We introduce a novel geometry-guided online video view synthesis method with enhanced view and temporal consistency. Traditional approaches achieve high-quality synthesis from dense multi-view camera setups but require significant computational resources. In contrast, selective-input methods reduce this cost but often compromise quality, leading to multi-view and temporal inconsistencies such as flickering artifacts. Our method addresses this challenge to deliver efficient, high-quality novel-view synthesis with view and temporal consistency. The key innovation of our approach lies in using global geometry to guide an image-based rendering pipeline. To accomplish this, we progressively refine depth maps using color difference masks across time. These depth maps are then accumulated through truncated signed distance fields in the synthesized view’s image space. This depth representation is view and temporally consistent, and is used to guide a pre-trained blending network that fuses multiple forward-rendered input-view images. Thus, the network is encouraged to output geometrically consistent synthesis results across multiple views and time. Our approach achieves consistent, high-quality video synthesis, while running efficiently in an online manner.

We propose a method that achieves state-of-the-art rendering quality and efficiency on monocular dynamic scene reconstruction using deformable 3D Gaussians. Implicit deformable representations commonly model motion with a canonical space and time-dependent backward-warping deformation field. Our method, GauFRe, uses a forward-warping deformation to explicitly model non-rigid transformations of scene geometry. Specifically, we propose a template set of 3D Gaussians residing in a canonical space, and a time-dependent forward-warping deformation field to model dynamic objects. Additionally, we tailor a 3D Gaussian-specific static component supported by an inductive bias-aware initialization approach which allows the deformation field to focus on moving scene regions, improving the rendering of complex real-world motion. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Experiments show our method achieves competitive results and higher efficiency than both previous state-of-the-art NeRF and Gaussian-based methods. For real-world scenes, GauFRe can train in ≈20 mins and offer 96 FPS real-time rendering on an RTX 3090 GPU.

Holographic near-eye displays promise unparalleled depth cues, high-resolution imagery, and realistic three-dimensional parallax at a compact form factor, making them promising candidates for emerging augmented and virtual reality systems. However, existing holographic display methods often assume ideal viewing conditions and overlook real-world factors such as eye floaters and eyelashes—obstructions that can severely degrade perceived image quality. In this work, we propose a new metric that quantifies hologram resilience to artifacts and apply it to computer generated holography (CGH) optimization. We call this Artifact Resilient Holography (ARH). We begin by introducing a simulation method that models the effects of pre- and post-pupil obstructions on holographic displays. Our analysis reveals that eyebox regions dominated by low frequencies—produced especially by the smooth-phase holograms broadly adopted in recent holography work—are vulnerable to visual degradation from dynamic obstructions such as floaters and eyelashes. In contrast, random phase holograms spread energy more uniformly across the eyebox spectrum, enabling them to diffract around obstructions without producing prominent artifacts. By characterizing a random phase eyebox using the Rayleigh Distribution, we derive a differentiable metric in the eyebox domain. We then apply this metric to train a real-time neural network-based phase generator, enabling it to produce artifact-resilient 3D holograms that preserve visual fidelity across a range of practical viewing conditions—enhancing both robustness and user interactivity.

Holographic displays hold the promise of providing authentic depth cues, resulting in enhanced immersive visual experiences for near-eye applications. However, current holographic displays are hindered by speckle noise, which limits accurate reproduction of color and texture in displayed images. We present HoloChrome, a polychromatic holographic display framework designed to mitigate these limitations. HoloChrome utilizes an ultrafast, wavelength-adjustable laser and a dual-Spatial Light Modulator (SLM) architecture, enabling the multiplexing of a large set of discrete wavelengths across the visible spectrum. By leveraging spatial separation in our dual-SLM setup, we independently manipulate speckle patterns across multiple wavelengths. This novel approach effectively reduces speckle noise through incoherent averaging achieved by wavelength multiplexing, specifically by using a single SLM pattern to modulate multiple wavelengths simultaneously on one or more SLM devices. Our method is complementary to existing speckle reduction techniques, offering a new pathway to address this challenge. Furthermore, the use of polychromatic illumination broadens the achievable color gamut compared to traditional three-color primary holographic displays. Our simulations and tabletop experiments validate that HoloChrome significantly reduces speckle noise and expands the color gamut. These advancements enhance the performance of holographic near-eye displays, moving us closer to practical, immersive next-generation visual experiences.

