CHING-YI TSAI

Uncertain Pointer
Situated Feedforward Visualizations for Ambiguity-Aware AR Target Selection
Ching-Yi Tsai, Nicole Tacconi, Andrew D. Wilson, and Parastoo Abtahi
ACM CHI'26 PAPER | 13 APRIL 2026

Mouse Cursors for PC
mouse cursor evolution
Mouse cursors help perfom realiable actions over a single screen object.
Uncertain Pointers for AR
airracket gif
Uncertain Pointers help disambiguate uncertain input over multiple real-world targets.

Intro

A cursor is clear and useful for highlighting a "single" 2D object, and it has been explored through many iterations — from dots to arrows to tilted arrows (see James and Alan Kay’s email for example) — to provide feedforward about where a click will land. But what if the system can’t reliably tell which target the user intends to select? When input is uncertain, as in noisy, everyday real-world AR, what visual feedforward should we use? In this work, we explore Uncertain Pointers that support diambiguation by annotating "multiple" real-world targets.

Uncertain Pointer Design

Level Pointer

Our level visualizations modulate pointer intensity (e.g., size, opacity) to reveal the system’s interpretation.

Identity Pointer

Our identity visualizations add descriptive attributes (e.g., color, letters) to support verbal disambiguation.

Pointer Archetypes + Uncertainty Viusalizations = Uncertain Pointers

Our key idea in exploring Uncertain Pointers is to combine established pointer designs with visual signifiers commonly used for uncertainty visualization. We systematically surveyed 30 years of ACM CHI, UIST, DIS, VRST, SUI, and AutomotiveUI, as well as IEEE TVCG, ISMAR, 3DUI, and VR. We summarized four pointer archetypes: internal, external, boundary, and fill and found the most prevalent uncertainty signifiers: size, color, opacity, and text/numeric/symbol.

Pointer space of Uncertain Pointer.

Evaluations across Different Target Complexities

We conducted two pre-registered online studies with 100 participants using photorealistic scenes with Gaussian splats to simulate different target complexities (near vs. far, sparse vs. densely-packed) and measured pointer identifiability and target visibility, along with subjective metrics such as user preference and perceived mental effort, etc.

We uses Gaussian splat to render different real-world scenes to simulate our pointer designs over projects.

We apply data analysis such as clustering to understand the trade-off and effectiveness of different Uncertain Pointers, distilling design recommendations.

Our study information differernt clusters of design combinations according to (a) objective and (b) subjective metrics.

Additional Applications

We demonstrate Uncertain Pointers in human-robot interaction: (A–C) show a robot placing objects with linguistic ambiguity resolved via boundary pointers with text labels, and (D–F) show gaze-based tool fetching where level pointers provide feedforward for intent clarification.

Additional human-robot interaction use cases.