Each year the IEEE Visualization Conference continues to be the premiere forum for presenting state-of-the-art research in all topics related to visualization. In this issue, we present revised and extended versions of some of the best papers from the 2004 edition of the conference.
The selection process for inviting papers for this special issue followed the pattern set by the 2003 conference. In June 2004, after the conference paper review and selection was complete, the best papers were identified. The chairs considered the written comments from the reviewers as well as their own reading of the papers in this process. The authors of the papers identified were invited to prepare revised and extended versions of their conference papers during the summer and early fall of 2004. We then sent out the revised papers for a full journal-level review by external referees. The results of the revision and review process are the six papers which appear in this issue.
The goal of visualization is to generate images that enable people to gain understanding. There are many facets to this endeavor, ranging from complex mathematical issues to issues of how we as humans interact with images. The papers included here span this range of activity.
Two of the papers deal with the problem of interactive visualization of massive triangle models. The papers take different approaches to different versions of the problem. They have in common, though, introducing new algorithms based on new data representations that are demonstrated in fully implemented systems.
In "Real-Time Optimal Adaptation for Planetary Geometry and Texture: 4-8 Tile Hierarchies," Hwa, Duchaineau, and Joy consider the problem of terrain visualization. They introduce a unique diamond data structure to represent the mesh. The hierarchical data structure has the interesting property that diamonds in the hierarchy overlap with, but are not coincident with, their children. This structure facilitates more gradual transitions in level of detail than have been possible with previous approaches. The conference paper this is based on received the IEEE Visualization best paper award.
"Quick-VDR: Out-of-Core View-Dependent Rendering of Gigantic Models" by Yoon, Salomon, Gayle, and Manocha uses a new representation for general triangle meshes—CHPM, a clustered hierarchy of progressive meshes. These clusters are the basic organization for level of detail management, culling, and out-of-core rendering. The system is demonstrated on a wide range of data sets, ranging from isosurfaces from turbulent flow simulations to data from 3D scanning of cultural heritage objects.
Two of the papers focus on visualizing complex fields. Naive visualizations of these types of data sets result in overwhelming clutter. Both papers present robust methods for extracting key topological features to reduce massive data sets to elegantly simple representations revealing their basic structure.
Theisel, Weinkauf, Hege, and Seidel consider temporally varying fields in "Topological Methods for 2D Time-Dependent Vector Fields Based on Stream Lines and Path Lines." The visualization of stream line topology has been previously studied. In this paper, the alternative of path line topology is introduced for the same class of data sets. The authors describe how to extract features in both approaches and compare the characteristics of the flow they reveal.
Zheng, Parlett, and Pang consider tensor fields in "Topological Lines in 3D Tensor Fields and Discriminant Hessian Factorization." Tensor fields arise in problems such as fluid flow and stress analysis. Topological lines are an alternative to visualizing tensors as glyphs shaped by the eigenvectors of the tensor. Topological features are identified by finding where the tensor is degenerate, that is, where it has two or more identical eigenvalues. In this paper, the authors show that these degeneracies occur along lines and demonstrate how to extract these lines from the field.
Finally, the third pair of papers presents novel representations and ways of interacting with data. In one case, the generic problem of viewing complicated surfaces within a 3D data scalar data set is considered. In the other case, the specific problem of using the results of diffusion tensor imaging (DTI) to understand brain structure is addressed.
Viola, Kanitsar, and Gröller present a novel approach, inspired by cut away views used in technical illustration to rendering 3D scalar data sets in "Importance-Driven Feature Enhancement in Volume Visualization." By assigning a value of "importance" to segments of the 3D field, the method can render selected structures from any view while maintaining the 3D context given by the full field automatically.
In "Exploring Connectivity of the Brain's White Matter with Dynamic Queries," Sherbondy, Akers, Mackenzie, Dougherty, and Wandell consider the specific application of using data from DTI to better understand the brain. DTI technology provides a new resource for probing the pathways in the brain's white matter. Researchers need a way, though, to make sense of this data. The authors present an interface that allows users to form queries to explore possible interesting pathways, such as between the left and right hemispheres of the brain's visual area.
We think you will find these papers interesting and thought provoking. We thank the authors for their contributions and the anonymous reviewers for their timely comments and suggestions. We look forward to the next generation of new ideas that will be presented at the October 2005 IEEE Visualization Conference.
Jarke J. van Wijk
• H. Rushmeier is with the Department of Computer Science, Yale University, PO Box 208285, New Haven, CT 06520-8285.
• J.J. van Wijk is with the Department of Mathematics and Computer Science, Technische Universiteit Eindhoven, PO Box 513, 5600 MB Eindhoven, The Netherlands. E-mail: firstname.lastname@example.org.
• G. Turk is with the College of Computing, Georgia Institute of Technology, 801 Atlantic Dr., Atlanta, GA 30332-0280. E-mail: email@example.com.
For information on obtaining reprints of this article, please send e-mail to: firstname.lastname@example.org.
received the BS, MS, and PhD degrees in mechanical engineering from Cornell University. She is a professor of computer science at Yale University. Since receiving the PhD, she has held positions at the Georgia Institute of Technology, the National Institute of Standards and Technology, and IBM T.J. Watson Research Center. Her research interests include realistic rendering, data visualization, 3D scanning, and applications of computer graphics in cultural heritage.
Jarke J. van Wijk
received the MSc degree in industrial design engineering in 1982 and the PhD degree in computer science in 1986, both with honors. He worked from 1988 to 1998 at The Netherlands Energy Research Foundation ECN in The Netherlands, where he was engaged in research on flow visualization and computational steering. In 1998, he joined the Technische Universiteit Eindhoven and, in 2001, he was appointed a full professor in visualization. His main research interests are information visualization and flow visualization, both with a focus on the development of new visual representations. He has (co)authored more than 70 papers on visualization and computer graphics, including 13 IEEE Vis and six IEEE InfoVis papers. He is a member of the IEEE.
received the PhD degree in computer science in 1992 from the University of North Carolina (UNC) at Chapel Hill. He was a postdoctoral researcher at Stanford University for two years followed by two years as a research scientist at UNC Chapel Hill. He is currently an associate professor at the Georgia Institute of Technology, where he is a member of the College of Computing and the Graphics, Visualization, and Usability Center. His research interests include computer graphics, scientific visualization, and computer vision. He is a member of the IEEE.