Browsing by Author "Merzbach, Sebastian"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Fabric Appearance Benchmark
    (The Eurographics Association, 2020) Merzbach, Sebastian; Klein, Reinhard; Ritschel, Tobias and Eilertsen, Gabriel
    Appearance modeling is a difficult problem that still receives considerable attention from the graphics and vision communities. Though recent years have brought a growing number of high-quality material databases that have sparked new research, there is a general lack of evaluation benchmarks for performance assessment and fair comparisons between competing works. We therefore release a new dataset and pose a public challenge that will enable standardized evaluations. For this we measured 56 fabric samples with a commercial appearance scanner. We publish the resulting calibrated HDR images, along with baseline SVBRDF fits. The challenge is to recreate, under known light and view sampling, the appearance of a subset of unseen images. User submissions will be automatically evaluated and ranked by a set of standard image metrics.
  • Thumbnail Image
    Item
    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/.
  • Thumbnail Image
    Item
    Neural Appearance Synthesis and Transfer
    (The Eurographics Association, 2019) Mazlov, Ilya; Merzbach, Sebastian; Trunz, Elena; Klein, Reinhard; Klein, Reinhard and Rushmeier, Holly
    Appearance acquisition is a challenging problem. Existing approaches require expensive hardware and acquisition times are long. Alternative ''in-the-wild'' few-shot approaches provide a limited reconstruction quality. Furthermore, there is a fundamental tradeoff between spatial resolution and the physical sample dimensions that can be captured in one measurement. In this paper, we investigate how neural texture synthesis and neural style transfer approaches can be applied to generate new materials with high spatial resolution from high quality SVBRDF measurements. We perform our experiments on a new database of measured SVBRDFs.
  • Thumbnail Image
    Item
    Spectral Rendering with the Bounded MESE and sRGB Data
    (The Eurographics Association, 2019) Peters, Christoph; Merzbach, Sebastian; Hanika, Johannes; Dachsbacher, Carsten; Klein, Reinhard and Rushmeier, Holly
    In a recent journal paper, we introduced a technique to represent reflectance spectra by an arbitrary number of Fourier coefficients. As a special case, we converted tristimulus data to three Fourier coefficients. After summarizing this work, we introduce the Fourier sRGB color space. It is defined in terms of Fourier coefficients but designed to behave similar to sRGB. Textures stored in Fourier sRGB support efficient spectral rendering but can be compressed with techniques designed for sRGB textures. Compression errors are similar to sRGB.