DeepMetricEye: Metric Depth Estimation in Periocular VR Imagery
Yitong Sun Zijian Zhou Cyriel Diels Ali Asadipour
Despite the enhanced realism and immersion provided by VR headsets, users
frequently encounter adverse effects such as digital eye strain (DES), dry eye,
and potential long-term visual impairment due to excessive eye stimulation from
VR displays and pressure from the mask. Recent VR headsets are increasingly
equipped with eye-oriented monocular cameras to segment ocular feature maps.
Yet, to compute the incident light stimulus and observe periocular condition
alterations, it is imperative to transform these relative measurements into
metric dimensions. To bridge this gap, we propose a lightweight framework
derived from the U-Net 3+ deep learning backbone that we re-optimised, to
estimate measurable periocular depth maps. Compatible with any VR headset
equipped with an eye-oriented monocular camera, our method reconstructs
three-dimensional periocular regions, providing a metric basis for related
light stimulus calculation protocols and medical guidelines. Navigating the
complexities of data collection, we introduce a Dynamic Periocular Data
Generation (DPDG) environment based on UE MetaHuman, which synthesises
thousands of training images from a small quantity of human facial scan data.
Evaluated on a sample of 36 participants, our method exhibited notable efficacy
in the periocular global precision evaluation experiment, and the pupil
diameter measurement.