43-Issue 3

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https://diglib.eg.org/handle/10.2312/3606991

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Transmittance-based Extinction and Viewpoint Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Himmler, Paul; Günther, Tobias; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
A long-standing challenge in volume visualization is the effective communication of relevant spatial structures that might be hidden due to occlusions. Given a scalar field that indicates the importance of every point in the domain, previous work synthesized volume visualizations by weighted averaging of samples along view rays or by optimizing a spatially-varying extinction field through an energy minimization. This energy minimization, however, did not directly measure the contribution of an individual sample to the final pixel color. In this paper, we measure the visibility of relevant structures directly by incorporating the transmittance into a non-linear energy minimization. For the first time, we not only perform a transmittance-based extinction optimization, we concurrently optimize the camera position to find ideal viewpoints. We derive the partial derivatives for the gradient-based optimization symbolically, which makes the application of automatic differentiation methods unnecessary. The transmittance-based formulation gives a direct visibility measure that is communicated to the user in order to make aware of potentially overlooked relevant structures. Our approach is compatible with any measure of importance and its versatility is demonstrated in multiple data sets.
Depth for Multi-Modal Contour Ensembles
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Chaves-de-Plaza, Nicolas F.; Molenaar, Mathijs; Mody, Prerak; Staring, Marius; Egmond, René van; Eisemann, Elmar; Vilanova, Anna; Hildebrandt, Klaus; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The contour depth methodology enables non-parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear-time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear-time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non-parametric multimodal analysis. To promote research and adoption, we offer the contour-depth Python package.
Visual Analytics for Fine-grained Text Classification Models and Datasets
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Battogtokh, Munkhtulga; Xing, Yiwen; Davidescu, Cosmin; Abdul-Rahman, Alfie; Luck, Michael; Borgo, Rita; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
In natural language processing (NLP), text classification tasks are increasingly fine-grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse-grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design-and-evaluation process to characterize and tackle the growing requirements in their workflow of developing fine-grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine-grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zellmann, Stefan; Wu, Qi; Sahistan, Alper; Ma, Kwan-Liu; Wald, Ingo; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for HPC, and thus also for scientific visualization. AMR data is usually cell centric (which imposes numerous challenges), complex, and generally hard to render. Recent work on GPU-accelerated AMR rendering has made much progress towards real-time volume and isosurface rendering of such data, but so far this work has focused exclusively on ray marching, with simple lighting models and without scattering events or global illumination. True high-quality rendering requires a modified approach that is able to trace arbitrary incoherent paths; but this may not be a perfect fit for the types of data structures recently developed for ray marching. In this paper, we describe a novel approach to high-quality path tracing of complex AMR data, with a specific focus on analyzing and comparing different data structures and algorithms to achieve this goal.
AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Duarte, Diogo; Costa, Rita; Bizarro, Pedro; Duarte, Carlos; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Charts remain widely inaccessible on the web for users of assistive technologies like screen readers. This is, in part, due to data visualization experts still lacking the experience, knowledge, and time to consistently implement accessible charts. As a result, screen reader users are prevented from accessing information and are forced to resort to tabular alternatives (if available), limiting the insights that they can gather. We worked with both groups to develop AutoVizuA11y, a tool that automates the addition of accessible features to web-based charts. It generates human-like descriptions of the data using a large language model, calculates statistical insights from the data, and provides keyboard navigation between multiple charts and underlying elements. Fifteen screen reader users interacted with charts made accessible with AutoVizuA11y in a usability test, thirteen of which praised the tool for its intuitive design, short learning curve, and rich information. On average, they took 66 seconds to complete each of the eight analytical tasks presented and achieved a success rate of 89%. Through a SUS questionnaire, the participants gave AutoVizuA11y an ''Excellent'' score-83.5/100 points. We also gathered feedback from two data visualization experts who used the tool. They praised the tool availability, ease of use and functionalities, and provided feedback to add AutoVizuA11y support for other technologies in the future.
Sparse q-ball imaging towards efficient visual exploration of HARDI data
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lei, Danhua; Miandji, Ehsan; Unger, Jonas; Hotz, Ingrid; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Diffusion-weighted magnetic resonance imaging (D-MRI) is a technique to measure the diffusion of water, in biological tissues. It is used to detect microscopic patterns, such as neural fibers in the living human brain, with many medical and neuroscience applications e.g. for fiber tracking. In this paper, we consider High-Angular Resolution Diffusion Imaging (HARDI) which provides one of the richest representations of water diffusion. It records the movement of water molecules by measuring diffusion under 64 or more directions. A key challenge is that it generates high-dimensional, large, and complex datasets. In our work, we develop a novel representation that exploits the inherent sparsity of the HARDI signal by approximating it as a linear sum of basic atoms in an overcomplete data-driven dictionary using only a sparse set of coefficients. We show that this approach can be efficiently integrated into the standard q-ball imaging pipeline to compute the diffusion orientation distribution function (ODF). Sparse representations have the potential to reduce the size of the data while also giving some insight into the data. To explore the results, we provide a visualization of the atoms of the dictionary and their frequency in the data to highlight the basic characteristics of the data. We present our proposed pipeline and demonstrate its performance on 5 HARDI datasets.
