is the state of the art in physically based rendering and the ultimate goal is to acquire them from real world samples. Recently several promising deep learning approaches have emerged that create such models from a few uncalibrated photos, after being trained on synthetic SVBRDF datasets. While the achieved results are already very impressive, the reconstruction accuracy that is achieved by these approaches is still far from that of specialized devices. On the other hand, fitting SVBRDF parameter maps to the gibabytes of calibrated HDR images per material acquired by state of the art high quality material scanners takes on the order of several hours for realistic spatial resolutions. In this paper, we present a first deep learning approach that is capable of producing SVBRDF parameter maps more than two orders of magnitude faster than state of the art approaches, while still providing results of equal quality and generalizing to new materials unseen during the training. This is made possible by training our network on a large-scale database of material scans that we have gathered with a commercially available SVBRDF scanner. In particular, we train a convolutional neural network to map calibrated input images to the 13 parameter maps of an anisotropic Ward BRDF, modified to account for Fresnel reflections, and evaluate the results by comparing the measured images against re-renderings from our SVBRDF predictions. The novel approach is extensively validated on real world data taken from our material database, which we make publicly available under https://cg.cs.uni-bonn.de/svbrdfs/.
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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