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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Johnson, C.: Top scientific visualization research problems. IEEE Comput. Graph. Appl. 24(4), 13–17 (2004)
Woodring, J., Shen, H.-W.: Multi-variate, time varying, and comparative visualization with contextual cues. IEEE Trans. Vis. Comput. Graph. 12(5), 909–916 (2006)
Urness, T., Interrante, V., Marusic, I., Longmire, E., Ganapathisubramani, B.: Effectively visualizing multi-valued flow data using color and texture. In: Proceedings of the 14th IEEE Visualization 2003 (VIS’03), p. 16. IEEE Computer Society Seattle, WA (2003)
Fang, M., Lu, J., Peng, Q.: Volumetric data modeling and analysis based on seven-directional box spline. Sci. China Inf. Sci. 57(6), 1–14 (2014)
Strengert, M., Klein, T., Botchen, R., Stegmaier, S., Chen, M., Ertl, T.: Spectral volume rendering using gpu-based raycasting. Vis. Comput. 22(8), 550–561 (2006)
Max, N.: Optical models for direct volume rendering. IEEE Trans. Vis. Comput. Graph. 1(2), 99–108 (1995)
Chuang, J., Weiskopf, D., Moller, T.: Hue-preserving color blending. IEEE Trans. Vis. Comput. Graph. 15(6), 1275–1282 (2009)
Kuhne, L., Giesen, J., Zhang, Z., Ha, S., Mueller, K.: A data-driven approach to hue-preserving color-blending. IEEE Trans. Vis. Comput. Graph. 18(12), 2122–2129 (2012)
Khlebnikov, R., Kainz, B., Steinberger, M., Schmalstieg, D.: Noise-based volume rendering for the visualization of multivariate volumetric data. IEEE Trans. Vis. Comput. Graph. 19(12), 2926–2935 (2013)
Khlebnikov, R., Kainz, B., Steinberger, M., Streit, M., Schmalstieg, D.: Procedural texture synthesis for zoom-independent visualization of multivariate data. Comp. Graph. Forum 31(3pt4), 1355–1364 (2012). doi:10.1111/j.1467-8659.2012.03127.x
Tufte, E.R.: Envisioning information. Optom. Vis. Sci. 68(4), 322–324 (1991)
Biswas, A., Dutta, S., Shen, H.-W., Woodring, J.: An information-aware framework for exploring multivariate data sets. IEEE Trans. Vis. Comput. Graph. 19(12), 2683–2692 (2013)
Guo, H., Xiao, H., Yuan, X.: Multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates. In: Pacific Visualization Symposium (PacificVis), 2011 IEEE, pp. 19–26. IEEE, Hong Kong (2011)
Guo, H., Xiao, H., Yuan, X., et al.: Scalable multivariate volume visualization and analysis based on dimension projection and parallel coordinates. IEEE Trans. Vis. Comput. Graph. 18(9), 1397–1410 (2012)
Cai, W., Sakas, G.: Data intermixing and multi-volume rendering. Comp. Graph. Forum 18(3), 359–368 (1999). doi:10.1111/1467-8659.00356
Akiba, H., Ma, K.-L., Chen, J.H., Hawkes, E.R.: Visualizing multivariate volume data from turbulent combustion simulations. Comput. Sci. Eng. 9(2), 76–83 (2007)
Akiba, H., Ma, K.-L.: A tri-space visualization interface for analyzing time-varying multivariate volume data. In: Proceedings of the 9th Joint Eurographics/IEEE VGTC Conference on Visualization. EUROVIS’07, pp. 115–122. Eurographics Association, Aire-la-Ville, Switzerland (2007)
Sauber, N., Theisel, H., Seidel, H.-P.: Multifield-graphs: an approach to visualizing correlations in multifield scalar data. IEEE Trans. Vis. Comput. Graph. 12(5), 917–924 (2006)
Crawfis, R.: Multivariate volume rendering. In: Tech. rep.. Lawrence Livermore National Lab., CA (1996)
Djurcilov, S., Kim, K., Lermusiaux, P., Pang, A.: Visualizing scalar volumetric data with uncertainty. Comput. Graph. 26(2), 239–248 (2002)
Hagh-Shenas, H., Kim, S., Interrante, V., Healey, C.: Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. IEEE Trans. Vis. Comput. Graph. 13(6), 1270–1277 (2007)
Fuchs, R., Hauser, H.: Visualization of multi-variate scientific data. Comp. Graph. Forum 28(6), 1670–1690 (2009). doi:10.1111/j.1467-8659.2009.01429.x
Kehrer, J., Hauser, H.: Visualization and visual analysis of multifaceted scientific data: a survey. IEEE Trans. Vis. Comput. Graph. 19(3), 495–513 (2013)
Yellott, J.I.: Spectral consequences of photoreceptor sampling in the rhesus retina. Science 221(4608), 382–385 (1983)
Cook, R.L.: Stochastic sampling in computer graphics. ACM Trans. Graph. 5(1), 51–72 (1986)
Cohen, M.F., Shade, J., Hiller, S., Deussen, O.: Wang tiles for image and texture generation. ACM Trans. Graph. 22(3), 287–294 (2003). doi:10.1145/882262.882265
Lagae, A., Dutré, P.: A procedural object distribution function. ACM Trans. Graph. 24(4), 1442–1461 (2005)
Balzer, M., Schlömer, T., Deussen, O.: Capacity-constrained point distributions: a variant of Lloyd’s method. ACM Trans. Graph. 28(3), 86:1–86:8 (2009). doi:10.1145/1531326.1531392
Wei, L.-Y.: Multi-class blue noise sampling, ACM Trans. Graph 29(4) (2010)
Wu, Y.-C., Tsai, Y.-T., Lin, W.-C., Li, W.-H.: Generating pointillism paintings based on seurat’s color composition. Comp. Graph. Forum 32(4), 153–162 (2013). doi:10.1111/cgf.12161
Yuan, X., Guo, P., Xiao, H., Zhou, H., Qu, H.: Scattering points in parallel coordinates. IEEE Trans. Vis. Comput. Graph. 15(6), 1001–1008 (2009)
Janicke, H., Bottinger, M., Scheuermann, G.: Brushing of attribute clouds for the visualization of multivariate data. IEEE Trans. Vis. Comput. Graph. 14(6), 1459–1466 (2008)
Tzeng, F.-Y., Lum, E.B., Ma, K.-L.: An intelligent system approach to higher-dimensional classification of volume data. IEEE Trans. Vis. Comput. Graph. 11(3), 273–284 (2005)
Theisel, H., Sahner, J., Weinkauf, T., Hege, H.-C., Seidel, H.-P.: Extraction of parallel vector surfaces in 3d time-dependent fields and application to vortex core line tracking. In: Visualization, 2005. VIS 05. IEEE, pp. 631–638. IEEE, Minneapolis, MN (2005)
Barakat, S., Andrysco, N., Tricoche, X.: Fast extraction of high-quality crease surfaces for visual analysis. Comp. Graph. Forum 30(3), 961–970 (2011). doi:10.1111/j.1467-8659.2011.01945.x
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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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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DOI: https://doi.org/10.1007/s00371-015-1070-6