We introduce ReplaceAnything3D model RAM3D, a novel method for 3D object replacement in 3D scenes based on users' text description. Given multi-view images of a scene, a text prompt describing the object to replace, and another describing the new object, our Erase-and-Replace approach can effectively swap objects in 3D scenes with newly generated content while maintaining 3D consistency across multiple viewpoints. We demonstrate the versatility of RAM3D by applying it to various realistic 3D scene types, showcasing results of modified objects that blend in seamlessly with the scene without impacting its overall integrity.

Depth estimation is an important step in many computer vision problems such as 3D reconstruction, novel view synthesis, and computational photography. Most existing work focuses on depth estimation from single frames. When applied to videos, the result lacks temporal consistency, showing flickering and swimming artifacts. In this paper we aim to estimate temporally consistent depth maps of video streams in an online setting. This is a difficult problem as future frames are not available and the method must choose between enforcing consistency and correcting errors from previous estimations. The presence of dynamic objects further complicates the problem. We propose to address these challenges by using a global point cloud that is dynamically updated each frame, along with a learned fusion approach in image space. Our approach encourages consistency while simultaneously allowing updates to handle errors and dynamic objects. Qualitative and quantitative results show that our method achieves state-of-the-art quality for consistent video depth estimation.

The Visual Turing Test is the ultimate goal to evaluate the realism of holographic displays. Previous studies have focused on addressing challenges such as limited étendue and image quality over a large focal volume, but they have not investigated the effect of pupil sampling on the viewing experience in full 3D holograms. In this work, we tackle this problem with a novel hologram generation algorithm motivated by matching the projection operators of incoherent (Light Field) and coherent (Wigner Function) light transport. To this end, we supervise hologram computation using synthesized photographs, which are rendered on-the-fly using Light Field refocusing from stochastically sampled pupil states during optimization. The proposed method produces holograms with correct parallax and focus cues, which are important for passing the Visual Turing Test. We validate that our approach compares favorably to state-of-the-art CGH algorithms that use Light Field and Focal Stack supervision. Our experiments demonstrate that our algorithm improves the viewing experience when evaluated under a large variety of different pupil states.

The task of synthesizing novel views from a single image has useful applications in virtual reality and mobile computing, and a number of approaches to the problem have been proposed in recent years. A Multiplane Image (MPI) estimates the scene as a stack of RGBA layers, and can model complex appearance effects, anti-alias depth errors and synthesize soft edges better than methods that use textured meshes or layered depth images. And unlike neural radiance fields, an MPI can be efficiently rendered on graphics hardware. However, MPIs are highly redundant and require a large number of depth layers to achieve plausible results. Based on the observation that the depth complexity in local image regions is lower than that over the entire image, we split an MPI into many small, tiled regions, each with only a few depth planes. We call this representation a Tiled Multiplane Image (TMPI). We propose a method for generating a TMPI with adaptive depth planes for single-view 3D photography in the wild. Our synthesized results are comparable to state-of-the-art single-view MPI methods while having lower computational overhead.

Stereoscopic, head-tracked display systems can show users realistic, world-locked virtual objects and environments. However, discrepancies between the rendering pipeline and physical viewing conditions can lead to perceived instability in the rendered content resulting in reduced immersion and, potentially, visually-induced motion sickness. Precise requirements to achieve perceptually stable world-locked rendering (WLR) are unknown due to the challenge of constructing a wide field of view, distortion-free display with highly accurate head and eye tracking. We present a system capable of rendering virtual objects over real-world references without perceivable drift under such constraints. This platform is used to study acceptable errors in render camera position for WLR in augmented and virtual reality scenarios, where we find an order of magnitude difference in perceptual sensitivity. We conclude with an analytic model which examines changes to apparent depth and visual direction in response to camera displacement errors.

