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The Shared View Paradigm in Asymmetric Virtual Reality Setups
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2020
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De Gruyter
Zusammenfassung
Asymmetric Virtual Reality (VR) applications are a substantial subclass of multi-user VR that offers not all participants the same interaction possibilities with the virtual scene. While one user might be immersed using a VR head-mounted display (HMD), another user might experience the VR through a common desktop PC. In an educational scenario, for example, learners can use immersive VR technology to inform themselves at different exhibits within a virtual scene. Educators can use a desktop PC setup for following and guiding learners through virtual exhibits and still being able to pay attention to safety aspects in the real world (e. g., avoid learners bumping against a wall). In such scenarios, educators must ensure that learners have explored the entire scene and have been informed about all virtual exhibits in it. According visualization techniques can support educators and facilitate conducting such VR-enhanced lessons. One common technique is to render the view of the learners on the 2D screen available to the educators. We refer to this solution as the <em>shared view paradigm</em>. However, this straightforward visualization involves challenges. For example, educators have no control over the scene and the collaboration of the learning scenario can be tedious. In this paper, we differentiate between two classes of visualizations that can help educators in asymmetric VR setups. First, we investigate five techniques that visualize the view direction or field of view of users (<em>view visualizations</em>) within virtual environments. Second, we propose three techniques that can support educators to understand what parts of the scene learners already have explored (<em>exploration visualization</em>). In a user study, we show that our participants preferred a volume-based rendering and a view-in-view overlay solution for view visualizations. Furthermore, we show that our participants tended to use combinations of different view visualizations.