Screen Space Particle Selection

dc.contributor.authorKöster, Marcelen_US
dc.contributor.authorKrüger, Antonioen_US
dc.contributor.editor{Tam, Gary K. L. and Vidal, Francken_US
dc.date.accessioned2018-09-19T15:15:11Z
dc.date.available2018-09-19T15:15:11Z
dc.date.issued2018
dc.description.abstractAnalyses of large 3D particle datasets typically involve many different exploration and visualization steps. Interactive exploration techniques are essential to reveal and select interesting subsets like clusters or other sophisticated structures. State-of-the-art techniques allow for context-aware selections that can be refined dynamically. However, these techniques require large amounts of memory and have high computational complexity which heavily limits their applicability to large datasets. We propose a novel, massively parallel particle selection method that is easy to implement and has a processing complexity of O(n*k) (where n is the number of particles and k the maximum number of neighbors per particle) and requires only O(n) memory. Furthermore, our algorithm is designed for GPUs and performs a selection step in several milliseconds while still being able to achieve high-quality results.en_US
dc.description.sectionheadersGraphics
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20181208
dc.identifier.isbn978-3-03868-071-0
dc.identifier.pages61-69
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20181208
dc.identifier.urihttps://doi.org/10.2312/cgvc.20181208
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization systems and tools
dc.subjectInteraction techniques
dc.subjectScientific visualization
dc.titleScreen Space Particle Selectionen_US
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