Perceptual rasterization for head-mounted display image synthesis

We suggest a rasterization pipeline tailored towards the needs of HMDs, where latency and field-of-view requirements pose new challenges beyond those of traditional desktop displays. Instead of image warping for low latency, or using multiple passes for foveation, we show how both can be produced directly in a single perceptual rasterization pass. We do this with per-fragment ray-casting. This is enabled by derivations of tight space-time-fovea pixel bounds, introducing just enough flexibility for the requisite geometric tests, but retaining most of the simplicity and efficiency of the traditional rasterizaton pipeline. To produce foveated images, we rasterize to an image with spatially varying pixel density. To compensate for latency, we extend the image formation model to directly produce "rolling" images where the time at each pixel depends on its display location. Our approach overcomes limitations of warping with respect to disocclusions, object motion and view-dependent shading, as well as geometric aliasing artifacts in other foveated rendering techniques. A set of perceptual user studies demonstrates the efficacy of our approach

Low-Latency Rendering With Dataflow Architectures

Recent years have seen a resurgence of virtual reality (VR), sparked by the repurposing of low-cost COTS components. VR aims to generate stimuli that appear to come from a source other than the interface through which they are delivered. The synthetic stimuli replace real-world stimuli, and transport the user to another, perhaps imaginary, “place.” To do this, we must overcome many challenges, often related to matching the synthetic stimuli to the expectations and behavior of the real world. One way in which the stimuli can fail is its latency–– the time between a user's action and the computer's response. We constructed a novel VR renderer, that optimized latency above all else. Our prototype allowed us to explore how latency affects human–computer interaction. We had to completely reconsider the interaction between time, space, and synchronization on displays and in the traditional graphics pipeline. Using a specialized architecture––dataflow computing––we combined consumer, industrial, and prototype components to create an integrated 1:1 room-scale VR system with a latency of under 3 ms. While this was prototype hardware, the considerations in achieving this performance inform the design of future VR pipelines, and our human factors studies have provided new and sometimes surprising contributions to the body of knowledge on latency in HCI

Foveated Path Tracing with Fast Reconstruction and Efficient Sample Distribution

Polunseuranta on tietokonegrafiikan piirtotekniikka, jota on käytetty pääasiassa ei-reaaliaikaisen realistisen piirron tekemiseen. Polunseuranta tukee luonnostaan monia muilla tekniikoilla vaikeasti saavutettavia todellisen valon ilmiöitä kuten heijastuksia ja taittumista. Reaaliaikainen polunseuranta on hankalaa polunseurannan suuren laskentavaatimuksen takia. Siksi nykyiset reaaliaikaiset polunseurantasysteemi tuottavat erittäin kohinaisia kuvia, jotka tyypillisesti suodatetaan jälkikäsittelykohinanpoisto-suodattimilla. Erittäin immersiivisiä käyttäjäkokemuksia voitaisiin luoda polunseurannalla, joka täyttäisi laajennetun todellisuuden vaatimukset suuresta resoluutiosta riittävän matalassa vasteajassa. Yksi mahdollinen ratkaisu näiden vaatimusten täyttämiseen voisi olla katsekeskeinen polunseuranta, jossa piirron resoluutiota vähennetään katseen reunoilla. Tämän johdosta piirron laatu on katseen reunoilla sekä harvaa että kohinaista, mikä asettaa suuren roolin lopullisen kuvan koostavalle suodattimelle. Tässä työssä esitellään ensimmäinen reaaliajassa toimiva regressionsuodatin. Suodatin on suunniteltu kohinaisille kuville, joissa on yksi polunseurantanäyte pikseliä kohden. Nopea suoritus saavutetaan tiileissä käsittelemällä ja nopealla sovituksen toteutuksella. Lisäksi työssä esitellään Visual-Polar koordinaattiavaruus, joka jakaa polunseurantanäytteet siten, että niiden jakauma seuraa silmän herkkyysmallia. Visual-Polar-avaruuden etu muihin tekniikoiden nähden on että se vähentää työmäärää sekä polunseurannassa että suotimessa. Nämä tekniikat esittelevät toimivan prototyypin katsekeskeisestä polunseurannasta, ja saattavat toimia tienraivaajina laajamittaiselle realistisen reaaliaikaisen polunseurannan käyttöönotolle.Photo-realistic offline rendering is currently done with path tracing, because it naturally produces many real-life light effects such as reflections, refractions and caustics. These effects are hard to achieve with other rendering techniques. However, path tracing in real time is complicated due to its high computational demand. Therefore, current real-time path tracing systems can only generate very noisy estimate of the final frame, which is then denoised with a post-processing reconstruction filter. A path tracing-based rendering system capable of filling the high resolution in the low latency requirements of mixed reality devices would generate a very immersive user experience. One possible solution for fulfilling these requirements could be foveated path tracing, wherein the rendering resolution is reduced in the periphery of the human visual system. The key challenge is that the foveated path tracing in the periphery is both sparse and noisy, placing high demands on the reconstruction filter. This thesis proposes the first regression-based reconstruction filter for path tracing that runs in real time. The filter is designed for highly noisy one sample per pixel inputs. The fast execution is accomplished with blockwise processing and fast implementation of the regression. In addition, a novel Visual-Polar coordinate space which distributes the samples according to the contrast sensitivity model of the human visual system is proposed. The specialty of Visual-Polar space is that it reduces both path tracing and reconstruction work because both of them can be done with smaller resolution. These techniques enable a working prototype of a foveated path tracing system and may work as a stepping stone towards wider commercial adoption of photo-realistic real-time path tracing

