用编码光学与神经网络实现看远看近都清晰的镜片
Wavefront Coding for Accommodation-Invariant Near-Eye Displays
- 通过编码光学+神经网络预处理，让眼睛无需调节就能看清不同距离
- 实测在4屈光度范围内保持清晰成像，解决视差调节冲突
- 适合做虚拟现实/增强现实眼镜，尤其对视力不稳定的用户友好
我们提出一种新型计算式近眼显示方法，通过实现视觉调节不变性来解决立体显示中的视差-调节冲突问题。系统结合折射镜片与新型波前编码衍射光学元件，并协同一个预处理卷积神经网络。采用端到端学习联合优化光学编码与图像预处理模块。为此，我们构建了一个可微分的视网膜成像模型，考虑了人眼光学带来的限光孔径和色差影响。同时将神经传递函数与对比敏感度函数引入损失函数，以模拟感知效应。为应对离轴畸变，预处理模块加入位置依赖性。除了仿真分析外，我们还制备了设计的衍射光学元件并搭建实验平台，实证在最高达4屈光度的深度范围内实现调节不变性。
原文摘要 · Abstract (English)
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.
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