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Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display?

机译:网络连通性的沉浸式协作分析:CAVE式还是头戴式显示器?

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High-quality immersive display technologies are becoming mainstream with the release of head-mounted displays (HMDs) such as the Oculus Rift. These devices potentially represent an affordable alternative to the more traditional, centralised CAVE-style immersive environments. One driver for the development of CAVE-style immersive environments has been collaborative sense-making. Despite this, there has been little research on the effectiveness of collaborative visualisation in CAVE-style facilities, especially with respect to abstract data visualisation tasks. Indeed, very few studies have focused on the use of these displays to explore and analyse abstract data such as networks and there have been no formal user studies investigating collaborative visualisation of abstract data in immersive environments. In this paper we present the results of the first such study. It explores the relative merits of HMD and CAVE-style immersive environments for collaborative analysis of network connectivity, a common and important task involving abstract data. We find significant differences between the two conditions in task completion time and the physical movements of the participants within the space: participants using the HMD were faster while the CAVE2 condition introduced an asymmetry in movement between collaborators. Otherwise, affordances for collaborative data analysis offered by the low-cost HMD condition were not found to be different for accuracy and communication with the CAVE2. These results are notable, given that the latest HMDs will soon be accessible (in terms of cost and potentially ubiquity) to a massive audience.
机译:随着Oculus Rift等头戴式显示器(HMD)的发布,高质量的沉浸式显示技术正成为主流。这些设备潜在地代表了更传统的集中式CAVE式沉浸式环境的负担得起的替代方案。协作感官开发是CAVE风格沉浸式环境开发的一个驱动力。尽管如此,关于CAVE风格设施中的协作可视化效果的研究很少,尤其是在抽象数据可视化任务方面。确实,很少有研究专注于使用这些显示器来探索和分析诸如网络之类的抽象数据,并且还没有正式的用户研究来研究沉浸式环境中抽象数据的协作可视化。在本文中,我们介绍了第一个此类研究的结果。它探索了HMD和CAVE式沉浸式环境在网络连接协作分析方面的相对优势,这是涉及抽象数据的常见且重要的任务。我们发现两种条件在任务完成时间和参与者在空间内的身体运动之间存在显着差异:使用HMD的参与者速度更快,而CAVE2条件引入了协作者之间运动的不对称性。否则,在低成本HMD条件下提供的协作数据分析能力在准确性和与CAVE2的通信上并没有不同。这些结果是值得注意的,因为最新的HMD很快就会(在成本和潜在的普遍性方面)为广大观众所使用。

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