Tone mapping operators aim to remap content to a display’s dynamic range. Virtual reality is a popular new display modality that has significant differences from other media, making the use of traditional tone mapping techniques difficult. Moreover, real-time adaptive estimation of tone curves that faithfully maintain appearance remains a significant challenge. In this work, we propose a real-time perceptual contrast-matching framework, that allows us to optimally remap scenes for target displays. Our framework is optimized for efficiency and runs on a mobile Quest 2 headset in under 1ms per frame. A subjective study on an HDR-VR prototype demonstrates our method’s effectiveness across a wide range of display luminances, producing imagery that is preferred to alternatives tone mapped at peak luminances an order of magnitude higher. This result highlights the importance of good tone mapping for visual quality in VR.

Holographic displays promise several benefits including high quality 3D imagery, accurate accommodation cues, and compact form-factors. However, holography relies on coherent illumination which can create undesirable speckle noise in the final image. Although smooth phase holograms can be speckle-free, their non-uniform eyebox makes them impractical, and speckle mitigation with partially coherent sources also reduces resolution. Averaging sequential frames for speckle reduction requires high speed modulators and consumes temporal bandwidth that may be needed elsewhere in the system. In this work, we propose multisource holography, a novel architecture that uses an array of sources to suppress speckle in a single frame without sacrificing resolution. By using two spatial light modulators, arranged sequentially, each source in the array can be controlled almost independently to create a version of the target content with different speckle. Speckle is then suppressed when the contributions from the multiple sources are averaged at the image plane. We introduce an algorithm to calculate multisource holograms, analyze the design space, and demonstrate up to a 10 dB increase in peak signal-to-noise ratio compared to an equivalent single source system. Finally, we validate the concept with a benchtop experimental prototype by producing both 2D images and focal stacks with natural defocus cues.

As virtual reality (VR) headsets continue to achieve ever more immersive visuals along the axes of resolution, field of view, focal cues, distortion mitigation, and so on, the luminance and dynamic range of these devices falls far short of widely available consumer televisions. While work remains to be done on the display architecture side, power and weight limitations in head-mounted displays pose a challenge for designs aiming for high luminance. In this paper, we seek to gain a basic understanding of VR user preferences for display luminance values in relation to known, real-world luminances for immersive, natural scenes. To do so, we analyze the luminance characteristics of an existing high-dynamic-range (HDR) panoramic image dataset, build an HDR VR headset capable of reproducing over 20,000 nits peak luminance, and conduct a first-of-its-kind study on user brightness preferences in VR. We conclude that current commercial VR headsets do not meet user preferences for display luminance, even for indoor scenes.

Holographic displays promise to deliver unprecedented display capabilities in augmented reality applications, featuring a wide field of view, wide color gamut, spatial resolution, and depth cues all in a compact form factor. While emerging holographic display approaches have been successful in achieving large étendue and high image quality as seen by a camera, the large étendue also reveals a problem that makes existing displays impractical: the sampling of the holographic field by the eye pupil. Existing methods have not investigated this issue due to the lack of displays with large enough étendue, and, as such, they suffer from severe artifacts with varying eye pupil size and location. We show that the holographic field as sampled by the eye pupil is highly varying for existing display setups, and we propose pupil-aware holography that maximizes the perceptual image quality irrespective of the size, location, and orientation of the eye pupil in a near-eye holographic display. We validate the proposed approach both in simulations and on a prototype holographic display and show that our method eliminates severe artifacts and significantly outperforms existing approaches.

As virtual reality (VR) devices become increasingly commonplace, asymmetric interactions between people with and without headsets are becoming more frequent. Existing video pass-through VR headsets solve one side of these asymmetric interactions by showing the user a live reconstruction of the outside world. This paper further advocates for reverse pass-through VR , wherein a three-dimensional view of the user's face and eyes is presented to any number of outside viewers in a perspective-correct manner using a light field display. Tying together research in social telepresence and copresence, autostereoscopic displays, and facial capture, reverse pass-through VR enables natural eye contact and other important non-verbal cues in a wider range of interaction scenarios, providing a path to potentially increase the utility and social acceptability of VR headsets in shared and public spaces.

