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Computer-Generated Holography (CGH) is a set of algorithmic methods for identifying holograms that reconstruct Three-Dimensional (3D) scenes in holographic displays. CGH algorithms decompose 3D scenes into multiplanes at different depth levels and rely on simulations of light that propagated from a source plane to a targeted plane. Thus, for n planes, CGH typically optimizes holograms using n plane-to-plane light transport simulations, leading to major time and computational demands. Our work replaces multiple planes with a focal surface and introduces a learned light transport model that could propagate a light field from a source plane to the focal surface in a single inference. Our learned light transport model leverages spatially adaptive convolution to achieve depth-varying propagation demanded by targeted focal surfaces. The proposed model reduces the hologram optimization process up to 1.5x, which contributes to hologram dataset generation and the training of future learned CGH models., Comment: SIGGRAPH Asia 2024 Technical Communications

Precision pose detection is increasingly demanded in fields such as personal fabrication, Virtual Reality (VR), and robotics due to its critical role in ensuring accurate positioning information. However, conventional vision-based systems used in these systems often struggle with achieving high precision and accuracy, particularly when dealing with complex environments or fast-moving objects. To address these limitations, we investigate Laser Speckle Imaging (LSI), an emerging optical tracking method that offers promising potential for improving pose estimation accuracy. Specifically, our proposed LSI-Based Tracking (SpecTrack) leverages the captures from a lensless camera and a retro-reflector marker with a coded aperture to achieve multi-axis rotational pose estimation with high precision. Our extensive trials using our in-house built testbed have shown that SpecTrack achieves an accuracy of 0.31{\deg} (std=0.43{\deg}), significantly outperforming state-of-the-art approaches and improving accuracy up to 200%.

Immersive displays are advancing rapidly in terms of delivering perceptually realistic images by utilizing emerging perceptual graphics methods such as foveated rendering. In practice, multiple such methods need to be performed sequentially for enhanced perceived quality. However, the limited power and computational resources of the devices that drive immersive displays make it challenging to deploy multiple perceptual models simultaneously. We address this challenge by proposing a computationally-lightweight, text-guided, learned multitasking perceptual graphics model. Given RGB input images, our model outputs perceptually enhanced images by performing one or more perceptual tasks described by the provided text prompts. Our model supports a variety of perceptual tasks, including foveated rendering, dynamic range enhancement, image denoising, and chromostereopsis, through multitask learning. Uniquely, a single inference step of our model supports different permutations of these perceptual tasks at different prompted rates (i.e., mildly, lightly), eliminating the need for daisy-chaining multiple models to get the desired perceptual effect. We train our model on our new dataset of source and perceptually enhanced images, and their corresponding text prompts. We evaluate our model's performance on embedded platforms and validate the perceptual quality of our model through a user study. Our method achieves on-par quality with the state-of-the-art task-specific methods using a single inference step, while offering faster inference speeds and flexibility to blend effects at various intensities.

In the pursuit of advancing holographic display technology, we face a unique yet persistent roadblock: the inflexibility of learned holography in adapting to various hardware configurations. This is due to the variances in the complex optical components and system settings in existing holographic displays. Although the emerging learned approaches have enabled rapid and high-quality hologram generation, any alteration in display hardware still requires a retraining of the model. Our work introduces a configurable learned model that interactively computes 3D holograms from RGB-only 2D images for a variety of holographic displays. The model can be conditioned to predefined hardware parameters of existing holographic displays such as working wavelengths, pixel pitch, propagation distance, and peak brightness without having to retrain. In addition, our model accommodates various hologram types, including conventional single-color and emerging multi-color holograms that simultaneously use multiple color primaries in holographic displays. Notably, we enabled our hologram computations to rely on identifying the correlation between depth estimation and 3D hologram synthesis tasks within the learning domain for the first time in the literature. We employ knowledge distillation via a student-teacher learning strategy to streamline our model for interactive performance. Achieving up to a 2x speed improvement compared to state-of-the-art models while consistently generating high-quality 3D holograms with different hardware configurations., Comment: 14 pages, 5 figures

Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce AutoColor , the first learned method for estimating the optimal light source powers required for illuminating multi-color holograms. For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information. We generate these synthetic images using a trending pipeline combining generative, large language, and monocular depth estimation models. Finally, we train our learned model using our dataset and experimentally demonstrate that AutoColor significantly decreases the number of steps required to optimize multi-color holograms from > 1000 to 70 iteration steps without compromising image quality., Comment: 6 pages, 2 figures, SPIE VR|AR|MR 2024

