We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays through accommodation-invariance. Our system integrates a refractive lens eyepiece with a novel wavefront coding diffractive optical element, operating in tandem with a pre-processing convolutional neural network. We employ end-to-end learning to jointly optimize the wavefront-coding optics and the image pre-processing module. To 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. To 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.

翻译：本文提出一种新型计算近眼显示方法，通过消解调焦依赖性来解决立体显示中的会聚-调焦冲突问题。该系统将折射透镜目镜与创新的波前编码衍射光学元件相结合，并与预处理卷积神经网络协同工作。我们采用端到端学习方式联合优化波前编码光学元件与图像预处理模块。为实现该方法，我们建立了可微分的视网膜成像模型，该模型考虑了人眼光学系统引入的限幅孔径与色差效应。我们进一步将神经传递函数和对比敏感度函数整合到损失模型中，以涵盖相关感知效应。为解决离轴畸变问题，我们在预处理模块中引入了位置依赖性机制。除基于仿真的严格分析外，我们还制作了设计的衍射光学元件并搭建了实验平台，成功实现了高达四屈光度的深度范围内保持调焦不变性的验证。