Metameric Varifocal Holograms

Metameric Varifocal Holograms
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DOI:
10.1109/vr51125.2022.00096
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发表时间:
2021-10
期刊:
2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
影响因子:
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通讯作者:
David R. Walton;Koray Kavaklı;R. K. D. Anjos;David Swapp;T. Weyrich;H. Urey;A. Steed;Tobias Ritschel;K. Akşit
David R. Walton;Koray Kavaklı;R. K. D. Anjos;David Swapp;T. Weyrich;H. Urey;A. Steed;Tobias Ritschel;K. Akşit
中科院分区:
其他
文献类型:
--
作者:
David R. Walton;Koray Kavaklı;R. K. D. Anjos;David Swapp;T. Weyrich;H. Urey;A. Steed;Tobias Ritschel;K. Akşit

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计算机生成全息(CGH)提供了真正的,高质量的三维视觉效果的潜力。然而,由于计算复杂性和视觉质量问题,实现这种潜力仍然是一个实际的挑战。我们提出了一种新的计算全息方法,利用视线偶然性和感知图形,以加速实用的全息显示系统的发展。首先,我们的方法推断出用户的焦深,并生成图像只在他们的焦平面,而不使用任何移动部件。其次,显示的图像是条件等色;在用户的周边视觉中,它们只需要在统计上正确并与中央凹无缝融合。与以前的方法不同,我们的方法优先考虑并提高了中央凹视觉质量,而不会在外围引起感知上可见的失真。为了使我们的方法,我们引入了一种新的同色异谱损失函数,鲁棒地比较了两个给定的图像的统计数据为一个已知的凝视位置。同时,我们实现了一个模型,表示全息图和它们的图像重建之间的关系。我们耦合我们的可微损失函数和模型同色异谱变焦全息图使用随机梯度下降求解器。我们评估我们的方法与一个实际的概念验证全息显示,我们表明,我们的计算全息方法导致实际和感知三维图像重建。
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.