PeriphAR: Fast and Accurate Real-World Object Selection with Peripheral Augmented Reality Displays

TL;DR

PeriphAR is a novel visualization technique that enhances peripheral vision cues for gaze-based object selection in AR, improving accuracy and speed by leveraging peripheral color sensitivity and contrast strategies.

Contribution

This work introduces PeriphAR, a new peripheral visualization method for gaze-based selection in AR, with strategies to optimize color contrast and improve real-world object detection performance.

Findings

Peripheral vision is more sensitive to color than shape.

Contrast enhancement improves peripheral detection accuracy.

PeriphAR enables faster and more accurate object selection in AR.

Abstract

Gaze-based selection in XR requires visual confirmation due to eye-tracking limitations and target ambiguity in 3D contexts. Current designs for wide-FOV displays use world-locked, central overlays, which are not conducive to always-on AR glasses. This paper introduces PeriphAR (per-ree-far), a visualization technique that leverages peripheral vision for feedback during gaze-based selection on a monocular AR display. In a first user study, we isolated text, color, and shape properties of target objects to compare peripheral selection cues. Peripheral vision was more sensitive to color than shape, but this sensitivity rapidly declined at lower contrast. To preserve preattentive processing of color, we developed two strategies to enhance color in users' peripheral vision. In a second user study, our strategy that maximized contrast of the target to the neighboring object with the most similar color was subjectively preferred. As proof of concept, we implemented PeriphAR in an end-to-end system to test performance with real-world object detection.