Optimizing Focal Plane Configuration for Near-Eye Multifocal Displays via the Learning-based Algorithm

Abstract

Multifocal displays which physically float multiple focal planes are one of the promising solutions to provide focus cues for near-eye displays. With respect to the focal plane arrangements, several works proposed to dynamically change focal plane configuration as it could efficiently cover wide depth range with a few focal planes to reduce focusing error. Although they optimize the plane locations based on the resultant retinal images for target volumetric scenes, computational loads to synthesize retinal images become significantly larger as the resolution of target contents increases. Here, we propose to exploit the deep neural network to figure out the optimized focal plane configuration without directly depicting resultant retinal images. We demonstrate that designed network computes the focal plane positions to achieve optimal retinal images through numerical simulations.