Holographic displays promise unprecedented capabilities for direct-view displays as well as virtual and augmented reality applications. However, one of the biggest challenges for computer-generated holography (CGH) is the fundamental tradeoff between algorithm runtime and achieved image quality, which has prevented high-quality holographic image synthesis at fast speeds. Moreover, the image quality achieved by most holographic displays is low, due to the mismatch between the optical wave propagation of the display and its simulated model. Here, we develop an algorithmic CGH framework that achieves unprecedented image fidelity and real-time framerates. Our framework comprises several parts, including a novel camera-in-the-loop optimization strategy that allows us to either optimize a hologram directly or train an interpretable model of the optical wave propagation and a neural network architecture that represents the first CGH algorithm capable of generating full-color high-quality holographic images at 1080p resolution in real time.

Occlusion is a powerful visual cue that is crucial for depth perception and realism in optical see-through augmented reality (OST-AR). However, existing OST-AR systems additively overlay physical and digital content with beam combiners - an approach that does not easily support mutual occlusion, resulting in virtual objects that appear semi-transparent and unrealistic. In this work, we propose a new type of occlusion-capable OST-AR system. Rather than additively combining the real and virtual worlds, we employ a single digital micromirror device (DMD) to merge the respective light paths in a multiplicative manner. This unique approach allows us to simultaneously block light incident from the physical scene on a pixel-by-pixel basis while also modulating the light emitted by a light-emitting diode (LED) to display digital content. Our technique builds on mixed binary/continuous factorization algorithms to optimize time-multiplexed binary DMD patterns and their corresponding LED colors to approximate a target augmented reality (AR) scene. In simulations and with a prototype benchtop display, we demonstrate hard-edge occlusions, plausible shadows, and also gaze-contingent optimization of this novel display mode, which only requires a single spatial light modulator.

Holographic near-eye displays are a key enabling technology for virtual and augmented reality (VR/AR) applications. Holographic stereograms (HS) are a method of encoding a light field into a hologram, which enables them to natively support view-dependent lighting effects. However, existing HS algorithms require the choice of a hogel size, forcing a tradeoff between spatial and angular resolution. Based on the fact that the short-time Fourier transform (STFT) connects a hologram to its observable light field, we develop the overlap-add stereogram (OLAS) as the correct method of "inverting" the light field into a hologram via the STFT. The OLAS makes more efficient use of the information contained within the light field than previous HS algorithms, exhibiting better image quality at a range of distances and hogel sizes. Most remarkably, the OLAS does not degrade spatial resolution with increasing hogel size, overcoming the spatio-angular resolution tradeoff that previous HS algorithms face. Importantly, the optimal hogel size of previous methods typically varies with the depth of every object in a scene, making the OLAS not only a hogel size-invariant method, but also nearly scene independent. We demonstrate the performance of the OLAS both in simulation and on a prototype near-eye display system, showing focusing capabilities and view-dependent effects.

Virtual reality systems are widely believed to be the next major computing platform. There are, however, some barriers to adoption that must be addressed, such as that of motion sickness - which can lead to undesirable symptoms including postural instability, headaches, and nausea. Motion sickness in virtual reality occurs as a result of moving visual stimuli that cause users to perceive self-motion while they remain stationary in the real world. There are several contributing factors to both this perception of motion and the subsequent onset of sickness, including field of view, motion velocity, and stimulus depth. We verify first that differences in vection due to relative stimulus depth remain correlated with sickness. Then, we build a dataset of stereoscopic 3D videos and their corresponding sickness ratings in order to quantify their nauseogenicity, which we make available for future use. Using this dataset, we train a machine learning algorithm on hand-crafted features (quantifying speed, direction, and depth as functions of time) from each video, learning the contributions of these various features to the sickness ratings. Our predictor generally outperforms a naïve estimate, but is ultimately limited by the size of the dataset. However, our result is promising and opens the door to future work with more extensive datasets. This and further advances in this space have the potential to alleviate developer and end user concerns about motion sickness in the increasingly commonplace virtual world.

Although emerging virtual and augmented reality (VR/AR) systems can produce highly immersive experiences, they can also cause visual discomfort, eyestrain, and nausea. One of the sources of these symptoms is a mismatch between vergence and focus cues. In current VR/AR near-eye displays, a stereoscopic image pair drives the vergence state of the human visual system to arbitrary distances, but the accommodation, or focus, state of the eyes is optically driven towards a fixed distance. In this work, we introduce a new display technology, dubbed accommodation-invariant (AI) near-eye displays, to improve the consistency of depth cues in near-eye displays. Rather than producing correct focus cues, AI displays are optically engineered to produce visual stimuli that are invariant to the accommodation state of the eye. The accommodation system can then be driven by stereoscopic cues, and the mismatch between vergence and accommodation state of the eyes is significantly reduced. We validate the principle of operation of AI displays using a prototype display that allows for the accommodation state of users to be measured while they view visual stimuli using multiple different display modes.