Hierarchical opacity optimization for sets of 3D line fields

Günther, Tobias; Rössl, Christian; Theisel, Holger

doi:10.1111/cgf.12336

Cited by 20 publications

(25 citation statements)

References 26 publications

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“…Edges may be generated by defining the neighbourhood relationship of two nodes using their spatial distance [PTRV12] or their distance in the feature space, such as transition probability [JS09, GW11]. They may also originate from the hierarchical structure by clustering the nodes [IVJ12, GRT14].…”

Section: Discussionmentioning

confidence: 99%

“…We can perform node grouping bottom up by agglomerative clustering [PIB*11] or top down by graph partitioning [IVJ12]. The complete hierarchy can be captured by tree‐based representations [LLRR08, IVJ12, GRT14], where all clusters at different levels are displayed. An alternative strategy is to display the nodes corresponding to coarse clusters at the beginning.…”

Section: Discussionmentioning

confidence: 99%

“…The tree serves as an interface for users to explore the embedded structures. Günther et al [GRT14] also utilized a tree structure to store streamline clustering information for a 3D vector field. With this tree, users can set opacities for different streamline segments to reduce visual occlusion and clutter and highlight regions of interest.…”

Section: Hierarchical Clustering or Partitioning Treesmentioning

confidence: 99%

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Graphs in Scientific Visualization: A Survey

Wang

Tao

2016

Computer Graphics Forum

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Graphs represent general node‐link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph‐based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition‐wise, relationship‐wise, structure‐wise and provenance‐wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph‐based visualization pipeline. We also point out research trends and remaining challenges in graph‐based representations and techniques for scientific visualization.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Hierarchical Clustering or Partitioning Treesmentioning

confidence: 99%

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Graphs in Scientific Visualization: A Survey

Wang

Tao

2016

Computer Graphics Forum

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“…Future work includes the extension to animated surfaces and accelerated solving by partitioning the surfaces in a view‐dependent way in the spirit of [GRT14]. Surface opacity optimization can also be extended to point sets, since geodesics can be computed for them, too [CWW13].…”

Section: Discussionmentioning

confidence: 99%

Opacity Optimization for Surfaces

Günther¹,

Schulze²,

Esturo

et al. 2014

Computer Graphics Forum

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In flow visualization, integral surfaces rapidly tend to expand, fold and produce vast amounts of occlusion. While silhouette enhancements and local transparency mappings proved useful for semi‐transparent depictions, they still introduce visual clutter when surfaces grow more complex. An effective visualization of the flow requires a balance between the presentation of interesting surface parts and the avoidance of occlusions that hinder the view. In this paper, we extend the concept of opacity optimization to surfaces to obtain a global approach to the occlusion problem. Starting with a partition of the surfaces into patches, we compute per‐patch opacity as minimizer of a bounded‐variable least‐squares problem. For the final rendering, opacity is interpolated on the surfaces. The resulting visualization technique is interactive, frame‐coherent, view‐dependent and driven by domain knowledge.

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“…For line geometry, Günther et al [20] formulated the adjustment of the line opacity as a global optimization problem with bounded variables. Their approach was later extended to animated lines [21], opacity optimization for surfaces [22] and extinction optimization for volumes [1]. Ament et al [1] approximated the energy and reformulated the minimization in ray space, which enabled a significant acceleration for volume data, which was later carried into point, line and surface geometry by Günther et al [23] in their decoupled opacity optimization.…”

Section: Introductionmentioning

confidence: 99%

Fourier Opacity Optimization for Scalable Exploration

Rojo

Groß

Günther

2020

IEEE Trans. Visual. Comput. Graphics

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Over the past decades, scientific visualization became a fundamental aspect of modern scientific data analysis. Across all data-intensive research fields, ranging from structural biology to cosmology, data sizes increase rapidly. Dealing with the growing large-scale data is one of the top research challenges of this century. For the visual exploratory data analysis, interactivity, a view-dependent visibility optimization and frame coherence are indispensable. In this work, we extend the recent decoupled opacity optimization framework to enable a navigation without occlusion of important features through large geometric data. By expressing the accumulation of importance and optical depth in Fourier basis, the computation, evaluation and rendering of optimized transparent geometry become not only order-independent, but also operate within a fixed memory bound. We study the quality of our Fourier approximation in terms of accuracy, memory requirements and efficiency for both the opacity computation, as well as the order-independent compositing. We apply the method to different point, line and surface data sets originating from various research fields, including meteorology, health science, astrophysics and organic chemistry.

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Hierarchical opacity optimization for sets of 3D line fields

Cited by 20 publications

References 26 publications

Graphs in Scientific Visualization: A Survey

Graphs in Scientific Visualization: A Survey

Opacity Optimization for Surfaces

Fourier Opacity Optimization for Scalable Exploration

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