Holographic displays generate Three-Dimensional (3D) images by displaying single-color holograms time-sequentially, each lit by a single-color light source. However, representing each color one by one limits brightness in holographic displays. This paper introduces a new driving scheme for realizing brighter images in holographic displays. Unlike the conventional driving scheme, our method utilizes three light sources to illuminate each displayed hologram simultaneously at various intensity levels. In this way, our method reconstructs a multiplanar three-dimensional target scene using consecutive multi-color holograms and persistence of vision. We co-optimize multi-color holograms and required intensity levels from each light source using a gradient descent-based optimizer with a combination of application-specific loss terms. We experimentally demonstrate that our method can increase the intensity levels in holographic displays up to three times, reaching a broader range and unlocking new potentials for perceptual realism in holographic displays., Comment: 11 pages, 11 figures

An emerging alternative to conventional Augmented Reality (AR) glasses designs, Beaming displays promise slim AR glasses free from challenging design trade-offs, including battery-related limits or computational budget-related issues. These beaming displays remove active components such as batteries and electronics from AR glasses and move them to a projector that projects images to a user from a distance (1-2 meters), where users wear only passive optical eyepieces. However, earlier implementations of these displays delivered poor resolutions (7 cycles per degree) without any optical focus cues and were introduced with a bulky form-factor eyepiece (50 mm thick). This paper introduces a new milestone for beaming displays, which we call HoloBeam. In this new design, a custom holographic projector populates a micro-volume located at some distance (1-2 meters) with multiple planes of images. Users view magnified copies of these images from this small volume with the help of an eyepiece that is either a Holographic Optical Element (HOE) or a set of lenses. Our HoloBeam prototypes demonstrate the thinnest AR glasses to date with a submillimeter thickness (e.g., HOE film is only 120 um thick). In addition, HoloBeam prototypes demonstrate near retinal resolutions (24 cycles per degree) with a 70 degrees-wide field of view., Comment: 15 pages, 18 Figures, 1 Table, 1 Listing

A large portion of today's world population suffer from vision impairments and wear prescription eyeglasses. However, eyeglasses causes additional bulk and discomfort when used with augmented and virtual reality headsets, thereby negatively impacting the viewer's visual experience. In this work, we remedy the usage of prescription eyeglasses in Virtual Reality (VR) headsets by shifting the optical complexity completely into software and propose a prescription-aware rendering approach for providing sharper and immersive VR imagery. To this end, we develop a differentiable display and visual perception model encapsulating display-specific parameters, color and visual acuity of human visual system and the user-specific refractive errors. Using this differentiable visual perception model, we optimize the rendered imagery in the display using stochastic gradient-descent solvers. This way, we provide prescription glasses-free sharper images for a person with vision impairments. We evaluate our approach on various displays, including desktops and VR headsets, and show significant quality and contrast improvements for users with vision impairments., Comment: 12 pages, 9 figures, 1 table, 1 listing

This paper introduces a new multiplane CGH computation method to reconstruct artefact-free high-quality holograms with natural-looking defocus blur. Our method introduces a new targeting scheme and a new loss function. While the targeting scheme accounts for defocused parts of the scene at each depth plane, the new loss function analyzes focused and defocused parts separately in reconstructed images. Our method support phase-only CGH calculations using various iterative (e.g., Gerchberg-Saxton, Gradient Descent) and non-iterative (e.g., Double Phase) CGH techniques. We achieve our best image quality using a modified gradient descent-based optimization recipe where we introduce a constraint inspired by the double phase method. We validate our method experimentally using our proof-of-concept holographic display, comparing various algorithms, including multi-depth scenes with sparse and dense contents., Comment: 16 pages in total, first 9 pages are for the manuscript, remaining pages are for supplementary. For more visit: https://complightlab.com/publications/realistic_defocus_cgh For our codebase visit https://github.com/complight/realistic_defocus

Conventional image reconstruction models for lensless cameras often assume that each measurement results from convolving a given scene with a single experimentally measured point-spread function. These image reconstruction models fall short in simulating lensless cameras truthfully as these models are not sophisticated enough to account for optical aberrations or scenes with depth variations. Our work shows that learning a supervised primal-dual reconstruction method results in image quality matching state of the art in the literature without demanding a large network capacity. This improvement stems from our primary finding that embedding learnable forward and adjoint models in a learned primal-dual optimization framework can even improve the quality of reconstructed images (+5dB PSNR) compared to works that do not correct for the model error. In addition, we built a proof-of-concept lensless camera prototype that uses a pseudo-random phase mask to demonstrate our point. Finally, we share the extensive evaluation of our learned model based on an open dataset and a dataset from our proof-of-concept lensless camera prototype., Comment: 8 pages, 5 figures, not published at any conference

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.

