Browsing by Author "Dischler, Jean-Michel"

Now showing 1 - 8 of 8
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    Anisotropic Filtering for On-the-fly Patch-based Texturing
    (The Eurographics Association, 2019) Lutz, Nicolas; Sauvage, Basile; Larue, Frédéric; Dischler, Jean-Michel; Cignoni, Paolo and Miguel, Eder
    On-the-fly patch-based texturing consists of choosing at run-time, for several patches within a tileable texture, one random candidate among a pre-computed set of possible contents. This category of methods generates unbounded textures, for which filtering is not straightforward, because the screen pixel footprint may overlap multiple patches in texture space, i.e. different randomly chosen contents. In this paper, we propose a real-time anisotropic filtering which is fully compliant with the standard graphics pipeline. The main idea is to pre-filter the contents independently, store them in an atlas, and combine them at run-time to produce the final pixel color. The patch-map, referencing to which patch belong the fetched texels, requires a specific filtering approach, in order to recover the patches that overlap at low resolutions. In addition, we show how this method can achieve blending at patch boundaries in order to further reduce visible seams, without modification of our filtering algorithm.
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    Color-mapped Noise Vector Fields for Generating Procedural Micro-patterns
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Grenier, Charline; Sauvage, Basile; Dischler, Jean-Michel; Thery, Sylvain; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Stochastic micro-patterns successfully enhance the realism of virtual scenes. Procedural models using noise combined with transfer functions are extremely efficient. However, most patterns produced today employ 1D transfer functions, which assign color, transparency, or other material attributes, based solely on the single scalar quantity of noise. Multi-dimensional transfer functions have received widespread attention in other fields, such as scientific volume rendering. But their potential has not yet been well explored for modeling micro-patterns in the field of procedural texturing. We propose a new procedural model for stochastic patterns, defined as the composition of a bi-dimensional transfer function (a.k.a. color-map) with a stochastic vector field. Our model is versatile, as it encompasses several existing procedural noises, including Gaussian noise and phasor noise. It also generates a much larger gamut of patterns, including locally structured patterns which are notoriously difficult to reproduce. We leverage the Gaussian assumption and a tiling and blending algorithm to provide real-time generation and filtering. A key contribution is a real-time approximation of the second order statistics over an arbitrary pixel footprint, which enables, in addition, the filtering of procedural normal maps. We exhibit a wide variety of results, including Gaussian patterns, profiled waves, concentric and non-concentric patterns.
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    Cyclostationary Gaussian Noise: Theory and Synthesis
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Lutz, Nicolas; Sauvage, Basile; Dischler, Jean-Michel; Mitra, Niloy and Viola, Ivan
    Stationary Gaussian processes have been used for decades in the context of procedural noises to model and synthesize textures with no spatial organization. In this paper we investigate cyclostationary Gaussian processes, whose statistics are repeated periodically. It enables the modeling of noises having periodic spatial variations, which we call "cyclostationary Gaussian noises". We adapt to the cyclostationary context several stationary noises along with their synthesis algorithms: spot noise, Gabor noise, local random-phase noise, high-performance noise, and phasor noise. We exhibit real-time synthesis of a variety of visual patterns having periodic spatial variations.
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    Importance Sampling of Glittering BSDFs based on Finite Mixture Distributions
    (The Eurographics Association, 2021) Chermain, Xavier; Sauvage, Basile; Dischler, Jean-Michel; Dachsbacher, Carsten; Bousseau, Adrien and McGuire, Morgan
    We propose an importance sampling scheme for the procedural glittering BSDF of Chermain et al. [CSDD20]. Glittering BSDFs have multi-lobe visible normal distribution functions (VNDFs) which are difficult to sample. They are typically sampled using a mono-lobe Gaussian approximation, leading to high variance and fireflies in the rendering. Our method optimally samples the multi-lobe VNDF, leading to lower variance and removing firefly artefacts at equal render time. It allows, for example, the rendering of glittering glass which requires an efficient sampling of the BSDF. The procedural VNDF of Chermain et al. is a finite mixture of tensor products of two 1D tabulated distributions. We sample the visible normals from their VNDF by first drawing discrete variables according to the mixture weights and then sampling the corresponding 1D distributions using the technique of inverse cumulative distribution functions (CDFs). We achieve these goals by tabulating and storing the CDFs, which uses twice the memory as the original work. We prove the optimality of our VNDF sampling and validate our implementation with statistical tests.