Holographic optical elements (HOEs) have a wide range of applications, including their emerging use in virtual and augmented reality displays, but their design and fabrication have remained largely limited to configurations using simple wavefronts. In this paper, we present a pipeline for the design, optimization, and fabrication of complex, customized HOEs that enhances their imaging performance and enables new applications. In particular, we propose an optimization method for grating vector fields that accounts for the unique selectivity properties of HOEs. We further show how our pipeline can be applied to two distinct HOE fabrication methods. The first uses a pair of freeform refractive elements to manufacture HOEs with high optical quality and precision. The second uses a holographic printer with two wavefront-modulating arms, enabling rapid prototyping. We propose a unified wavefront decomposition framework suitable for both fabrication approaches. To demonstrate the versatility of these methods, we fabricate and characterize a series of specialized HOEs, including an aspheric lens, a head-up display lens, a lens array, and, for the first time, a full-color caustic projection element.

Due to higher resolutions and refresh rates, as well as more photorealistic effects, real-time rendering has become increasingly challenging for video games and emerging virtual reality headsets. To meet this demand, modern graphics hardware and game engines often reduce the computational cost by rendering at a lower resolution and then upsampling to the native resolution. Following the recent advances in image and video superresolution in computer vision, we propose a machine learning approach that is specifically tailored for high-quality upsampling of rendered content in real-time applications. The main insight of our work is that in rendered content, the image pixels are point-sampled, but precise temporal dynamics are available. Our method combines this specific information that is typically available in modern renderers (i.e., depth and dense motion vectors) with a novel temporal network design that takes into account such specifics and is aimed at maximizing video quality while delivering real-time performance. By training on a large synthetic dataset rendered from multiple 3D scenes with recorded camera motion, we demonstrate high fidelity and temporally stable results in real-time, even in the highly challenging 4 × 4 upsampling scenario, significantly outperforming existing superresolution and temporal antialiasing work.

Addressing vergence-accommodation conflict in head-mounted displays (HMDs) requires resolving two interrelated problems. First, the hardware must support viewing sharp imagery over the full accommodation range of the user. Second, HMDs should accurately reproduce retinal defocus blur to correctly drive accommodation. A multitude of accommodation-supporting HMDs have been proposed, with three architectures receiving particular attention: varifocal, multifocal, and light field displays. These designs all extend depth of focus, but rely on computationally expensive rendering and optimization algorithms to reproduce accurate defocus blur (often limiting content complexity and interactive applications). To date, no unified framework has been proposed to support driving these emerging HMDs using commodity content. In this paper, we introduce DeepFocus , a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

Conventional binocular head-mounted displays (HMDs) vary the stimulus to vergence with the information in the picture, while the stimulus to accommodation remains fixed at the apparent distance of the display, as created by the viewing optics. Sustained vergence-accommodation conflict (VAC) has been associated with visual discomfort, motivating numerous proposals for delivering near-correct accommodation cues. We introduce focal surface displays to meet this challenge, augmenting conventional HMDs with a phase-only spatial light modulator (SLM) placed between the display screen and viewing optics. This SLM acts as a dynamic freeform lens, shaping synthesized focal surfaces to conform to the virtual scene geometry. We introduce a framework to decompose target focal stacks and depth maps into one or more pairs of piecewise smooth focal surfaces and underlying display images. We build on recent developments in "optimized blending" to implement a multifocal display that allows the accurate depiction of occluding, semi-transparent, and reflective objects. Practical benefits over prior accommodation-supporting HMDs are demonstrated using a binocular focal surface display employing a liquid crystal on silicon (LCOS) phase SLM and an organic light-emitting diode (OLED) display.

As head-mounted displays (HMDs) commonly present a single, fixed-focus display plane, a conflict can be created between the vergence and accommodation responses of the viewer. Multifocal HMDs have long been investigated as a potential solution in which multiple image planes span the viewer's accommodation range. Such displays require a scene decomposition algorithm to distribute the depiction of objects across image planes, and previous work has shown that simple decompositions can be achieved in real-time. However, recent optimal decompositions further improve image quality, particularly with complex content. Such decompositions are more computationally involved and likely require better alignment of the image planes with the viewer's eyes, which are potential barriers to practical applications. Our goal is to enable interactive optimal decomposition algorithms capable of driving a vergence- and accommodation-tracked multifocal testbed. Ultimately, such a testbed is necessary to establish the requirements for the practical use of multifocal displays, in terms of computational demand and hardware accuracy. To this end, we present an efficient algorithm for optimal decompositions, incorporating insights from vision science. Our method is amenable to GPU implementations and achieves a three-orders-of-magnitude speedup over previous work. We further show that eye tracking can be used for adequate plane alignment with efficient image-based deformations, adjusting for both eye rotation and head movement relative to the display. We also build the first binocular multifocal testbed with integrated eye tracking and accommodation measurement, paving the way to establish practical eye tracking and rendering requirements for this promising class of display. Finally, we report preliminary results from a pilot user study utilizing our testbed, investigating the accommodation response of users to dynamic stimuli presented under optimal decomposition.

