Time-varying volume visualization: a survey
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REVI EW PAPE R
Zhihui Bai
•
Yubo Tao • Hai Lin
Time-varying volume visualization: a survey
Received: 1 August 2019 / Accepted: 19 January 2020 Ó The Visualization Society of Japan 2020
Abstract Time-varying volume data is often generated from scientific simulations in a variety of application domains, such as computational fluid dynamics, combustion science, and computational cosmology. Data visualization plays an important role in analyzing the dynamics and evolution of phenomena hidden in the data. Over the last two decades, a substantial amount of visualization techniques have been proposed in this research area. In this paper, we systematically review the recent literature on data visualization and visual analytics for time-varying scalar volume data. We first collect a corpus of relevant technical and application papers in visualization journals and conferences from 2008 to 2019. Based on this corpus, we classify these techniques into three aspects, including feature tracking, evolution visualization, and rendering, and then detaily describe relevant techniques in these three aspects. Finally, we conclude this survey with emerging trends and future challenges in time-varying volume visualization. Keywords Time-varying volume data Feature tracking Evolution visualization Rendering
1 Introduction Nothing is permanent except change, and the change is closely related to time. In the light of this statement, phenomena in a variety of scientific domains are inherently time-dependent. As a result, overwhelming amounts of data generated from scientific simulations aiming to explore such scientific phenomena is timevarying volume data, ranging from computational fluid dynamics (Laney et al. 2006), meteorology simulation (Kumpf et al. 2019), combustion simulation (Bremer et al. 2011), cosmology simulations (Takle et al. 2012) to biomolecular simulations (Krone et al. 2013). Advances in computational power for scientific simulations have motivated the generation of timevarying volume data with both high temporal and spatial resolution. Visualization plays an important role in analyzing the dynamics and evolution of phenomena hidden in the data. Time-varying volume visualization is one of the top scientific visualization research problems (Johnson 2004; Wong et al. 2012), owning to the complexity, large scale, and spatial–temporal characteristic of these data sets. Over the last two decades, a substantial amount of visualization techniques have been proposed for analyzing and visualizing such data.
Z. Bai Y. Tao (&) H. Lin State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China E-mail: [email protected] Z. Bai E-mail: [email protected] H. Lin E-mail: [email protected]
Z. Bai et al.
Four surveys have been proposed by researchers to summarize the visualization techniques tailored for time-varying volume data. Three surveys of them focused on a specific area of time-varying volume visualization, including rendering methods (Ayala et al. 2005), modeling approaches (Weiss and De Floriani
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