EGPGV24: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/3606992

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Browsing EGPGV24: Eurographics Symposium on Parallel Graphics and Visualization by Title

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    An Accelerated Clip Algorithm for Unstructured Meshes: A Batch-Driven Approach
    (The Eurographics Association, 2024) Tsalikis, Spiros; Schroeder, Will; Szafir, Daniel; Moreland, Kenneth; Reina, Guido; Rizzi, Silvio
    The clip technique is a popular method for visualizing complex structures and phenomena within 3D unstructured meshes. Meshes can be clipped by specifying a scalar isovalue to produce an output unstructured mesh with its external surface as the isovalue. Similar to isocontouring, the clipping process relies on scalar data associated with the mesh points, including scalar data generated by implicit functions such as planes, boxes, and spheres, which facilitates the visualization of results interior to the grid. In this paper, we introduce a novel batch-driven parallel algorithm based on a sequential clip algorithm designed for high-quality results in partial volume extraction. Our algorithm comprises five passes, each progressively processing data to generate the resulting clipped unstructured mesh. The novelty lies in the use of fixed-size batches of points and cells, which enable rapid workload trimming and parallel processing, leading to a significantly improved memory footprint and run-time performance compared to the original version. On a 32-core CPU, the proposed batch-driven parallel algorithm demonstrates a run-time speed-up of up to 32.6x and a memory footprint reduction of up to 4.37x compared to the existing sequential algorithm. The software is currently available under an open-source license in the VTK visualization system.
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    Efficient Adaptive Multiresolution Aggregations of Spatio-temporal Ensembles
    (The Eurographics Association, 2024) Borrelli, Gabriel; Evers, Marina; Linsen, Lars; Reina, Guido; Rizzi, Silvio
    Spatio-temporal ensemble data consist of several simulation runs with multiple spatial and a temporal dimension, where the runs are obtained using different parameter settings or initial conditions for the simulation. During analysis, one is interested in investigating the different facets of space, time, and parameter values. When globally analyzing some facet(s), others shall be aggregated to generate summary visualizations. Due to the large amount of data that an ensemble consists of, one may want to generate summary visualizations at multiple levels of detail. Wavelet transforms are a well-known concept for efficiently switching between multiple resolutions. We propose to extend this concept to ensemble data, where individual facets may be aggregated adaptively. We present how to apply the scheme for any data sizes to generate correct averages even when the number of samples is not a power of two in each dimension. We further develop an out-of-core strategy to handle large data sizes. Our scheme is coupled with common 1D, 2D, and 3D visualization methods for an interactive visual analysis of the ensemble data.
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    Efficient Construction of Out-of-Core Octrees for Managing Large Point Sets
    (The Eurographics Association, 2024) Fischer, Jonathan; Rosenthal, Paul; Linsen, Lars; Reina, Guido; Rizzi, Silvio
    Among various space partitioning approaches for managing point sets out-of-core, octrees are commonly used for being simple and effective. An efficient and adaptive out-of-core octree construction method has been proposed by Kontkanen et al. [KTO11], generating the octree data in a single sweep over the points sorted in Morton order, for a given maximum point count m per octree leaf. Their method keeps m+1 points in memory during the process, which may become an issue for large m. We present an extension to their algorithm that requires a minimum of two points to be held in memory in addition to a limited sequence of integers, thus adapting their method for use cases with large m. Moreover, we do not compute Morton codes explicitly but rather perform both the sorting and the octree generation directly on the point data, supporting coordinates of any finite precision.
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    Fast Rendering of Parametric Objects on Modern GPUs
    (The Eurographics Association, 2024) Unterguggenberger, Johannes; Lipp, Lukas; Wimmer, Michael; Kerbl, Bernhard; Schütz, Markus; Reina, Guido; Rizzi, Silvio
    Parametric functions are an extremely efficient representation for 3D geometry, capable of compactly modelling highly complex objects. Once specified, parametric 3D objects allow for visualization at arbitrary levels of detail, at no additional memory cost, limited only by the amount of evaluated samples. However, mapping the sample evaluation to the hardware rendering pipelines of modern graphics processing units (GPUs) is not trivial. This has given rise to several specialized solutions, each targeting interactive rendering of a constrained set of parametric functions. In this paper, we propose a general method for efficient rendering of parametrically defined 3D objects. Our solution is carefully designed around modern hardware architecture. Our method adaptively analyzes, allocates and evaluates parametric function samples to produce high-quality renderings. Geometric precision can be modulated from few pixels down to sub-pixel level, enabling real-time frame rates of several 100 frames per second (FPS) for various parametric functions. We propose a dedicated level-of-detail (LOD) stage, which outputs patches of similar geometric detail to a subsequent rendering stage that uses either a hardware tessellation-based approach or performs point-based softare rasterization. Our method requires neither preprocessing nor caching, and the proposed LOD mechanism is fast enough to run each frame. Hence, our approach also lends itself to animated parametric objects. We demonstrate the benefits of our method over a state-of-the-art spherical harmonics (SH) glyph rendering method, while showing its flexibility on a range of other demanding shapes.
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    PGV 2024: Frontmatter
    (The Eurographics Association, 2024) Reina, Guido; Rizzi, Silvio; Reina, Guido; Rizzi, Silvio