Multi-user augmented reality with communication efficient and spatially consistent virtual objects
Multi-user augmented reality with communication efficient and spatially consistent virtual objects
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DOI：
10.1145/3386367.3431312
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发表时间：
2020-11
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影响因子：
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通讯作者：
Xukan Ran;Carter Slocum;Yi-Zhen Tsai;Kittipat Apicharttrisorn;M. Gorlatova;Jiasi Chen
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文献类型：
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作者：
Xukan Ran;Carter Slocum;Yi-Zhen Tsai;Kittipat Apicharttrisorn;M. Gorlatova;Jiasi Chen
多用户增强现实（AR），即多个共处一地的用户观看一组共同的虚拟对象，正变得越来越流行。例如，谷歌的《Just a Line》允许多个用户在同一物理空间绘制虚拟涂鸦。多用户AR需要网络通信来协调每个用户显示器上虚拟对象的位置，但目前对于此类应用如何通信知之甚少。在这项工作中，我们通过表明通信数据直接影响在用户显示器上渲染的虚拟对象的延迟和定位，来填补这一关键知识空白。我们针对所发现的问题从三个方面制定了解决方案：（1）有效的通信策略，在虚拟对象的空间一致性和通信延迟之间进行权衡；（2）一种新的度量标准，使移动AR设备能够在移动并观察更多场景时更新其虚拟对象；（3）一种工具，用于自动量化虚拟对象的位置在时间和空间上无意发生的变化程度。我们在运行开源AR的安卓智能手机上进行了评估。结果表明，与基线方法相比，我们的系统SPAR可将延迟降低多达55%，同时将空间不一致性降低多达60%。
Multi-user augmented reality (AR), where multiple co-located users view a common set of virtual objects, is becoming increasingly popular. For example, Google Just a Line allows multiple users to draw virtual graffiti in the same physical space. Multi-user AR requires network communications in order to coordinate the positions of the virtual objects on each user's display, yet there is currently little understanding of how such apps communicate. In this work, we address this key gap in knowledge by showing that the communicated data directly impacts the latency and positioning of the virtual objects rendered on the users' displays. We develop solutions to these problems that we find along three facets: (1) efficient communication strategies that trade off communication latency for spatial consistency of the virtual objects; (2) a new metric that enables mobile AR devices to update their virtual objects as they move around and observe more of the scene; and (3) a tool to automatically quantify how much the virtual objects' positions inadvertently change in time and space. Our evaluation is performed on Android smartphones running open-source AR. The results show that our system, SPAR, can decrease the latency by up to 55%, while decreasing the spatial inconsistency by up to 60%, compared to baseline methods.