Recent progresses of near-eye display for AR and VR

Near-eye displays (NEDs) for augmented and virtual reality (AR/VR) are spotlighted because they have a possibility to provide much more immersive experiences never possible before. With the virtue of recent progress in sensors, optics, and computer science, several commercial products are already available, and the consumer market is expanding rapidly. However, there are several challenging issues for AR and VR NEDs to become closer to our lives. Here, we will explore these issues and important topics for AR and VR, and introduce some of the ideas to overcome them: diffractive optical elements (DOEs), retinal projection displays, and 3D display with focus cues. First, unlike VR with a simple optical system, AR that needs to merge an artificial image with an outer scene requires additional optics. The diffractive elements have the merit of being thin and transparent, suitable for the image combiner. Among them, holographic optical elements (HOEs) have great potential as they can record the desired volume grating from the simple lens to the complex wavefront using light interference. Second, in order to wear the NEDs for a long time, it must deal with the visual fatigue as well as the form factor. Retinal projection display can effectively prevent the vergence-accommodation conflict problem even with a simple optical design. In the retinal projection display, the light rays from the display are adjusted to converge into a small point using a lens. It ensures a wide depth range in which the images are clearly visible. Furthermore, it is possible to provide observers with accurate focus cues for the alleviation of visual fatigue via multi-layer displays and holographic displays. Recently, we conceived tomographic NED that can reproduce dense focal planes. We confirm that this system provides quasi-continuous focus cues, semi-original contrast, and considerable depth of field. The experimental results of our prototypes are explained. We also explain the recent activities of using deep learning in holographic NED system.