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    Preserving the Autocovariance of Texture Tilings Using Importance Sampling
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Lutz, Nicolas; Sauvage, Basile; Dischler, Jean-Michel; Myszkowski, Karol; Niessner, Matthias
    By-example aperiodic tilings are popular texture synthesis techniques that allow a fast, on-the-fly generation of unbounded and non-periodic textures with an appearance matching an arbitrary input sample called the ''exemplar''. But by relying on uniform random sampling, these algorithms fail to preserve the autocovariance function, resulting in correlations that do not match the ones in the exemplar. The output can then be perceived as excessively random. In this work, we present a new method which can well preserve the autocovariance function of the exemplar. It consists in fetching contents with an importance sampler taking the explicit autocovariance function as the probability density function (pdf) of the sampler. Our method can be controlled for increasing or decreasing the randomness aspect of the texture. Besides significantly improving synthesis quality for classes of textures characterized by pronounced autocovariance functions, we moreover propose a real-time tiling and blending scheme that permits the generation of high-quality textures faster than former algorithms with minimal downsides by reducing the number of texture fetches.
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    Procedural Physically based BRDF for Real-Time Rendering of Glints
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Chermain, Xavier; Sauvage, Basile; Dischler, Jean-Michel; Dachsbacher, Carsten; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    Physically based rendering of glittering surfaces is a challenging problem in computer graphics. Several methods have proposed off-line solutions, but none is dedicated to high-performance graphics. In this work, we propose a novel physically based BRDF for real-time rendering of glints. Our model can reproduce the appearance of sparkling materials (rocks, rough plastics, glitter fabrics, etc.). Compared to the previous real-time method [ZK16], which is not physically based, our BRDF uses normalized NDFs and converges to the standard microfacet BRDF [CT82] for a large number of microfacets. Our method procedurally computes NDFs with hundreds of sharp lobes. It relies on a dictionary of 1D marginal distributions: at each location two of them are randomly picked and multiplied (to obtain a NDF), rotated (to increase the variety), and scaled (to control standard deviation/roughness). The dictionary is multiscale, does not depend on roughness, and has a low memory footprint (less than 1 MiB)
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    Semi-Procedural Textures Using Point Process Texture Basis Functions
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Guehl, Pascal; Allègre, Remi; Dischler, Jean-Michel; Benes, Bedrich; Galin, Eric; Dachsbacher, Carsten and Pharr, Matt
    We introduce a novel semi-procedural approach that avoids drawbacks of procedural textures and leverages advantages of datadriven texture synthesis. We split synthesis in two parts: 1) structure synthesis, based on a procedural parametric model and 2) color details synthesis, being data-driven. The procedural model consists of a generic Point Process Texture Basis Function (PPTBF), which extends sparse convolution noises by defining rich convolution kernels. They consist of a window function multiplied with a correlated statistical mixture of Gabor functions, both designed to encapsulate a large span of common spatial stochastic structures, including cells, cracks, grains, scratches, spots, stains, and waves. Parameters can be prescribed automatically by supplying binary structure exemplars. As for noise-based Gaussian textures, the PPTBF is used as stand-alone function, avoiding classification tasks that occur when handling multiple procedural assets. Because the PPTBF is based on a single set of parameters it allows for continuous transitions between different visual structures and an easy control over its visual characteristics. Color is consistently synthesized from the exemplar using a multiscale parallel texture synthesis by numbers, constrained by the PPTBF. The generated textures are parametric, infinite and avoid repetition. The data-driven part is automatic and guarantees strong visual resemblance with inputs.
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    State-of-the-art in Large-Scale Volume Visualization Beyond Structured Data
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Sarton, Jonathan; Zellmann, Stefan; Demirci, Serkan; Güdükbay, Ugur; Alexandre-Barff, Welcome; Lucas, Laurent; Dischler, Jean-Michel; Wesner, Stefan; Wald, Ingo; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
    Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state-of-the-art report, we review works focusing on largescale volume rendering beyond those typical structured and regular grid representations.We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out-of-core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever-increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large-scale volume rendering systems and also include a review of tools that support the various volume data types discussed.