In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics.

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Curve Boxplot: Generalization of Boxplot for Ensembles of Curves

In recent years, there is an emerging direction that leverages information theory to solve many challenging problems in scientific data analysis and visualization. In this article, we review the key concepts in information theory, discuss how the principles of information theory can be useful for visualization, and provide specific examples to draw connections between data communication and data visualization in terms of how information can be measured quantitatively. As the amount of digital data available to us increases at an astounding speed, the goal of this article is to introduce the interested readers to this new direction of data analysis research, and to inspire them to identify new applications and seek solutions using information theory.

https://www.mdpi.com/1099-4300/13/1/254/pdf

Information Theory in Scientific Visualization

This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera- and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. 'output-sensitive' algorithms and system designs. This leads to recent output-sensitive approaches that are 'ray-guided', 'visualization-driven' or 'display-aware'. In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks-the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context-the notion of address translation. For our purposes here, we view parallel distributed visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey.

State-of-the-Art in GPU-Based Large-Scale Volume Visualization

Flow visualization is a well established branch of scientific visualization and it currently represents an invaluable resource to many fields, like automotive design, meteorology and medical imaging. Thanks to the capabilities of modern hardware, flow datasets are increasing in size and complexity, and traditional flow visualization techniques need to be updated and improved in order to deal with the upcoming challenges. A fairly recent trend to enhance the expressiveness of scientific visualization is to produce depictions of physical phenomena taking inspiration from traditional handcrafted illustrations: this approach is known as illustrative visualization, and it is getting a foothold in flow visualization as well. In this state of the art report we give an overview of the existing illustrative techniques for flow visualization, we highlight which problems have been solved and which issues still need further investigation, and, finally, we provide remarks and insights on the current trends in illustrative flow visualization.

Illustrative Flow Visualization: State of the Art, Trends and Challenges

Effective 3D streamline placement and visualization play an essential role in many science and engineering disciplines The main challenge for effective streamline visualization lies in seed placement, ie, where to drop seeds and how many seeds should be placed Seeding too many or too few streamlines may not reveal flow features and patterns either because it easily leads to visual clutter in rendering or it conveys little information about the flow field Not only does the number of streamlines placed matter, their spatial relationships also play a key role in understanding the flow field Therefore, effective flow visualization requires the streamlines to be placed in the right place and in the right amount This paper introduces hierarchical streamline bundles, a novel approach to simplifying and visualizing 3D flow fields defined on regular grids By placing seeds and generating streamlines according to flow saliency, we produce a set of streamlines that captures important flow features near critical points without enforcing the dense seeding condition We group spatially neighboring and geometrically similar streamlines to construct a hierarchy from which we extract streamline bundles at different levels of detail Streamline bundles highlight multiscale flow features and patterns through clustered yet not cluttered display This selective visualization strategy effectively reduces visual clutter while accentuating visual foci, and therefore is able to convey the desired insight into the flow data

Hierarchical Streamline Bundles

This paper introduces a new streamline placement and selection algorithm for 3D vector fields. Instead of considering the problem as a simple feature search in data space, we base our work on the observation that most streamline fields generate a lot of self-occlusion which prevents proper visualization. In order to avoid this issue, we approach the problem in a view-dependent fashion and dynamically determine a set of streamlines which contributes to data understanding without cluttering the view. Since our technique couples flow characteristic criteria and view-dependent streamline selection we are able achieve the best of both worlds: relevant flow description and intelligible, uncluttered pictures. We detail an efficient GPU implementation of our algorithm, show comprehensive visual results on multiple datasets and compare our method with existing flow depiction techniques. Our results show that our technique greatly improves the readability of streamline visualizations on different datasets without requiring user intervention.

/pdf/view-dependent-streamlines-for-3d-vector-fields-4kfjob6b3o.pdf

View-Dependent Streamlines for 3D Vector Fields

Shading is an important feature for the comprehension of volume datasets, but is difficult to implement accurately. Current techniques based on pre-integrated direct volume rendering approximate the volume rendering integral by ignoring non-linear gradient variations between front and back samples, which might result in cumulated shading errors when gradient variations are important and / or when the illumination function features high frequencies. In this paper, we explore a simple approach for pre-integrated volume rendering with non-linear gradient interpolation between front and back samples. We consider that the gradient smoothly varies along a quadratic curve instead of a segment in-between consecutive samples. This not only allows us to compute more accurate shaded pre-integrated look-up tables, but also allows us to more efficiently process shading amplifying effects, based on gradient filtering. An interesting property is that the pre-integration tables we use remain two-dimensional as for usual pre-integrated classification. We conduct experiments using a full hardware approach with the Blinn-Phong illumination model as well as with a non-photorealistic illumination model.

/pdf/pre-integrated-volume-rendering-with-non-linear-gradient-1cgrvkxsed.pdf

Pre-Integrated Volume Rendering with Non-Linear Gradient Interpolation

The pervasive concept of cloud computing suggests that visualization, which is both data and computing intensive, is a perfect cloud computing application. This paper presents a sketch of an interface design for an online visualization service. To make such a service attractive to a wider audience, its user interface must be simple and easy to use for both casual and expert users. We envision an interface that supports visualization processes mainly directed by browsing and assessing existing visualizations in terms of images and videos will be very appealing to, in particular, casual users. That is, the aim is to maximize the utilization of the rich visualization data on the web. Without losing generality, we consider volume data visualization applications for our interface design. We also discuss issues in organizing online visualization data, and constructing and managing a rendering cloud.

An Interface Design for Future Cloud-Based Visualization Services

In the field of parallel volume rendering, occlusion is a concept which is already widely exploited in order to improve performance. However, when one moves to larger datasets the use of parallelism becomes a necessity, and in that context, exploiting occlusion to speed up volume rendering is not straightforward. In this paper, we propose and detail a new scheme in which the processors exchange occlusion information so as to speed up the rendering by discarding invisible areas. Our pipeline uses full floating point accuracy for all the intermediate stages, allowing the production of high quality pictures.We further show comprehensive performance results using this pipeline with multiple datasets and demonstrate that cross-processor occlusion can improve the performance of parallel volume rendering.

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Stéphane Marchesin

Papers

View-Dependent Streamlines for 3D Vector Fields

Pre-Integrated Volume Rendering with Non-Linear Gradient Interpolation

An Interface Design for Future Cloud-Based Visualization Services

Cross-node occlusion in sort-last volume rendering