THESIS
2023
1 online resource (vi, 65 pages) : illustrations (some color)
Abstract
Pupil swim is a kind of distortion resulting from imperfect lens distortion correction of Virtual
Reality Head-Mounted Displays (VR-HMDs) and eye motion. Because of pupil swim, there is
a sensation of movement or optic flow in parts of the virtual environment that the users expect
to be stationary, which can be disturbing. New VR-HMD prototypes need to be tested for pupil
swim, however psychophysical modelling of pupil swim perception is preferable as it is more
efficient. Existing studies on the psychophysical effects of different optic flow patterns cannot
fully explain pupil swim as these studies mainly focused on optic flow perceived in self-motion.
This concept was validated by decomposing pupil swim into a linear sum of common
components. The difference between the original pupil...[
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Pupil swim is a kind of distortion resulting from imperfect lens distortion correction of Virtual
Reality Head-Mounted Displays (VR-HMDs) and eye motion. Because of pupil swim, there is
a sensation of movement or optic flow in parts of the virtual environment that the users expect
to be stationary, which can be disturbing. New VR-HMD prototypes need to be tested for pupil
swim, however psychophysical modelling of pupil swim perception is preferable as it is more
efficient. Existing studies on the psychophysical effects of different optic flow patterns cannot
fully explain pupil swim as these studies mainly focused on optic flow perceived in self-motion.
This concept was validated by decomposing pupil swim into a linear sum of common
components. The difference between the original pupil swim optic flow map and an artificial
optic flow map reconstructed using the components was an inverse metric for the ability of
these patterns to represent pupil swim. Components were algorithmically selected from the
basis set of Zernike vector polynomials, which include common lens aberrations. It was found
that the identified components could represent pupil swim with higher accuracy compared to
optic flow from self-motion alone. Moreover, the linear coefficient fitted to a component
represented its magnitude in the pupil swim. By determining the quantitative relationship
between component magnitude and perception, a predictive model for pupil swim perception
was developed. This predictive model was trained and validated through a user study of pupil
swim from real VR-HMD lens designs. This perception model showed sufficient accuracy in
predicting subject-reported disorientation scores for different pupil swim cases.
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