We present a novel design for an optical see-through augmented reality display that offers a wide field of view and supports a compact form factor approaching ordinary eyeglasses. Instead of conventional optics, our design uses only two simple hardware components: an LCD panel and an array of point light sources (implemented as an edge-lit, etched acrylic sheet) placed directly in front of the eye, out of focus. We code the point light sources through the LCD to form miniature see-through projectors. A virtual aperture encoded on the LCD allows the projectors to be tiled, creating an arbitrarily wide field of view. Software rearranges the target augmented image into tiled sub-images sent to the display, which appear as the correct image when observed out of the viewer's accommodation range. We evaluate the design space of tiled point light projectors with an emphasis on increasing spatial resolution through the use of eye tracking. We demonstrate feasibility through software simulations and a real-time prototype display that offers a 110° diagonal field of view in the form factor of large glasses and discuss remaining challenges to constructing a practical display.

We demonstrate that layered spatial light modulators (SLMs), subject to fixed lateral displacements and refreshed at staggered intervals, can synthesize images with greater spatiotemporal resolution than that afforded by any single SLM used in their construction. Dubbed cascaded displays , such architectures enable superresolution flat panel displays (e.g., using thin stacks of liquid crystal displays (LCDs)) and digital projectors (e.g., relaying the image of one SLM onto another). We introduce a comprehensive optimization framework, leveraging non-negative matrix and tensor factorization, that decomposes target images and videos into multi-layered, time-multiplexed attenuation patterns---offering a flexible trade-off between apparent image brightness, spatial resolution, and refresh rate. Through this analysis, we develop a real-time dual-layer factorization method that quadruples spatial resolution and doubles refresh rate. Compared to prior superresolution displays, cascaded displays place fewer restrictions on the hardware, offering thin designs without moving parts or the necessity of temporal multiplexing. Furthermore, cascaded displays are the first use of multi-layer displays to increase apparent temporal resolution. We validate these concepts using two custom-built prototypes: a dual-layer LCD and a dual-modulation liquid crystal on silicon (LCoS) projector, with the former emphasizing head-mounted display (HMD) applications.

We propose near-eye light field displays that enable thin, lightweight head-mounted displays (HMDs) capable of presenting nearly correct convergence, accommodation, binocular disparity, and retinal defocus depth cues. Sharp images are depicted by out-of-focus elements by synthesizing light fields corresponding to virtual objects within a viewer's natural accommodation range. We formally assess the capabilities of microlens arrays to achieve practical near-eye light field displays. Building on concepts shared with existing integral imaging displays and light field cameras, we optimize performance in the context of near-eye viewing. We establish fundamental trade-offs between the quantitative parameters of resolution, field of view, and depth of field, as well as the ergonomic parameters of form factor and ranges of allowed eye movement. As with light field cameras, our design supports continuous accommodation of the eye throughout a finite depth of field; as a result, binocular configurations provide a means to address the accommodation-convergence conflict occurring with existing stereoscopic displays. We construct a complete prototype display system, comprising: a custom-fabricated HMD using modified off-the-shelf parts and real-time, GPU-accelerated light field renderers (including a general ray tracing method and a "backward compatible" rasterization method supporting existing stereoscopic content). Through simulations and experiments, we motivate near-eye light field displays as thin, lightweight alternatives to conventional near-eye displays.

