Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them occlusion-free while keeping visual linkings legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers the current and predicted future states of objects and labels, such as positions and velocities, as well as the user's viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. Our experiments on two real-world datasets show that RL-LABEL effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on AR labels within dynamic scenes.

翻译：标签在增强现实（AR）中广泛用于显示数字信息。确保AR标签的可读性需要将其放置在无遮挡的位置，同时保持视觉连接线的清晰可辨，尤其是在场景中存在多个标签时。尽管现有的基于优化方法（如基于力的方法）在静态场景中能够有效管理AR标签，但在物体不断移动的动态场景中往往表现不佳。这是因为它们专注于生成当前时刻的最优布局，而忽略了未来时刻，导致布局随时间推移变得次优或不稳定。在本工作中，我们提出RL-LABEL，一种基于深度强化学习的方法，用于管理涉及移动物体的场景中AR标签的放置。RL-LABEL综合考虑物体和标签当前及预测的未来状态（如位置和速度）以及用户视点，以做出关于标签放置的明智决策。它平衡了即时目标与长期目标之间的权衡。我们在两个真实数据集上的实验表明，RL-LABEL有效学习了长期优化的决策过程，通过最小化标签遮挡、连线交叉和标签移动距离，优于两种基线方法（即无视图管理和基于力的方法）。此外，一项涉及18名参与者的用户研究显示，RL-LABEL在帮助用户于动态场景中识别、比较和总结AR标签数据方面优于基线方法。