不确定指针：面向模糊感知的AR目标选择的定位前馈可视化
Uncertain Pointer: Situated Feedforward Visualizations for Ambiguity-Aware AR Target Selection
AI总结：
Uncertain Pointer通过前馈可视化帮助AR中目标选择的歧义消除，通过视觉标识和不确定性调节提升用户体验。
摘要：
在增强现实（AR）中，目标歧义消除对于解决输入歧义至关重要，尤其是在处理远处物体或复杂场景时。然而，支持此过程的视觉前馈技术仍被忽视。我们提出了Uncertain Pointer，一种系统探索前馈可视化的研究，该可视化在用户确认之前标注多个候选目标，通过添加独特的视觉标识（例如颜色）来支持歧义消除，或通过调节视觉强度（例如不透明度）来传达系统不确定性。首先，我们通过分析现有放置策略和目标可视化中使用的视觉指示符，构建了25个指针空间，覆盖30年相关文献。然后，我们通过两个在线实验（n=60和40）评估这些指针，测量用户偏好、信心、心理舒适度、目标可见性和可识别性，针对不同物体距离和稀疏度进行评估。最后，从结果中我们得出设计建议，根据AR环境和歧义消除技术选择不同的Uncertain Pointers。
英文摘要：
Target disambiguation is crucial in resolving input ambiguity in augmented reality (AR), especially for queries over distant objects or cluttered scenes on the go. Yet, visual feedforward techniques that support this process remain underexplored. We present Uncertain Pointer, a systematic exploration of feedforward visualizations that annotate multiple candidate targets before user confirmation, either by adding distinct visual identities (e.g., colors) to support disambiguation or by modulating visual intensity (e.g., opacity) to convey system uncertainty. First, we construct a pointer space of 25 pointers by analyzing existing placement strategies and visual signifiers used in target visualizations across 30 years of relevant literature. We then evaluate them through two online experiments (n = 60 and 40), measuring user preference, confidence, mental ease, target visibility, and identifiability across varying object distances and sparsities. Finally, from the results, we derive design recommendations in choosing different Uncertain Pointers based on AR context and disambiguation techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