We present a glasses-free 3D display design with the potential to provide viewers with nearly correct accommodative depth cues, as well as motion parallax and binocular cues. Building on multilayer attenuator and directional backlight architectures, the proposed design achieves the high angular resolution needed for accommodation by placing spatial light modulators about a large lens: one conjugate to the viewer's eye, and one or more near the plane of the lens. Nonnegative tensor factorization is used to compress a high angular resolution light field into a set of masks that can be displayed on a pair of commodity LCD panels. By constraining the tensor factorization to preserve only those light rays seen by the viewer, we effectively steer narrow high-resolution viewing cones into the user's eyes, allowing binocular disparity, motion parallax, and the potential for nearly correct accommodation over a wide field of view. We verify the design experimentally by focusing a camera at different depths about a prototype display, establish formal upper bounds on the design's accommodation range and diffraction-limited performance, and discuss practical limitations that must be overcome to allow the device to be used with human observers.

Optical aberrations of the human eye are currently corrected using eyeglasses, contact lenses, or surgery. We describe a fourth option: modifying the composition of displayed content such that the perceived image appears in focus, after passing through an eye with known optical defects. Prior approaches synthesize pre-filtered images by deconvolving the content by the point spread function of the aberrated eye. Such methods have not led to practical applications, due to severely reduced contrast and ringing artifacts. We address these limitations by introducing multilayer pre-filtering, implemented using stacks of semi-transparent, light-emitting layers. By optimizing the layer positions and the partition of spatial frequencies between layers, contrast is improved and ringing artifacts are eliminated. We assess design constraints for multilayer displays; autostereoscopic light field displays are identified as a preferred, thin form factor architecture, allowing synthetic layers to be displaced in response to viewer movement and refractive errors. We assess the benefits of multilayer pre-filtering versus prior light field pre-distortion methods, showing pre-filtering works within the constraints of current display resolutions. We conclude by analyzing benefits and limitations using a prototype multilayer LCD.

We introduce tensor displays: a family of compressive light field displays comprising all architectures employing a stack of time-multiplexed, light-attenuating layers illuminated by uniform or directional backlighting (i.e., any low-resolution light field emitter). We show that the light field emitted by an N -layer, M -frame tensor display can be represented by an N th -order, rank- M tensor. Using this representation we introduce a unified optimization framework, based on nonnegative tensor factorization (NTF), encompassing all tensor display architectures. This framework is the first to allow joint multilayer, multiframe light field decompositions, significantly reducing artifacts observed with prior multilayer-only and multiframe-only decompositions; it is also the first optimization method for designs combining multiple layers with directional backlighting. We verify the benefits and limitations of tensor displays by constructing a prototype using modified LCD panels and a custom integral imaging backlight. Our efficient, GPU-based NTF implementation enables interactive applications. Through simulations and experiments we show that tensor displays reveal practical architectures with greater depths of field, wider fields of view, and thinner form factors, compared to prior automultiscopic displays.

Abstract The plenoptic function is a ray‐based model for light that includes the colour spectrum as well as spatial, temporal and directional variation. Although digital light sensors have greatly evolved in the last years, one fundamental limitation remains: all standard CCD and CMOS sensors integrate over the dimensions of the plenoptic function as they convert photons into electrons; in the process, all visual information is irreversibly lost, except for a two‐dimensional, spatially varying subset—the common photograph. In this state‐of‐the‐art report, we review approaches that optically encode the dimensions of the plenoptic function transcending those captured by traditional photography and reconstruct the recorded information computationally.

We introduce polarization field displays as an optically-efficient design for dynamic light field display using multi-layered LCDs. Such displays consist of a stacked set of liquid crystal panels with a single pair of crossed linear polarizers. Each layer is modeled as a spatially-controllable polarization rotator, as opposed to a conventional spatial light modulator that directly attenuates light. Color display is achieved using field sequential color illumination with monochromatic LCDs, mitigating severe attenuation and moiré occurring with layered color filter arrays. We demonstrate such displays can be controlled, at interactive refresh rates, by adopting the SART algorithm to tomographically solve for the optimal spatially-varying polarization state rotations applied by each layer. We validate our design by constructing a prototype using modified off-the-shelf panels. We demonstrate interactive display using a GPU-based SART implementation supporting both polarization-based and attenuation-based architectures. Experiments characterize the accuracy of our image formation model, verifying polarization field displays achieve increased brightness, higher resolution, and extended depth of field, as compared to existing automultiscopic display methods for dual-layer and multi-layer LCDs.