ChoreoVis: Planning and Assessing Formations in Dance Choreographies
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Beck, Samuel; Doerr, Nina; Kurzhals, Kuno; Riedlinger, Alexander; Schmierer, Fabian; Sedlmair, Michael; Koch, Steffen; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real-world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.
Interactive Optimization for Cartographic Aggregation of Building Features
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Takahashi, Shigeo; Kokubun, Ryo; Nishimura, Satoshi; Misue, Kazuo; Arikawa, Masatoshi; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Aggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale-aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation.
ProtEGOnist: Visual Analysis of Interactions in Small World Networks Using Ego-graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Brich, Nicolas; Harbig, Theresa A.; Witte Paz, Mathias; Nieselt, Kay; Krone, Michael; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Visualizing small-world networks such as protein-protein interaction networks or social networks often leads to visual clutter and limited interpretability. To overcome these problems, we present ProtEGOnist, a visualization approach designed to explore small-world networks. ProtEGOnist visualizes networks using ego-graphs that represent local neighborhoods. Egographs are visualized in an aggregated state as a glyph where the size encodes the size of the neighborhood and in a detailed version where the original network nodes can be explored. The ego-graphs are arranged in an ego-graph network, where edges encode similarity using the Jaccard index. Our design aims to reduce visual complexity and clutter while enabling detailed exploration and facilitating the discovery of meaningful patterns. To achieve this, our approach offers a network overview using ego-graphs, a radar chart for a one-to-many ego-graph comparison and meta-data integration, and detailed ego-graph subnetworks for interactive exploration. We demonstrate the applicability of our approach on a co-author network and two different protein-protein interaction networks. A web-based prototype of ProtEGOnist can be accessed online at https://protegonist-tuevis.cs.uni-tuebingen.de/.
RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lv, Cheng; Zhang, Huijie; Lin, Yiming; Dong, Jialu; Tian, Liang; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Analyzing the preference of route choice not only facilitates the understanding of individuals' decision-making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed-loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio-temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status.
Guided By AI: Navigating Trust, Bias, and Data Exploration in AI-Guided Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Ha, Sunwoo; Monadjemi, Shayan; Ottley, Alvitta; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the AI's lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness of AI-guided VA tools.
Investigating the Effect of Operation Mode and Manifestation on Physicalizations of Dynamic Processes
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Pahr, Daniel; Ehlers, Henry; Wu, Hsiang-Yun; Waldner, Manuela; Raidou, Renata Georgia; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We conducted a study to systematically investigate the communication of complex dynamic processes along a two-dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic).We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel, with complications illustrated through modifications to the model. The results of a mixed-methods lab study with 28 participants show that both physical manifestation and manual operation have a strong positive impact on the audience's engagement. The study does not show a measurable knowledge increase with respect to cardiovascular pathologies using manually operated physical representations. However, subjectively, participants report a better understanding of the process-mainly through non-visual cues like haptics, but also auditory cues. The study also indicates an increased task load when interacting with the process, which, however, seems to play a minor role for the participants. Overall, the study shows a clear potential of physicalization for the communication of complex dynamic processes, which only fully unfold if observers have to chance to interact with the process.
Should I make it round? Suitability of circular and linear layouts for comparative tasks with matrix and connective data
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Ståhlbom, Emilia; Molin, Jesper; Ynnerman, Anders; Lundström, Claes; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development.
Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Matzen, Laura E.; Howell, Breannan C.; Tuft, Marie; Divis, Kristin M.; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
People frequently make decisions based on uncertain information. Prior research has shown that visualizations of uncertainty can help to support better decision making. However, research has also shown that different representations of the same information can lead to different patterns of decision making. It is crucial for researchers to develop a better scientific understanding of when, why and how different representations of uncertainty lead viewers to make different decisions. This paper seeks to address this need by comparing geospatial visualizations of wildfire risk to verbal descriptions of the same risk. In three experiments, we manipulated the specificity of the uncertain information as well as the visual cues used to encode risk in the visualizations. All three experiments found that participants were more likely to evacuate in response to a hypothetical wildfire if the risk information was presented verbally. When the risk was presented visually, participants were less likely to evacuate, particularly when transparency was used to encode the risk information. Experiment 1 showed that evacuation rates were lower for transparency maps than for other types of visualizations. Experiments 2 and 3 sought to replicate this effect and to test how it related to other factors. Experiment 2 varied the hue used for the transparency maps and Experiment 3 manipulated the salience of the borders between the different risk levels. These experiments showed lower evacuation rates in response to transparency maps regardless of hue. The effect was partially, but not entirely, mitigated by adding salient borders to the transparency maps. Taken together, these experiments show that using transparency to encode information about risk can lead to very different patterns of decision making than other encodings of the same information.
