EGWR: Eurographics Workshop on Rendering

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https://diglib.eg.org/handle/10.2312/378

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Browsing EGWR: Eurographics Workshop on Rendering by Subject "Antialiasing"

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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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    Extracting Microfacet-based BRDF Parameters from Arbitrary Materials with Power Iterations
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Dupuy, Jonathan; Heitz, Eric; Iehl, Jean-Claude; Poulin, Pierre; Ostromoukhov, Victor; Jaakko Lehtinen and Derek Nowrouzezahrai
    We introduce a novel fitting procedure that takes as input an arbitrary material, possibly anisotropic, and automatically converts it to a microfacet BRDF. Our algorithm is based on the property that the distribution of microfacets may be retrieved by solving an eigenvector problem that is built solely from backscattering samples. We show that the eigenvector associated to the largest eigenvalue is always the only solution to this problem, and compute it using the power iteration method. This approach is straightforward to implement, much faster to compute, and considerably more robust than solutions based on nonlinear optimizations. In addition, we provide simple conversion procedures of our fits into both Beckmann and GGX roughness parameters, and discuss the advantages of microfacet slope space to make our fits editable. We apply our method to measured materials from two large databases that include anisotropic materials, and demonstrate the benefits of spatially varying roughness on texture mapped geometric models.
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    Fast Generation of Approximate Blue Noise Point Sets
    (The Eurographics Association and Blackwell Publishing Ltd., 2012) Kalantari, Nima Khademi; Sen, Pradeep; Fredo Durand and Diego Gutierrez
    Poisson-disk sampling is a popular sampling method because of its blue noise power spectrum, but generation of these samples is computationally very expensive. In this paper, we propose an efficient method for fast generation of a large number of blue noise samples using a small initial patch of Poisson-disk samples that can be generated with any existing approach. Our main idea is to convolve this set of samples with another to generate our final set of samples. We use the convolution theorem from signal processing to show that the spectrum of the resulting sample set preserves the blue noise properties. Since our method is approximate, we have error with respect to the true Poisson-disk samples, but we show both mathematically and practically that this error is only a function of the number of samples in the small initial patch and is therefore bounded. Our method is parallelizable and we demonstrate an implementation of it on a GPU, running more than 10 times faster than any previous method and generating more than 49 million 2D samples per second. We can also use the proposed approach to generate multidimensional blue noise samples.