Intuitive Exploration of Volumetric Data Using Dynamic Galleries

A Survey of Designs for Combined 2D+3D Visual Representations

We examine visual representations of data that make use of combinations of both 2D and 3D data mappings. Combining 2D and 3D representations is a common technique that allows viewers to understand multiple facets of the data with which they are interacting. While 3D representations focus on the spatial character of the data or the dedicated 3D data mapping, 2D representations often show abstract data properties and take advantage of the unique benefits of mapping to a plane. Many systems have used unique combinations of both types of data mappings effectively. Yet there are no systematic reviews of the methods in linking 2D and 3D representations. We systematically survey the relationships between 2D and 3D visual representations in major visualization publications-IEEE VIS, IEEE TVCG, and EuroVis-from 2012 to 2022. We closely examined 105 articles where 2D and 3D representations are connected visually, interactively, or through animation. These approaches are designed based on their visual environment, the relationships between their visual representations, and their possible layouts. Through our analysis, we introduce a design space as well as provide design guidelines for effectively linking 2D and 3D visual representations.

Inviwo - A Visualization System with Usage Abstraction Levels

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities.

A Generative Model for Volume Rendering

We present a technique to synthesize and analyze volume-rendered images using generative models. We use the Generative Adversarial Network (GAN) framework to compute a model from a large collection of volume renderings, conditioned on (1) viewpoint and (2) transfer functions for opacity and color. Our approach facilitates tasks for volume analysis that are challenging to achieve using existing rendering techniques such as ray casting or texture-based methods. We show how to guide the user in transfer function editing by quantifying expected change in the output image. Additionally, the generative model transforms transfer functions into a view-invariant latent space specifically designed to synthesize volume-rendered images. We use this space directly for rendering, enabling the user to explore the space of volume-rendered images. As our model is independent of the choice of volume rendering process, we show how to analyze volume-rendered images produced by direct and global illumination lighting, for a variety of volume datasets.

Deep-Learning-Assisted Volume Visualization

Designing volume visualizations showing various structures of interest is critical to the exploratory analysis of volumetric data. The last few years have witnessed dramatic advances in the use of convolutional neural networks for identification of objects in large image collections. Whereas such machine learning methods have shown superior performance in a number of applications, their direct use in volume visualization has not yet been explored. In this paper, we present a deep-learning-assisted volume visualization to depict complex structures, which are otherwise challenging for conventional approaches. A significant challenge in designing volume visualizations based on the high-dimensional deep features lies in efficiently handling the immense amount of information that deep-learning methods provide. In this paper, we present a new technique that uses spectral methods to facilitate user interactions with high-dimensional features. We also present a new deep-learning-assisted technique for hierarchically exploring a volumetric dataset. We have validated our approach on two electron microscopy volumes and one magnetic resonance imaging dataset.

Volumetric Feature-Based Classification and Visibility Analysis for Transfer Function Design

Transfer function (TF) design is a central topic in direct volume rendering. The TF fundamentally translates data values into optical properties to reveal relevant features present in the volumetric data. We propose a semi-automatic TF design scheme which consists of two steps: First, we present a clustering process within 1D/2D TF domain based on the proximities of the respective volumetric features in the spatial domain. The presented approach provides an interactive tool that aids users in exploring clusters and identifying features of interest (FOI). Second, our method automatically generates a TF by iteratively refining the optical properties for the selected features using a novel feature visibility measurement. The proposed visibility measurement leverages the similarities of features to enhance their visibilities in DVR images. Compared to the conventional visibility measurement, the proposed feature visibility is able to efficiently sense opacity changes and precisely evaluate the impact of selected features on resulting visualizations. Our experiments validate the effectiveness of the proposed approach by demonstrating the advantages of integrating feature similarity into the visibility computations. We examine a number of datasets to establish the utility of our approach for semi-automatic TF design.

What Biological Visualizations Do Science Center Visitors Prefer in an Interactive Touch Table?

Hands-on digital interactivity in science centers provides new communicative opportunities. The Microcosmos multi-touch table allows visitors to interact with 64 image “cards” of (sub)microscopic biological structures and processes embedded across seven theme categories. This study presents the integration of biological content, interactive features and logging capabilities into the table, and analyses visitors’ usage and preferences. Data logging recorded 2,070,350 events including activated category, selected card, and various finger-based gestures. Visitors interacted with all cards during 858 sessions (96 s on average). Finger movements covered an average accumulated distance of 4.6 m per session, and about 56% of card interactions involved two fingers. Visitors made 5.53 category switches per session on average, and the virus category was most activated (average 0.96 per session). An overall ranking score related to card attractive power and holding power revealed that six of the most highly used cards depicted viruses and four were colourful instrument output images. The large finger traversal distance and proportion of two-finger card interaction may indicate the intuitiveness of the gestures. Observed trends in visitor engagement with the biological visualizations are considered in terms of construal level theory. Future work will examine how interactions are related to potential learning of biological content.

Vol2velle: Printable Interactive Volume Visualization

Interaction is an indispensable aspect of data visualization. The presentation of volumetric data, in particular, often significantly benefits from interactive manipulation of parameters such as transfer functions, rendering styles, or clipping planes. However, when we want to create hardcopies of such visualizations, this essential aspect is lost. In this paper, we present a novel approach for creating hardcopies of volume visualizations which preserves a certain degree of interactivity. We present a method for automatically generating Volvelles, printable tangible wheel charts that can be manipulated to explore different parameter settings. Our interactive system allows the flexible mapping of arbitrary visualization parameters and supports advanced features such as linked views. The resulting designs can be easily reproduced using a standard printer and assembled within a few minutes.