Wavefront Coding for Accommodation-Invariant Near-Eye Displays

Abstract--We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays through accommodation-invariance. We employ end-to-end learning to jointly optimize the wavefront-coding optics and the image pre-processing module. T o implement this approach, we develop a differentiable retinal image formation model that accounts for limiting aperture and chromatic aberrations introduced by the eye optics. We further integrate the neural transfer function and the contrast sensitivity function into the loss model to account for related perceptual effects. T o tackle off-axis distortions, we incorporate position dependency into the pre-processing module. In addition to conducting rigorous analysis based on simulations, we also fabricate the designed diffractive optical element and build a benchtop setup, demonstrating accommodation-invariance for depth ranges of up to four diopters. HE simplicity of stereoscopic near-eye display (NED) design has made these systems particularly attractive for virtual reality (VR) and augmented reality (AR) applications. However, a major drawback hindering their widespread adoption is the vergence-accommodation conflict (V AC), which is caused by the mismatch between the two visual cues. In natural viewing conditions, vergence and accommodation work in synchrony, but the link between them gets broken in stereoscopic NEDs, resulting in severe visual discomfort [1], [2], [3]. Two groups of methods have addressed the V AC. Accommodation-enabling (AE) displays have aimed at delivering close-to-natural viewing experience by recreating near-correct retinal blur to drive the accommodation to the vergence distance of the object. We discuss AE display approaches in more details in Sec. Instead of recreating focus cues, accommodation-invariant (AI) displays have aimed at coupling vergence with accommodation by removing the retinal defocus blur completely.