We develop tomographic techniques for image synthesis on displays composed of compact volumes of light-attenuating material. Such volumetric attenuators recreate a 4D light field or high-contrast 2D image when illuminated by a uniform backlight. Since arbitrary oblique views may be inconsistent with any single attenuator, iterative tomographic reconstruction minimizes the difference between the emitted and target light fields, subject to physical constraints on attenuation. As multi-layer generalizations of conventional parallax barriers, such displays are shown, both by theory and experiment, to exceed the performance of existing dual-layer architectures. For 3D display, spatial resolution, depth of field, and brightness are increased, compared to parallax barriers. For a plane at a fixed depth, our optimization also allows optimal construction of high dynamic range displays, confirming existing heuristics and providing the first extension to multiple, disjoint layers. We conclude by demonstrating the benefits and limitations of attenuation-based light field displays using an inexpensive fabrication method: separating multiple printed transparencies with acrylic sheets.

Commercial stereoscopic displays have reemerged in the consumer market within the last few years. Today, film studios routinely produce live-action and animated 3D content for theatrical release. While primarily enabled by the widespread adoption of digital projection, allowing accurate view synchronization, the underlying 3D display technologies have changed little in the last few decades. Theatrical systems rely on stereoscopic display: projecting a unique left/right image pair that is separated by various filters placed within a pair of glasses worn by each viewer. In contrast, several LCD manufactures are introducing automultiscopic displays: allowing view-dependent imagery to be perceived without the aide of special glasses. Given these developments, 3D display is poised for another resurgence.

We optimize automultiscopic displays built by stacking a pair of modified LCD panels. To date, such dual-stacked LCDs have used heuristic parallax barriers for view-dependent imagery: the front LCD shows a fixed array of slits or pinholes, independent of the multi-view content. While prior works adapt the spacing between slits or pinholes, depending on viewer position, we show both layers can also be adapted to the multi-view content, increasing brightness and refresh rate. Unlike conventional barriers, both masks are allowed to exhibit non-binary opacities. It is shown that any 4D light field emitted by a dual-stacked LCD is the tensor product of two 2D masks. Thus, any pair of 1D masks only achieves a rank-1 approximation of a 2D light field. Temporal multiplexing of masks is shown to achieve higher-rank approximations. Non-negative matrix factorization (NMF) minimizes the weighted Euclidean distance between a target light field and that emitted by the display. Simulations and experiments characterize the resultingcontent-adaptive parallax barriersfor low-rank light field approximation.

We transform an LCD into a display that supports both 2D multi-touch and unencumbered 3D gestures. Our BiDirectional (BiDi) screen, capable of both image capture and display, is inspired by emerging LCDs that use embedded optical sensors to detect multiple points of contact. Our key contribution is to exploit the spatial light modulation capability of LCDs to allow lensless imaging without interfering with display functionality. We switch between a display mode showing traditional graphics and a capture mode in which the backlight is disabled and the LCD displays a pinhole array or an equivalent tiled-broadband code. A large-format image sensor is placed slightly behind the liquid crystal layer. Together, the image sensor and LCD form a mask-based light field camera, capturing an array of images equivalent to that produced by a camera array spanning the display surface. The recovered multi-view orthographic imagery is used to passively estimate the depth of scene points. Two motivating applications are described: a hybrid touch plus gesture interaction and a light-gun mode for interacting with external light-emitting widgets. We show a working prototype that simulates the image sensor with a camera and diffuser, allowing interaction up to 50 cm in front of a modified 20.1 inch LCD.

We describe a unified representation of occluders in light transport and photography using shield fields: the 4D attenuation function which acts on any light field incident on an occluder. Our key theoretical result is that shield fields can be used to decouple the effects of occluders and incident illumination. We first describe the properties of shield fields in the frequency-domain and briefly analyze the "forward" problem of efficiently computing cast shadows. Afterwards, we apply the shield field signal-processing framework to make several new observations regarding the "inverse" problem of reconstructing 3D occluders from cast shadows -- extending previous work on shape-from-silhouette and visual hull methods. From this analysis we develop the first single-camera, single-shot approach to capture visual hulls without requiring moving or programmable illumination. We analyze several competing camera designs, ultimately leading to the development of a new large-format, mask-based light field camera that exploits optimal tiled-broadband codes for light-efficient shield field capture. We conclude by presenting a detailed experimental analysis of shield field capture and 3D occluder reconstruction.