skip to main content
research-article

Opacity optimization for 3D line fields

Published:21 July 2013Publication History
Skip Abstract Section

Abstract

For the visualization of dense line fields, the careful selection of lines to be rendered is a vital aspect. In this paper, we present a global line selection approach that is based on an optimization process. Starting with an initial set of lines that covers the domain, all lines are rendered with a varying opacity, which is subject to the minimization of a bounded-variable least-squares problem. The optimization strives to keep a balance between information presentation and occlusion avoidance. This way, we obtain view-dependent opacities of the line segments, allowing a real-time free navigation while minimizing the danger of missing important structures in the visualization. We compare our technique with existing local and greedy approaches and apply it to data sets in flow visualization, medical imaging, physics, and computer graphics.

Skip Supplemental Material Section

Supplemental Material

tp121.mp4

References

  1. Annen, T., Theisel, H., Rössl, C., Ziegler, G., and Seidel, H.-P. 2008. Vector field contours. In Proc. Graphics Interface, 97--105. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Candelaresi, S., and Brandenburg, A. 2011. Decay of helical and nonhelical magnetic knots. Phys. Rev. E 84, 016406.Google ScholarGoogle ScholarCross RefCross Ref
  3. Chen, Y., Cohen, J., and Krolik, J. 2007. Similarity-guided streamline placement with error evaluation. IEEE Transactions on Visualization and Computer Graphics 13, 1448--1455. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Coleman, T. F., and Li, Y. 1996. A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables. SIAM J. on Optimization 6, 4, 1040--1058. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Eichelbaum, S., Hlawitschka, M., and Scheuermann, G. 2013. LineAO -- improved three-dimensional line rendering. IEEE Transactions on Visualization and Computer Graphics 19, 3, 433--445. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Everts, M. H., Bekker, H., Roerdink, J. B. T. M., and Isenberg, T. 2009. Depth-dependent halos: Illustrative rendering of dense line data. IEEE Transactions on Visualization and Computer Graphics 15, 1299--1306. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Frederich, O., Wassen, E., and Thiele, F. 2008. Prediction of the flow around a short wall-mounted cylinder using LES and DES. Journal of Numerical Analysis, Industrial and Applied Mathematics (JNAIAM) 3, 3-4, 231--247.Google ScholarGoogle Scholar
  8. Furuya, S., and Itoh, T. 2008. A streamline selection technique for integrated scalar and vector visualization. In IEEE Visualization Poster Session.Google ScholarGoogle Scholar
  9. Günther, T., Bürger, K., Westermann, R., and Theisel, H. 2011. A view-dependent and inter-frame coherent visualization of integral lines using screen contribution. Proc. Vision, Modeling, and Visualization (VMV), 215--222.Google ScholarGoogle Scholar
  10. Jobard, B., and Lefer, W. 1997. Creating evenly-spaced streamlines of arbitrary density. Proc. Eurographics Workshop on Visualization in Scientific Computing 7, 45--55.Google ScholarGoogle Scholar
  11. Jobard, B., and Lefer, W. 2001. Multiresolution flow visualization. WSCG 2001 Conference Proceedings, 33--37.Google ScholarGoogle Scholar
  12. Kutz, B. M., Kowarsch, U., Kessler, M., and Krämer, E. 2012. Numerical investigation of helicopter rotors in ground effect. In 30th AIAA Applied Aerodynamics Conference.Google ScholarGoogle Scholar
  13. Lee, T.-Y., Mishchenko, O., Shen, H.-W., and Crawfis, R. 2011. View point evaluation and streamline filtering for flow visualization. In Proc. IEEE Pacific Visualization, 83--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Li, L., and Shen, H.-W. 2007. Image-based streamline generation and rendering. IEEE Transactions on Visualization and Computer Graphics 13, 630--640. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Li, L., Hsien, H. H., and Shen, H. W. 2008. Illustrative streamline placement and visualization. IEEE Pacific Visualization Symposium 2008, 79--86.Google ScholarGoogle Scholar