CUPID: Contextual Understanding of Prompt-conditioned Image Distributions
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Zhao, Yayan; Li, Mingwei; Berger, Matthew; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
We present CUPID: a visualization method for the contextual understanding of prompt-conditioned image distributions. CUPID targets the visual analysis of distributions produced by modern text-to-image generative models, wherein a user can specify a scene via natural language, and the model generates a set of images, each intended to satisfy the user's description. CUPID is designed to help understand the resulting distribution, using contextual cues to facilitate analysis: objects mentioned in the prompt, novel, synthesized objects not explicitly mentioned, and their potential relationships. Central to CUPID is a novel method for visualizing high-dimensional distributions, wherein contextualized embeddings of objects, those found within images, are mapped to a low-dimensional space via density-based embeddings. We show how such embeddings allows one to discover salient styles of objects within a distribution, as well as identify anomalous, or rare, object styles. Moreover, we introduce conditional density embeddings, whereby conditioning on a given object allows one to compare object dependencies within the distribution. We employ CUPID for analyzing image distributions produced by large-scale diffusion models, where our experimental results offer insights on language misunderstanding from such models and biases in object composition, while also providing an interface for discovery of typical, or rare, synthesized scenes.
Visual Highlighting for Situated Brushing and Linking
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Doerr, Nina; Lee, Benjamin; Baricova, Katarina; Schmalstieg, Dieter; Sedlmair, Michael; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Brushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics.
An Experimental Evaluation of Viewpoint-Based 3D Graph Drawing
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Wageningen, Simon van; Mchedlidze, Tamara; Telea, Alexandru; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Node-link diagrams are a widely used metaphor for creating visualizations of relational data. Most frequently, such techniques address creating 2D graph drawings, which are easy to use on computer screens and in print. In contrast, 3D node-link graph visualizations are far less used, as they have many known limitations and comparatively few well-understood advantages. A key issue here is that such 3D visualizations require users to select suitable viewpoints. We address this limitation by studying the ability of layout techniques to produce high-quality views of 3D graph drawings. For this, we perform a thorough experimental evaluation, comparing 3D graph drawings, rendered from a covering sampling of all viewpoints, with their 2D counterparts across various state-of-the-art node-link drawing algorithms, graph families, and quality metrics. Our results show that, depending on the graph family, 3D node-link diagrams can contain a many viewpoints that yield 2D visualizations that are of higher quality than those created by directly using 2D node-link diagrams. This not only sheds light on the potential of 3D node-link diagrams but also gives a simple approach to produce high-quality 2D node-link diagrams.
InverseVis: Revealing the Hidden with Curved Sphere Tracing
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Lawonn, Kai; Meuschke, Monique; Günther, Tobias; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Exploratory analysis of scalar fields on surface meshes presents significant challenges in identifying and visualizing important regions, particularly on the surface's backside. Previous visualization methods achieved only a limited visibility of significant features, i.e., regions with high or low scalar values, during interactive exploration. In response to this, we propose a novel technique, InverseVis, which leverages curved sphere tracing and uses the otherwise unused space to enhance visibility. Our approach combines direct and indirect rendering, allowing camera rays to wrap around the surface and reveal information from the backside. To achieve this, we formulate an energy term that guides the image synthesis in previously unused space, highlighting the most important regions of the backside. By quantifying the amount of visible important features, we optimize the camera position to maximize the visibility of the scalar field on both the front and backsides. InverseVis is benchmarked against state-of-the-art methods and a derived technique, showcasing its effectiveness in revealing essential features and outperforming existing approaches.
EuroVis 2024 CGF 43-3: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Fuchs, Johannes; Dennig, Frederik L.; Heinle, Maria-Viktoria; Keim, Daniel A.; Bartolomeo, Sara Di; Aigner, Wolfgang; Archambault, Daniel; Bujack, Roxana
The visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network's topology and additional attributes for entities and their connections. Although node-link diagrams and adjacency matrices are widespread, they have inherent limitations. Node-link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric's design space to visualize multivariate network data - focusing on three main categories: juxtaposed visualizations, embedded on-node and on-edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io.

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