VMV: Vision, Modeling, and Visualization
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Browsing VMV: Vision, Modeling, and Visualization by Author "Agarwal, Shivam"
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Item A Design and Application Space for Visualizing User Sessions of Virtual and Mixed Reality Environments(The Eurographics Association, 2020) Agarwal, Shivam; Auda, Jonas; Schneegaß, Stefan; Beck, Fabian; Krüger, Jens and Niessner, Matthias and Stückler, JörgVirtual and mixed reality environments gain complexity due to the inclusion of multiple users and physical objects. A core challenge for developers and researchers while analyzing sessions from such environments lies in understanding the interaction between entities. Additionally, the raw data recorded from such sessions is difficult to analyze due to the simultaneous temporal and spatial changes of multiple entities. However, similar data has already been visualized in other areas of application. We analyze which aspects of these related visualizations can be leveraged for analyzing user sessions in virtual and mixed reality environments and describe a design and application space for such visualizations. First, we examine what information is typically generated in interactive virtual and mixed reality applications and how it can be analyzed through such visualizations. Next, we study visualizations from related research fields and derive seven visualization categories. These categories act as building blocks of the design space, which can be combined into specific visualization systems. We also discuss the application space for these visualizations in debugging and evaluation scenarios. We present two application examples that showcase how one can visualize virtual and mixed reality user sessions and derive useful insights from them.Item Visual Comparison of Multi-label Classification Results(The Eurographics Association, 2021) Krause, Cedric; Agarwal, Shivam; Ghoniem, Mohammad; Beck, Fabian; Andres, Bjoern and Campen, Marcel and Sedlmair, MichaelIn multi-label classification, we do not only want to analyze individual data items but also the relationships between the assigned labels. Employing different sources and algorithms, the label assignments differ. We need to understand these differences to identify shared and conflicting assignments. We propose a visualization technique that addresses these challenges. In graphs, we present the labels for any classification result as nodes and the pairwise overlaps of labels as links between them. These graphs are juxtaposed for the different results and can be diffed graphically. Clustering techniques are used to further analyze similarities between labels or classification results, respectively. We demonstrate our prototype in two application examples from the machine learning domain.Item Visualizing Sets and Changes in Membership Using Layered Set Intersection Graphs(The Eurographics Association, 2020) Agarwal, Shivam; Tkachev, Gleb; Wermelinger, Michel; Beck, Fabian; Krüger, Jens and Niessner, Matthias and Stückler, JörgChallenges in set visualization include representing overlaps among sets, changes in their membership, and details of constituent elements. We present a visualization technique that addresses these challenges. The approach uses set intersection graphs that explicitly visualize each set intersection as a rectangular node and elements as circles inside them. We represent the graph as a layered node-link diagram using colors to indicate the sets. The layers reflect different levels of intersections, from the base sets in the lowest layer to potentially the intersection of all sets in the highest layer. We provide different perspectives to show temporal changes in set membership. Graphs for individual, two, and all timesteps are visualized in static, diff, and aggregated views. Together with linked views and filters, the technique supports the detailed exploration of dynamic set data. We demonstrate the effectiveness of the proposed approach by discussing two application examples. The submitted supplemental material contains a video showing proposed interactions in the implementation and the prototype itself.