Computer-Generated Holography (CGH) offers the potential for genuine, high-quality three-dimensional visuals. However, fulfilling this potential remains a practical challenge due to computational complexity and visual quality issues. We propose a new CGH method that exploits gaze-contingency and perceptual graphics to accelerate the development of practical holographic display systems. Firstly, our method infers the user's focal depth and generates images only at their focus plane without using any moving parts. Second, the images displayed are metamers; in the user's peripheral vision, they need only be statistically correct and blend with the fovea seamlessly. Unlike previous methods, our method prioritises and improves foveal visual quality without causing perceptually visible distortions at the periphery. To enable our method, we introduce a novel metameric loss function that robustly compares the statistics of two given images for a known gaze location. In parallel, we implement a model representing the relation between holograms and their image reconstructions. We couple our differentiable loss function and model to metameric varifocal holograms using a stochastic gradient descent solver. We evaluate our method with an actual proof-of-concept holographic display, and we show that our CGH method leads to practical and perceptually three-dimensional image reconstructions.
翻译:计算机光学全方位( CGH) 提供了真正、高质量三维视觉的潜力。 然而, 实现这一潜力仍是一个实际挑战, 原因是计算复杂性和视觉质量问题。 我们提出一种新的 CGH 方法, 利用观视宽度和感知性图形加速开发实用全息显示系统。 首先, 我们的方法推断用户的焦距深度, 只在其焦点平面上生成图像, 而不使用任何移动部分。 其次, 显示的图像是相片; 在用户的外围视觉中, 它们只需要在统计上正确, 并且与顶部无缝地混合。 与以往的方法不同, 我们的方法优先性, 并在不引起外观明显可见的扭曲的情况下, 改进视觉低视宽视质量。 为了便于我们的方法, 我们引入了新的代谢损失功能功能功能, 将两种给定图像的统计数据与已知的凝视位置进行对比。 与此同时, 我们使用一种模型, 代表传感图与其图像重建之间的关系。 我们将不同的损失功能和模型与代谢变异式的原位图和模型进行对比, 用一种实际的图像再显示方法, 我们的图像再显示, 我们的梯度- 和图像再显示方法, 我们的图像再对比。