Stray light analysis and suppression method of a pancake virtual reality head-mounted display

Ghost image analysis for pancake virtual reality systems

Pancake optics (also known as polarization-based catadioptric system) has been widely used as the imaging lens for virtual reality (VR) and mixed reality (MR) headsets because of its compact formfactor and excellent image quality. However, such a folded pancake optics not only dramatically lowers the optical efficiency to 25% because of the utilization of a half mirror, but also suffers from ghost images due to the stray light from multiple surface reflections and imperfect polarization control inside the optical system. In this paper, the origins including the light paths of the ghost images are explored by experiment and then analyzed by simulation. The effect of different incident angles on the intensity of each ghost is also investigated.

Comparing a head-mounted virtual reality perimeter and the Humphrey Field Analyzer for visual field testing in healthy and glaucoma patients

PURPOSE

To compare clinical visual field outputs in glaucoma and healthy patients returned by the Humphrey Field Analyzer (HFA) and virtual reality (Virtual Field, VF) perimetry.

METHODS

One eye of 54 glaucoma patients and 41 healthy subjects was prospectively tested (three times each in random order) using the HFA and VF perimeters (24-2 test grids). We extracted and compared global indices (mean deviation [MD] and pattern standard deviation [PSD]), pointwise sensitivity (and calculated 'equivalent' sensitivity after accounting for differences in background luminance) and pointwise defects. Bland-Altman (mean difference [Mdiff ] and 95% limits of agreement [LoA]) and intraclass correlation analyses were performed.

RESULTS

The VF test was shorter (by 76 s) and had lower fixation losses (by 0.08) and false-positive rate (by 0.01) compared to the HFA (all p < 0.0001). Intraclass correlations were 0.86, 0.82 and 0.47 for MD, PSD and pointwise sensitivity between devices, respectively. Test-retest variability was higher for VF (Mdiff 0.3 dB, LoA -7.6 to 8.2 dB) compared to the HFA (Mdiff -0.3 dB, LoA -6.4 to 5.9 dB), indicating greater test-retest variability. When using each device's underlying normative database, the HFA detected, on average, 7 more defects (at the p < 0.05 level) out of the 52 test locations compared to this iteration of VF in the glaucoma cohort.

CONCLUSIONS

Virtual Field returns global results that are correlated with the HFA, but pointwise sensitivities were more variable. Differences in test-retest variability and defect detection by its current normative database raise questions about the widespread adoption of VF in lieu of the HFA.

Machine learning phase modulation of liquid crystal devices for three-dimensional display

A machine learning phase modulation scheme based on convolutional neural networks (CNN) and recurrent neural network (RNN) is proposed to carry out the regression task of liquid crystal (LC) device electric field prediction for the 2D/3D switchable display. The hybrid neural network is built and trained based on the illuminance distribution under three-dimensional (3D) display. Compared with manual phase modulation, the modulation method using a hybrid neural network can achieve higher optical efficiency and lower crosstalk in the 3D display. The validity of the proposed method is confirmed through simulations and optical experiments.

Introduction to the feature issue on augmented/virtual reality: optics & photonics

In recent years, augmented/virtual reality (AR/VR) has been attracting attention and investment in both the tech and academic communities, kickstarting a new wave of innovations. In the wake of this momentum, this feature issue was launched to cover the latest advances in this burgeoning field that pertains to optics and photonics. Alongside the 31 research articles being published, this introduction is appended to share with readers the behind-the-issue stories, submission statistics, reading guides, author biographies, and editors' perspectives.