EGPGV23: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/2633345

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Browsing EGPGV23: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Computing methodologies"

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    Efficient Sphere Rendering Revisited
    (The Eurographics Association, 2023) Gralka, Patrick; Reina, Guido; Ertl, Thomas; Bujack, Roxana; Pugmire, David; Reina, Guido
    Glyphs are an intuitive way of displaying the results of atomistic simulations, usually as spheres. Raycasting of camera-aligned billboards is considered the state-of-the-art technique to render large sets of spheres in a rasterization-based pipeline since the approach was first proposed by Gumhold. Over time various acceleration techniques have been proposed, such as the rendering of point primitives as billboards, which are trivial to rasterize and avoid a high workload in the vertex pipeline. Other techniques attempt to optimize data upload and access patterns in shader programs, both relevant aspects for dynamic data. Recent advances in graphics hardware raise the question of whether these optimizations are still valid. We evaluate several rendering and data access scheme combinations on real-world datasets and derive recommendations for efficient rasterization-based sphere rendering.
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    Parallel Compositing of Volumetric Depth Images for Interactive Visualization of Distributed Volumes at High Frame Rates
    (The Eurographics Association, 2023) Gupta, Aryaman; Incardona, Pietro; Brock, Anton; Reina, Guido; Frey, Steffen; Gumhold, Stefan; Günther, Ulrik; Sbalzarini, Ivo F.; Bujack, Roxana; Pugmire, David; Reina, Guido
    We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.