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Related Experiment Video

Updated: Aug 30, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Gaze-Vergence-Controlled See-Through Vision in Augmented Reality.

Zhimin Wang, Yuxin Zhao, Feng Lu

    IEEE Transactions on Visualization and Computer Graphics
    |September 1, 2022
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    Summary

    This study introduces gaze-vergence-controlled (GVC) see-through vision in augmented reality (AR). GVC offers a more efficient and user-preferred interaction method for AR see-through applications compared to traditional controls.

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    Area of Science:

    • Human-Computer Interaction
    • Augmented Reality
    • Computer Vision

    Background:

    • Augmented Reality (AR) see-through vision allows users to perceive occluded objects.
    • Current AR see-through research primarily focuses on visual aspects, neglecting optimal interaction methods.
    • Conventional interaction modalities like midair clicks and speech may not be intuitive for controlling see-through vision.

    Purpose of the Study:

    • To propose and evaluate a novel gaze-vergence-controlled (GVC) see-through vision technique for AR.
    • To investigate the natural control of see-through vision using eye gaze and vergence, aligning with human visual perception.
    • To compare the efficiency and user preference of GVC against traditional interaction methods.

    Main Methods:

    • Developed a gaze tracking module with infrared cameras and algorithms for gaze depth estimation.
    • Integrated the gaze tracking module into Microsoft HoloLens 2.
    • Proposed and implemented two distinct GVC modes for AR see-through vision.
    • Conducted experiments to evaluate gaze depth estimation accuracy and compare GVC performance with conventional methods.

    Main Results:

    • The developed gaze depth estimation module demonstrated efficiency and accuracy.
    • GVC techniques proved superior to conventional interaction modalities in terms of efficiency.
    • Users expressed a higher preference for the GVC interaction methods.
    • Four example applications showcasing GVC see-through vision were presented.

    Conclusions:

    • Gaze-vergence control offers a natural and effective interaction paradigm for AR see-through vision.
    • The proposed GVC techniques enhance user experience and task efficiency in AR environments.
    • This research opens new avenues for intuitive control in AR applications requiring perception of occluded information.