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Time-varying volume visualization: a survey

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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.

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Notes

  1. This figure is from the slides by Wathsala Widanagamaachchi at the conference on IEEE Symposium on Large Data Analysis and Visualization, 2012, Page 17.

  2. This figure is from the slides by Eamonn Keogh from tutorial in SIGKDD 2007. Mining Shape and Time Series Databases with Symbolic Representations. August 12, 2007, Page 51.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by the National Key Research & Development Program of China (2017YFB0202203), National Natural Science Foundation of China (61672452, 61890954, and 61972343), and NSFC-Guangdong Joint Fund (U1611263).

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  1. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China

    Zhihui Bai, Yubo Tao & Hai Lin

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  1. Zhihui Bai
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Correspondence to Yubo Tao.

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Bai, Z., Tao, Y. & Lin, H. Time-varying volume visualization: a survey. J Vis 23, 745–761 (2020). https://doi.org/10.1007/s12650-020-00654-x

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