39-Issue 4

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https://diglib.eg.org/handle/10.2312/2632924

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Browsing 39-Issue 4 by Subject "Image processing"

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    A Comprehensive Theory and Variational Framework for Anti-aliasing Sampling Patterns
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Ă–ztireli, A. Cengiz; Dachsbacher, Carsten and Pharr, Matt
    In this paper, we provide a comprehensive theory of anti-aliasing sampling patterns that explains and revises known results, and introduce a variational optimization framework to generate point patterns with any desired power spectra and anti-aliasing properties. We start by deriving the exact spectral expression for expected error in reconstructing a function in terms of power spectra of sampling patterns, and analyzing how the shape of power spectra is related to anti-aliasing properties. Based on this analysis, we then formulate the problem of generating anti-aliasing sampling patterns as constrained variational optimization on power spectra. This allows us to not rely on any parametric form, and thus explore the whole space of realizable spectra. We show that the resulting optimized sampling patterns lead to reconstructions with less visible aliasing artifacts, while keeping low frequencies as clean as possible. Although we focus on image plane sampling, our theory and algorithms apply in any dimensions, and the variational optimization framework can be utilized in all problems where point pattern characteristics are given or optimized.
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    Guided Fine-Tuning for Large-Scale Material Transfer
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Deschaintre, Valentin; Drettakis, George; Bousseau, Adrien; Dachsbacher, Carsten and Pharr, Matt
    We present a method to transfer the appearance of one or a few exemplar SVBRDFs to a target image representing similar materials. Our solution is extremely simple: we fine-tune a deep appearance-capture network on the provided exemplars, such that it learns to extract similar SVBRDF values from the target image. We introduce two novel material capture and design workflows that demonstrate the strength of this simple approach. Our first workflow allows to produce plausible SVBRDFs of large-scale objects from only a few pictures. Specifically, users only need take a single picture of a large surface and a few close-up flash pictures of some of its details.We use existing methods to extract SVBRDF parameters from the close-ups, and our method to transfer these parameters to the entire surface, enabling the lightweight capture of surfaces several meters wide such as murals, floors and furniture. In our second workflow, we provide a powerful way for users to create large SVBRDFs from internet pictures by transferring the appearance of existing, pre-designed SVBRDFs. By selecting different exemplars, users can control the materials assigned to the target image, greatly enhancing the creative possibilities offered by deep appearance capture.