Uncertain Pointer: Situated Feedforward Visualizations for Ambiguity-Aware AR Target Selection

TL;DR

This paper introduces Uncertain Pointer, a visualization technique for AR that annotates multiple target candidates to improve disambiguation, evaluated through experiments and literature analysis.

Contribution

It systematically explores and evaluates feedforward visualizations for ambiguity-aware AR target selection, proposing design guidelines based on empirical data.

Findings

Color coding enhances target differentiation.

Opacity modulation conveys system uncertainty effectively.

Design recommendations vary with AR context and scene sparsity.

Abstract

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.