A Quality-Centered Analysis of Eye Tracking Data in Foveated Rendering

This work presents the analysis of data recorded by an eye tracking device in the course of evaluating a foveated rendering approach for head-mounted displays (HMDs). Foveated ren- dering methods adapt the image synthesis process to the user’s gaze and exploiting the human visual system’s limitations to increase rendering performance. Especially, foveated rendering has great potential when certain requirements have to be fulfilled, like low-latency rendering to cope with high display refresh rates. This is crucial for virtual reality (VR), as a high level of immersion, which can only be achieved with high rendering performance and also helps to reduce nausea, is an important factor in this field. We put things in context by first providing basic information about our rendering system, followed by a description of the user study and the collected data. This data stems from fixation tasks that subjects had to perform while being shown fly-through sequences of virtual scenes on an HMD. These fixation tasks consisted of a combination of various scenes and fixation modes. Besides static fixation targets, moving tar- gets on randomized paths as well as a free focus mode were tested. Using this data, we estimate the precision of the utilized eye tracker and analyze the participants’ accuracy in focusing the displayed fixation targets. Here, we also take a look at eccentricity-dependent quality ratings. Comparing this information with the users’ quality ratings given for the displayed sequences then reveals an interesting connection between fixation modes, fixation accuracy and quality ratings

State of the Art on Neural Rendering

Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. This state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems

Reproducing reality with a high-dynamic-range multi-focal stereo display

With well-established methods for producing photo-realistic results, the next big challenge of graphics and display technologies is to achieve perceptual realism --- producing imagery indistinguishable from real-world 3D scenes. To deliver all necessary visual cues for perceptual realism, we built a High-Dynamic-Range Multi-Focal Stereo Display that achieves high resolution, accurate color, a wide dynamic range, and most depth cues, including binocular presentation and a range of focal depth. The display and associated imaging system have been designed to capture and reproduce a small near-eye three-dimensional object and to allow for a direct comparison between virtual and real scenes. To assess our reproduction of realism and demonstrate the capability of the display and imaging system, we conducted an experiment in which the participants were asked to discriminate between a virtual object and its physical counterpart. Our results indicate that the participants can only detect the discrepancy with a probability of 0.44. With such a level of perceptual realism, our display apparatus can facilitate a range of visual experiments that require the highest fidelity of reproduction while allowing for the full control of the displayed stimuli.</jats:p

Foveated real-time ray tracing for head-mounted displays

Head-mounted displays with dense pixel arrays used for virtual reality applications require high frame rates and low latency rendering. This forms a challenging use case for any rendering approach. In addition to its ability of generating realistic images, ray tracing offers a number of distinct advantages, but has been held back mainly by its performance. In this paper, we present an approach that significantly improves image generation performance of ray tracing. This is done by combining foveated rendering based on eye tracking with reprojection rendering using previous frames in order to drastically reduce the number of new image samples per frame. To reproject samples a coarse geometry is reconstructed from a G-Buffer. Possible errors introduced by this reprojection as well as parts that are critical to the perception are scheduled for resampling. Additionally, a coarse color buffer is used to provide an initial image, refined smoothly by more samples were needed. Evaluations and user tests show that our method achieves real-time frame rates, while visual differences compared to fully rendered images are hardly perceivable. As a result, we can ray trace non-trivial static scenes for the Oculus DK2 HMD at 1182 × 1464 per eye within the the VSync limits without perceived visual differences.We would like to thank NVIDIA for providing us with two Quadro K6000 graphics cards as well as Intel Visual Computing Institute, the European Union (EU) for the co-funding as part of the Dreamspace and the FIWARE projects and the German Federal Ministry for Economic Affairs and Energy (BMWi) for funding the MATEDIS ZIM-project (grant no KF2644109)

