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IEEE Transactions on Visualization and Computer Graphics 2025

Collaborative work in large virtual environments often requires transitions from loosely-coupled collaboration at different locations to tightly-coupled collaboration at a common meeting point. Inspired by prior work on the continuum between these extremes, we present two novel interaction techniques designed to share spatial context while collaborating over large virtual distances. The first method replicates the familiar setup of a video conference by providing users with a virtual tablet to share video feeds with their peers. The second method called PASCAL (Parallel Avatars in a Shared Collaborative Aura Link) enables users to share their immediate spatial surroundings with others by creating synchronized copies of it at the remote locations of their collaborators. We evaluated both techniques in a within-subject user study, in which 24 participants were tasked with solving a puzzle in groups of two. Our results indicate that the additional contextual information provided by PASCAL had significantly positive effects on task completion time, ease of communication, mutual understanding, and co-presence. As a result, our insights contribute to the repertoire of successful interaction techniques to mediate between loosely- and tightly-coupled work in collaborative virtual environments.

IEEE Transactions on Visualization and Computer Graphics 2025

Working with scatterplots is a classic everyday task for data analysts, which gets increasingly complex the more plots are required to form an understanding of the underlying data. To help analysts retrieve relevant plots more quickly when they are needed, immersive virtual environments (iVEs) provide them with the option to freely arrange scatterplots in the 3D space around them. In this paper, we investigate the impact of different virtual environments on the users' ability to quickly find and retrieve individual scatterplots from a larger collection. We tested three different scenarios, all having in common that users were able to position the plots freely in space according to their own needs, but each providing them with varying numbers of landmarks serving as visual cues - an Emptycene as a baseline condition, a single landmark condition with one prominent visual cue being a Desk, and a multiple landmarks condition being a virtual Office. Results from a between-subject investigation with 45 participants indicate that the time and effort users invest in arranging their plots within an iVE had a greater impact on memory performance than the design of the iVE itself. We report on the individual arrangement strategies that participants used to solve the task effectively and underline the importance of an active arrangement phase for supporting the spatial memorization of scatterplots in iVEs.

2024 IEEE Uncertainty Visualization Workshop at IEEE VIS, 2024

second-order tensors are fundamental in various scientific and engineering domains, as they can represent properties such as material stresses or diffusion processes in brain tissue. In recent years, several approaches have been introduced and improved to analyze these fields using topological features, such as degenerate tensor locations, i.e., the tensor has repeated eigenvalues, or normal surfaces. Traditionally, the identification of such features has been limited to single tensor fields. However, it has become common to create ensembles to account for uncertainties and variability in simulations and measurements. In this work, we explore novel methods for describing and visualizing degenerate tensor locations in 3D symmetric second-order tensor field ensembles. We base our considerations on the tensor mode and analyze its practicality in characterizing the uncertainty of degenerate tensor locations before proposing a variety of visualization strategies to effectively communicate degenerate tensor information. We demonstrate our techniques for synthetic and simulation data sets. The results indicate that the interplay of different descriptions for uncertainty can effectively convey information on degenerate tensor locations.

Best Paper Award!