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About this book
Prevalent types of data in scientific visualization are volumetric data, vector field data, and particle-based data. Particle data typically originates from measurements and simulations in various fields, such as life sciences or physics. The particles are often visualized directly, that is, by simple representants like spheres. Interactive rendering facilitates the exploration and visual analysis of the data. With increasing data set sizes in terms of particle numbers, interactive high-quality visualization is a challenging task. This is especially true for dynamic data or abstract representations that are based on the raw particle data.
This book covers direct particle visualization using simple glyphs as well as abstractions that are application-driven such as clustering and aggregation. It targets visualization researchers and developers who are interested in visualization techniques for large, dynamic particle-based data. Its explanations focus on GPU-accelerated algorithms for high-performance rendering and data processing that run in real-time on modern desktop hardware. Consequently, the implementation of said algorithms and the required data structures to make use of the capabilities of modern graphics APIs are discussed in detail. Furthermore, it covers GPU-accelerated methods for the generation of application-dependent abstract representations. This includes various representations commonly used in application areas such as structural biology, systems biology, thermodynamics, and astrophysics.
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Table of contents (9 chapters)
Authors and Affiliations
About the authors
Martin Falk is an assistant lecturer in the Scientific Visualization Group at Linkoping University. He received his Ph.D. degree (Dr. rer. nat.) from the University of Stuttgart in 2013. His research interests are volume rendering, visualizations in the context of systems biology, large spatio-temporal data, glyph-based rendering, and GPU-based simulations. He is the main developer and software architect of the visualization tool CellVis [Falk et al., 2013b] for particle-based data in systems biology.Sebastian Grottel received his Ph.D. in computer science (Dr. rer. nat.) at the University of Stuttgart, Germany, at the Visualization Research Center (VISUS). Since 2012, he is a postdoctoral researcher at the Chair for Computer Graphics and Visualization at the TU Dresden, Germany. His research interests include high performance graphics for scientific visualization of dynamic data. He is the main developer and software architect of the visualization framework MegaMol [Grottel et al., 2015] for particle-based data.Michael Krone is a postdoctoral researcher at the Visualization Research Center of the University of Stuttgart (VISUS). He received his Ph.D. computer science (Dr. rer. nat.) in 2015 from the University of Stuttgart, Germany. His main research interest lies in biomolecular visualization and visual analysis for structural biology including molecular graphics, particle-based rendering, and GPU-accelerated computing.Guido Reina is a postdoctoral researcher at the Visualization Research Center of the University of Stuttgart (VISUS). He received his Ph.D. in computer science (Dr. rer. nat.) in 2008 from the University of Stuttgart, Germany. His research interests include large displays, particle-based rendering, and GPU-based methods in general. He is a principal investigator of the subproject Visualization of Systems with Large Numbers of Particles for the Collaborative Research Center (SFB) 716.
Bibliographic Information
Book Title: Interactive GPU-based Visualization of Large Dynamic Particle Data
Authors: Martin Falk, Sebastian Grottel, Michael Krone, Guido Reina
Series Title: Synthesis Lectures on Visualization
DOI: https://doi.org/10.1007/978-3-031-02604-1
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 7
Copyright Information: Springer Nature Switzerland AG 2017
Softcover ISBN: 978-3-031-01476-5Published: 02 October 2016
eBook ISBN: 978-3-031-02604-1Published: 31 May 2022
Series ISSN: 2159-516X
Series E-ISSN: 2159-5178
Edition Number: 1
Number of Pages: XII, 109
Topics: Visualization, Data Structures and Information Theory, Data Mining and Knowledge Discovery