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Context-Responsive Labeling in Augmented Reality

机译:增强现实中的上下文响应标记

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Route planning and navigation are common tasks that often require additional information on points of interest. Augmented Reality (AR) enables mobile users to utilize text labels, in order to provide a composite view associated with additional information in a real-world environment. Nonetheless, displaying all labels for points of interest on a mobile device will lead to unwanted overlaps between information, and thus a context-responsive strategy to properly arrange labels is expected. The technique should remove overlaps, show the right level-of-detail, and maintain label coherence. This is necessary as the viewing angle in an AR system may change rapidly due to users’ behaviors. Coherence plays an essential role in retaining user experience and knowledge, as well as avoiding motion sickness. In this paper, we develop an approach that systematically manages label visibility and levels-of-detail, as well as eliminates unexpected incoherent movement. We introduce three label management strategies, including (1) occlusion management, (2) level-of-detail management, and (3) coherence management by balancing the usage of the mobile phone screen. A greedy approach is developed for fast occlusion handling in AR. A level-of-detail scheme is adopted to arrange various types of labels. A 3D scene manipulation is then built to simultaneously suppress the incoherent behaviors induced by viewing angle changes. Finally, we present the feasibility and applicability of our approach through one synthetic and two real-world scenarios, followed by a qualitative user study.
机译:路线规划和导航是通常需要有关兴趣点的额外信息的常见任务。增强现实(AR)使移动用户能够利用文本标签,以便提供与现实世界环境中的附加信息相关联的综合视图。尽管如此,在移动设备上显示所有兴趣点的标签将导致信息之间的不需要的重叠,因此预期正确排列标签的上下文响应策略。该技术应消除重叠,显示正确的细节水平,并保持标签一致性。这是必要的,因为AR系统中的视角可能由于用户的行为而迅速变化。一致性在保留用户体验和知识以及避免运动疾病方面发挥着重要作用。在本文中,我们开发了一种系统地管理标签可视性和细节水平的方法,以及消除意外的非连贯运动。我们介绍了三个标签管理策略,包括(1)遮挡管理,(2)细节水平管理,并通过平衡手机屏幕的使用来实现一致管理。开发了一种贪婪的方法,用于在AR中快速遮挡处理。采用详细程度的方案来安排各种类型的标签。然后构建3D场景操作以同时抑制通过观察角度变化引起的不连贯行为。最后,我们通过一个合成和两个现实世界的情景提出了我们方法的可行性和适用性,其次是定性的用户学习。

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