Blickpunktabhängige Computergraphik

Contemporary digital displays feature multi-million pixels at ever-increasing refresh rates. Reality, on the other hand, provides us with a view of the world that is continuous in space and time. The discrepancy between viewing the physical world and its sampled depiction on digital displays gives rise to perceptual quality degradations. By measuring or estimating where we look, gaze-contingent algorithms aim at exploiting the way we visually perceive to remedy visible artifacts. This dissertation presents a variety of novel gaze-contingent algorithms and respective perceptual studies. Chapter 4 and 5 present methods to boost perceived visual quality of conventional video footage when viewed on commodity monitors or projectors. In Chapter 6 a novel head-mounted display with real-time gaze tracking is described. The device enables a large variety of applications in the context of Virtual Reality and Augmented Reality. Using the gaze-tracking VR headset, a novel gaze-contingent render method is described in Chapter 7. The gaze-aware approach greatly reduces computational efforts for shading virtual worlds. The described methods and studies show that gaze-contingent algorithms are able to improve the quality of displayed images and videos or reduce the computational effort for image generation, while display quality perceived by the user does not change.Moderne digitale Bildschirme ermöglichen immer höhere Auflösungen bei ebenfalls steigenden Bildwiederholraten. Die Realität hingegen ist in Raum und Zeit kontinuierlich. Diese Grundverschiedenheit führt beim Betrachter zu perzeptuellen Unterschieden. Die Verfolgung der Aug-Blickrichtung ermöglicht blickpunktabhängige Darstellungsmethoden, die sichtbare Artefakte verhindern können. Diese Dissertation trägt zu vier Bereichen blickpunktabhängiger und wahrnehmungstreuer Darstellungsmethoden bei. Die Verfahren in Kapitel 4 und 5 haben zum Ziel, die wahrgenommene visuelle Qualität von Videos für den Betrachter zu erhöhen, wobei die Videos auf gewöhnlicher Ausgabehardware wie z.B. einem Fernseher oder Projektor dargestellt werden. Kapitel 6 beschreibt die Entwicklung eines neuartigen Head-mounted Displays mit Unterstützung zur Erfassung der Blickrichtung in Echtzeit. Die Kombination der Funktionen ermöglicht eine Reihe interessanter Anwendungen in Bezug auf Virtuelle Realität (VR) und Erweiterte Realität (AR). Das vierte und abschließende Verfahren in Kapitel 7 dieser Dissertation beschreibt einen neuen Algorithmus, der das entwickelte Eye-Tracking Head-mounted Display zum blickpunktabhängigen Rendern nutzt. Die Qualität des Shadings wird hierbei auf Basis eines Wahrnehmungsmodells für jeden Bildpixel in Echtzeit analysiert und angepasst. Das Verfahren hat das Potenzial den Berechnungsaufwand für das Shading einer virtuellen Szene auf ein Bruchteil zu reduzieren. Die in dieser Dissertation beschriebenen Verfahren und Untersuchungen zeigen, dass blickpunktabhängige Algorithmen die Darstellungsqualität von Bildern und Videos wirksam verbessern können, beziehungsweise sich bei gleichbleibender Bildqualität der Berechnungsaufwand des bildgebenden Verfahrens erheblich verringern lässt

Bringing Telepresence to Every Desk

In this paper, we work to bring telepresence to every desktop. Unlike commercial systems, personal 3D video conferencing systems must render high-quality videos while remaining financially and computationally viable for the average consumer. To this end, we introduce a capturing and rendering system that only requires 4 consumer-grade RGBD cameras and synthesizes high-quality free-viewpoint videos of users as well as their environments. Experimental results show that our system renders high-quality free-viewpoint videos without using object templates or heavy pre-processing. While not real-time, our system is fast and does not require per-video optimizations. Moreover, our system is robust to complex hand gestures and clothing, and it can generalize to new users. This work provides a strong basis for further optimization, and it will help bring telepresence to every desk in the near future. The code and dataset will be made available on our website https://mcmvmc.github.io/PersonalTelepresence/