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.

翻译：目标消歧对于解决增强现实（AR）中的输入歧义至关重要，特别是在移动场景中对远距离对象或杂乱场景进行查询时。然而，支持这一过程的前馈可视化技术仍未得到充分探索。本文提出“不确定性指针”，这是一种系统性的前馈可视化方法，可在用户确认前对多个候选目标进行标注：既可通过添加差异化视觉标识（如颜色）以辅助消歧，也可通过调节视觉强度（如透明度）以传达系统不确定性。首先，我们通过分析近30年相关文献中目标可视化所使用的现有布局策略与视觉符号，构建了包含25种指针的指针空间。随后，我们通过两项在线实验（样本量分别为60和40）对其进行评估，测量用户在不同物体距离与稀疏度下的偏好度、置信度、心理负荷度、目标可见度与可识别性。最后，基于实验结果，我们提出了根据AR场景与消歧技术选择不同“不确定性指针”的设计建议。