Computer-Generated Holography (CGH) algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches., Comment: 11 pages. Corrected a typo in equation 3

In recent years, everyday activities such as work and socialization have steadily shifted to more remote and virtual settings. With the COVID-19 pandemic, the switch from physical to virtual has been accelerated, which has substantially affected various aspects of our lives, including business, education, commerce, healthcare, and personal life. This rapid and large-scale switch from in-person to remote interactions has revealed that our current technologies lack functionality and are limited in their ability to recreate interpersonal interactions. To help address these limitations in the future, we introduce "Telelife," a vision for the near future that depicts the potential means to improve remote living better aligned with how we interact, live and work in the physical world. Telelife encompasses novel synergies of technologies and concepts such as digital twins, virtual prototyping, and attention and context-aware user interfaces with innovative hardware that can support ultrarealistic graphics, user state detection, and more. These ideas will guide the transformation of our daily lives and routines soon, targeting the year 2035. In addition, we identify opportunities across high-impact applications in domains related to this vision of Telelife. Along with a recent survey of relevant fields such as human-computer interaction, pervasive computing, and virtual reality, the directions outlined in this paper will guide future research on remote living.

Existing near-eye display designs struggle to balance between multiple trade-offs such as form factor, weight, computational requirements, and battery life. These design trade-offs are major obstacles on the path towards an all-day usable near-eye display. In this work, we address these trade-offs by, paradoxically, \textit{removing the display} from near-eye displays. We present the beaming displays, a new type of near-eye display system that uses a projector and an all passive wearable headset. We modify an off-the-shelf projector with additional lenses. We install such a projector to the environment to beam images from a distance to a passive wearable headset. The beaming projection system tracks the current position of a wearable headset to project distortion-free images with correct perspectives. In our system, a wearable headset guides the beamed images to a user's retina, which are then perceived as an augmented scene within a user's field of view. In addition to providing the system design of the beaming display, we provide a physical prototype and show that the beaming display can provide resolutions as high as consumer-level near-eye displays. We also discuss the different aspects of the design space for our proposal., Comment: 10 pages. This is a preprint of a publication at IEEE Transactions on Visualization and Computer Graphics (TVCG), 2021. Presented at and nominated for best journal papers at IEEE Virtual Reality (VR) 2021 (https://ieeevr.org/2021/awards/conference-awards/)

Gaze tracking is an essential component of next generation displays for virtual reality and augmented reality applications. Traditional camera-based gaze trackers used in next generation displays are known to be lacking in one or multiple of the following metrics: power consumption, cost, computational complexity, estimation accuracy, latency, and form-factor. We propose the use of discrete photodiodes and light-emitting diodes (LEDs) as an alternative to traditional camera-based gaze tracking approaches while taking all of these metrics into consideration. We begin by developing a rendering-based simulation framework for understanding the relationship between light sources and a virtual model eyeball. Findings from this framework are used for the placement of LEDs and photodiodes. Our first prototype uses a neural network to obtain an average error rate of 2.67{\deg} at 400Hz while demanding only 16mW. By simplifying the implementation to using only LEDs, duplexed as light transceivers, and more minimal machine learning model, namely a light-weight supervised Gaussian process regression algorithm, we show that our second prototype is capable of an average error rate of 1.57{\deg} at 250 Hz using 800 mW., Comment: 10 pages, 8 figures, published in IEEE International Symposium on Mixed and Augmented Reality (ISMAR) 2020

We introduce a new gaze tracker for Head Mounted Displays (HMDs). We modify two off-the-shelf HMDs to be gaze-aware using Light Emitting Diodes (LEDs). Our key contribution is to exploit the sensing capability of LEDs to create low-power gaze tracker for virtual reality (VR) applications. This yields a simple approach using minimal hardware to achieve good accuracy and low latency using light-weight supervised Gaussian Process Regression (GPR) running on a mobile device. With our hardware, we show that Minkowski distance measure based GPR implementation outperforms the commonly used radial basis function-based support vector regression (SVR) without the need to precisely determine free parameters. We show that our gaze estimation method does not require complex dimension reduction techniques, feature extraction, or distortion corrections due to off-axis optical paths. We demonstrate two complete HMD prototypes with a sample eye-tracked application, and report on a series of subjective tests using our prototypes., Comment: 14 pages, 7 figures. THIS WORK WAS CONDUCTED IN 2015

Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acuity of the human visual system, along with a resulting need to follow the gaze of the eye as it moves, a process referred to as foveation. A display that moves its content along with the eye may be called a Foveated Display, though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity. We therefore recommend a definition for the term Foveated Display that accepts both of these interpretations. Furthermore, we include a simplified model for human visual Acuity Distribution Functions (ADFs) at various levels of visual acuity, across wide fields of view and propose comparison of this ADF with the Resolution Distribution Function of a foveated display for evaluation of its resolution at a particular gaze direction. We also provide a taxonomy to allow the field to meaningfully compare and contrast various aspects of foveated displays in a display and optical technology-agnostic manner., Comment: 9 pages, 8 figures, presented at IEEE VR 2020