Opacity Optimization for Surfaces

Günther, Tobias; Schulze, Maik; Esturo, Janick Martinez; Rössl, Christian; Theisel, Holger

doi:10.1111/cgf.12357

Cited by 15 publications

(25 citation statements)

References 39 publications

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“…Still, we argue that stream surface selection and rendering are related but different problems: we have shown that for a variety of data sets a single stream surface does not suffice in representing all flow features, and multiple, but potentially occluding, surfaces are required for a complete representation of the characteristic features of the flow domain. Whereas we use the IRIS rendering approach by Hummel et al [HGH*10] to minimize occlusion, the more recent method by Günther et al [GSM*14] is an even more advanced approach for minimization of surface occlusion.…”

Section: Discussionmentioning

confidence: 99%

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Sets of Globally Optimal Stream Surfaces for Flow Visualization

Schulze

Esturo²,

Günther

et al. 2014

Computer Graphics Forum

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Stream surfaces are a well‐studied and widely used tool for the visualization of 3D flow fields. Usually, stream surface seeding is carried out manually in time‐consuming trial and error procedures. Only recently automatic selection methods were proposed. Local methods support the selection of a set of stream surfaces, but, contrary to global selection methods, they evaluate only the quality of the seeding lines but not the quality of the whole stream surfaces. Global methods, on the other hand, only support the selection of a single optimal stream surface until now. However, for certain flow fields a single stream surface is not sufficient to represent all flow features. In our work, we overcome this limitation by introducing a global selection technique for a set of stream surfaces. All selected surfaces optimize global stream surface quality measures and are guaranteed to be mutually distant, such that they can convey different flow features. Our approach is an efficient extension of the most recent global selection method for single stream surfaces. We illustrate its effectiveness on a number of analytical and simulated flow fields and analyze the quality of the results in a user study.

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Section: Discussionmentioning

confidence: 99%

“…Carnecky et al [CFM*13] take into account findings from cognitive research to improve transparency. Günther et al [GSM*14] use optimization to clear view on important regions of surfaces. All these methods aim exclusively at generating and visualizing stream surfaces at interactive rates.…”

Section: Related Workmentioning

confidence: 99%

Sets of Globally Optimal Stream Surfaces for Flow Visualization

Schulze

Esturo²,

Günther

et al. 2014

Computer Graphics Forum

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“…The above methods did not yet consider the potential occlusion that might be introduced by the geometry. Günther et al [22] adjusted the transparency of surfaces to balance occlusion and visibility. Illustrative flow visualization [7] contributed methods to improve the perception of transparent surfaces, including the angle-based and normal variation mapping of Hummel et al [26], the display of surface slabs and contours by Born et al [6] and the diffusion of silhouettes and halos by Carnecky et al [9].…”

Section: Surface Placement and Selectionmentioning

confidence: 99%

“…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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“…More advanced rendering techniques are possible, which may be optimized towards a simultaneous perception of the shapes of both G and Q . This includes illustrative approaches, opacity optimization for surfaces [GSM∗14] or a piecewise rendering [ZSL∗16].…”

Section: Future Researchmentioning

confidence: 99%

Towards Glyphs for Uncertain Symmetric Second‐Order Tensors

Gerrits

Rössl

Theisel

2019

Computer Graphics Forum

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Measured data often incorporates some amount of uncertainty, which is generally modeled as a distribution of possible samples. In this paper, we consider second‐order symmetric tensors with uncertainty. In the 3D case, this means the tensor data consists of 6 coefficients – uncertainty, however, is encoded by 21 coefficients assuming a multivariate Gaussian distribution as model. The high dimension makes the direct visualization of tensor data with uncertainty a difficult problem, which was until now unsolved. The contribution of this paper consists in the design of glyphs for uncertain second‐order symmetric tensors in 2D and 3D. The construction consists of a standard glyph for the mean tensor that is augmented by a scalar field that represents uncertainty. We show that this scalar field and therefore the displayed glyph encode the uncertainty comprehensively, i.e., there exists a bijective map between the glyph and the parameters of the distribution. Our approach can extend several classes of existing glyphs for symmetric tensors to additionally encode uncertainty and therefore provides a possible foundation for further uncertain tensor glyph design. For demonstration, we choose the well‐known superquadric glyphs, and we show that the uncertainty visualization satisfies all their design constraints.

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Opacity Optimization for Surfaces

Cited by 15 publications

References 39 publications

Sets of Globally Optimal Stream Surfaces for Flow Visualization

Sets of Globally Optimal Stream Surfaces for Flow Visualization

Fourier Opacity Optimization for Scalable Exploration

Towards Glyphs for Uncertain Symmetric Second‐Order Tensors

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