  16. Liu, Z., Moorhead, R., and Groner, J. 2006. An advanced evenly-spaced streamline placement algorithm. IEEE Transactions on Visualization and Computer Graphics 12, 965--972. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Luft, T., Colditz, C., and Deussen, O. 2006. Image enhancement by unsharp masking the depth buffer. ACM Trans. Graph. 25, 3, 1206--1213. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ma, J., Wang, C., and Shene, C.-K. 2013. Coherent view-dependent streamline selection for importance-driven flow visualization. Proc. SPIE 8654, Visualization and Data Analysis.Google ScholarGoogle Scholar
  19. Marchesin, S., Chen, C.-K., Ho, C., and Ma, K.-L. 2010. View-dependent streamlines for 3D vector fields. IEEE Transactions on Visualization and Computer Graphics 16, 1578--1586. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Mattausch, O., Theussl, T., Hauser, H., and Gröller, E. 2003. Strategies for interactive exploration of 3D flow using evenly-spaced illuminated streamlines. In Proc. Spring Conference on Computer Graphics (SSCG), ACM, 213--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Maule, M., Comba, J. L., Torchelsen, R. P., and Bastos, R. 2011. A survey of raster-based transparency techniques. Computers & Graphics 35, 6, 1023--1034. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. McLoughlin, T., Jones, M., Laramee, R., Malki, R., Masters, I., and Hansen, C. 2012. Similarity measures for enhancing interactive streamline seeding. IEEE Transactions on Visualization and Computer Graphics. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Mebarki, A., Alliez, P., and Devillers, O. 2005. Farthest point seeding for efficient placement of streamlines. In IEEE Visualization, 479--486.Google ScholarGoogle Scholar
  24. Tao, J., Ma, J., Wang, C., and Shene, C. 2013. A unified approach to streamline selection and viewpoint selection for 3D flow visualization. IEEE Transactions on Visualization and Computer Graphics 19, 393--406. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Turk, G., and Banks, D. 1996. Image-guided streamline placement. In Proc. SIGGRAPH, 453--460. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Verma, V., Kao, D., and Pang, A. 2000. A flow-guided streamline seeding strategy. In IEEE Visualization, 163--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Wang, C., and Shen, H.-W. 2011. Information theory in scientific visualization. Entropy 13, 1, 254--273.Google ScholarGoogle ScholarCross RefCross Ref
  28. Xu, L., Lee, T.-Y., and Shen, H.-W. 2010. An information-theoretic framework for flow visualization. In IEEE Transactions on Visualization and Computer Graphics, 1216--1224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Yang, J. C., Hensley, J., Grün, H., and Thibieroz, N. 2010. Real-time concurrent linked list construction on the GPU. Computer Graphics Forum 29, 4, 1297--1304. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Ye, X., Kao, D., and Pang, A. 2005. Strategy for seeding 3D streamlines. IEEE Visualization Conference, 471--478.Google ScholarGoogle Scholar
  31. Yu, Y., Tung, C., van der Wall, B., Pausder, H.-J., Burley, C., Brooks, T., Beaumier, P., Mercker, Y. D. E., and Pengel, K. 2002. The HART-II test: Rotor wakes and aeroacoustics with higher-harmonic pitch control (HHC) inputs -- the joint German/French/Dutch/US project. American Helicopter Society 58th Annual Forum.Google ScholarGoogle Scholar
  32. Yu, H., Wang, C., Shene, C.-K., and Chen, J. 2012. Hierarchical streamline bundles. IEEE Transactions on Visualization and Computer Graphics 18, 8, 1353--1367. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Zöckler, M., Stalling, D., and Hege, H.-C. 1996. Interactive visualization of 3D vector fields using illuminated stream lines. In IEEE Visualization, 107--113. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Opacity optimization for 3D line fields

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Transactions on Graphics
        ACM Transactions on Graphics  Volume 32, Issue 4
        July 2013
        1215 pages
        ISSN:0730-0301
        EISSN:1557-7368
        DOI:10.1145/2461912
        Issue’s Table of Contents

        Copyright © 2013 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 21 July 2013
        Published in tog Volume 32, Issue 4

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader