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Visual inspection of multivariate volume data based on multi-class noise sampling

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Abstract

Visualizing multivariate volume data is useful when the user wants to inspect the correlational distributions of multiple variables in a spatial field. Existing solutions commonly rely on color blending or weaving techniques to show multiple variables on a sampling point, probably causing heavy visual confusion. This paper presents an alternative solution that employs a multi-class sampling technique to generate spatially separated sampling points for multiple variables and illustrates the sampling points of each variable individually. We combine this new sampling scheme with the conventional direct volume rendering mode, iso-surface mode, and the cutting plane mode to support interactive inspection of volumetric distributions of multiple variables. The effectiveness of our approach is demonstrated with the IEEE VIS Contest 2004 Hurricane dataset and a 3D nuclear fusion simulation dataset.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments. This work was partially supported by the National High Technology Research and Development Program of China (2012AA12090), Major Program of National Natural Science Foundation of China (61232012), National Natural Science Foundation of China (61422211), National Natural Science Foundation of China (61303134), the Fundamental Research Funds for the Central Universities (2013QNA5010).

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Authors and Affiliations

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

    Zhiyu Ding, Haidong Chen, Yubo Tao & Wei Chen

  2. Department of Computer Science, Purdue University, West Lafayette, USA

    Ziang Ding

  3. Zhejiang University of Finance and Economics, Hangzhou, China

    Weifeng Chen

  4. China University of Petroleum, Beijing, China

    Xin Li

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  1. Zhiyu Ding
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  2. Ziang Ding
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  3. Weifeng Chen
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  4. Haidong Chen
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  5. Yubo Tao
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  6. Xin Li
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  7. Wei Chen
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Correspondence to Wei Chen.

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Ding, Z., Ding, Z., Chen, W. et al. Visual inspection of multivariate volume data based on multi-class noise sampling. Vis Comput 32, 465–478 (2016). https://doi.org/10.1007/s00371-015-1070-6

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