3D-TV Production from Conventional Cameras for Sports Broadcast

3DTV production of live sports events presents a challenging problem involving conflicting requirements of main- taining broadcast stereo picture quality with practical problems in developing robust systems for cost effective deployment. In this paper we propose an alternative approach to stereo production in sports events using the conventional monocular broadcast cameras for 3D reconstruction of the event and subsequent stereo rendering. This approach has the potential advantage over stereo camera rigs of recovering full scene depth, allowing inter-ocular distance and convergence to be adapted according to the requirements of the target display and enabling stereo coverage from both existing and ‘virtual’ camera positions without additional cameras. A prototype system is presented with results of sports TV production trials for rendering of stereo and free-viewpoint video sequences of soccer and rugby

Efficient image-based rendering

Recent advancements in real-time ray tracing and deep learning have significantly enhanced the realism of computer-generated images. However, conventional 3D computer graphics (CG) can still be time-consuming and resource-intensive, particularly when creating photo-realistic simulations of complex or animated scenes. Image-based rendering (IBR) has emerged as an alternative approach that utilizes pre-captured images from the real world to generate realistic images in real-time, eliminating the need for extensive modeling. Although IBR has its advantages, it faces challenges in providing the same level of control over scene attributes as traditional CG pipelines and accurately reproducing complex scenes and objects with different materials, such as transparent objects. This thesis endeavors to address these issues by harnessing the power of deep learning and incorporating the fundamental principles of graphics and physical-based rendering. It offers an efficient solution that enables interactive manipulation of real-world dynamic scenes captured from sparse views, lighting positions, and times, as well as a physically-based approach that facilitates accurate reproduction of the view dependency effect resulting from the interaction between transparent objects and their surrounding environment. Additionally, this thesis develops a visibility metric that can identify artifacts in the reconstructed IBR images without observing the reference image, thereby contributing to the design of an effective IBR acquisition pipeline. Lastly, a perception-driven rendering technique is developed to provide high-fidelity visual content in virtual reality displays while retaining computational efficiency.Jüngste Fortschritte im Bereich Echtzeit-Raytracing und Deep Learning haben den Realismus computergenerierter Bilder erheblich verbessert. Konventionelle 3DComputergrafik (CG) kann jedoch nach wie vor zeit- und ressourcenintensiv sein, insbesondere bei der Erstellung fotorealistischer Simulationen von komplexen oder animierten Szenen. Das bildbasierte Rendering (IBR) hat sich als alternativer Ansatz herauskristallisiert, bei dem vorab aufgenommene Bilder aus der realen Welt verwendet werden, um realistische Bilder in Echtzeit zu erzeugen, so dass keine umfangreiche Modellierung erforderlich ist. Obwohl IBR seine Vorteile hat, ist es eine Herausforderung, das gleiche Maß an Kontrolle über Szenenattribute zu bieten wie traditionelle CG-Pipelines und komplexe Szenen und Objekte mit unterschiedlichen Materialien, wie z.B. transparente Objekte, akkurat wiederzugeben. In dieser Arbeit wird versucht, diese Probleme zu lösen, indem die Möglichkeiten des Deep Learning genutzt und die grundlegenden Prinzipien der Grafik und des physikalisch basierten Renderings einbezogen werden. Sie bietet eine effiziente Lösung, die eine interaktive Manipulation von dynamischen Szenen aus der realen Welt ermöglicht, die aus spärlichen Ansichten, Beleuchtungspositionen und Zeiten erfasst wurden, sowie einen physikalisch basierten Ansatz, der eine genaue Reproduktion des Effekts der Sichtabhängigkeit ermöglicht, der sich aus der Interaktion zwischen transparenten Objekten und ihrer Umgebung ergibt. Darüber hinaus wird in dieser Arbeit eine Sichtbarkeitsmetrik entwickelt, mit der Artefakte in den rekonstruierten IBR-Bildern identifiziert werden können, ohne das Referenzbild zu betrachten, und die somit zur Entwicklung einer effektiven IBR-Erfassungspipeline beiträgt. Schließlich wird ein wahrnehmungsgesteuertes Rendering-Verfahren entwickelt, um visuelle Inhalte in Virtual-Reality-Displays mit hoherWiedergabetreue zu liefern und gleichzeitig die Rechenleistung zu erhalten

INTERMEDIATE VIEW RECONSTRUCTION FOR MULTISCOPIC 3D DISPLAY

This thesis focuses on Intermediate View Reconstruction (IVR) which generates additional images from the available stereo images. The main application of IVR is to generate the content of multiscopic 3D displays, and it can be applied to generate different viewpoints to Free-viewpoint TV (FTV). Although IVR is considered a good approach to generate additional images, there are some problems with the reconstruction process, such as detecting and handling the occlusion areas, preserving the discontinuity at edges, and reducing image artifices through formation of the texture of the intermediate image. The occlusion area is defined as the visibility of such an area in one image and its disappearance in the other one. Solving IVR problems is considered a significant challenge for researchers. In this thesis, several novel algorithms have been specifically designed to solve IVR challenges by employing them in a highly robust intermediate view reconstruction algorithm. Computer simulation and experimental results confirm the importance of occluded areas in IVR. Therefore, we propose a novel occlusion detection algorithm and another novel algorithm to Inpaint those areas. Then, these proposed algorithms are employed in a novel occlusion-aware intermediate view reconstruction that finds an intermediate image with a given disparity between two input images. This novelty is addressed by adding occlusion awareness to the reconstruction algorithm and proposing three quality improvement techniques to reduce image artifices: filling the re-sampling holes, removing ghost contours, and handling the disocclusion area. We compared the proposed algorithms to the previously well-known algorithms on each field qualitatively and quantitatively. The obtained results show that our algorithms are superior to the previous well-known algorithms. The performance of the proposed reconstruction algorithm is tested under 13 real images and 13 synthetic images. Moreover, analysis of a human-trial experiment conducted with 21 participants confirmed that the reconstructed images from our proposed algorithm have very high quality compared with the reconstructed images from the other existing algorithms

Efficient data structures for piecewise-smooth video processing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 95-102).A number of useful image and video processing techniques, ranging from low level operations such as denoising and detail enhancement to higher level methods such as object manipulation and special effects, rely on piecewise-smooth functions computed from the input data. In this thesis, we present two computationally efficient data structures for representing piecewise-smooth visual information and demonstrate how they can dramatically simplify and accelerate a variety of video processing algorithms. We start by introducing the bilateral grid, an image representation that explicitly accounts for intensity edges. By interpreting brightness values as Euclidean coordinates, the bilateral grid enables simple expressions for edge-aware filters. Smooth functions defined on the bilateral grid are piecewise-smooth in image space. Within this framework, we derive efficient reinterpretations of a number of edge-aware filters commonly used in computational photography as operations on the bilateral grid, including the bilateral filter, edgeaware scattered data interpolation, and local histogram equalization. We also show how these techniques can be easily parallelized onto modern graphics hardware for real-time processing of high definition video. The second data structure we introduce is the video mesh, designed as a flexible central data structure for general-purpose video editing. It represents objects in a video sequence as 2.5D "paper cutouts" and allows interactive editing of moving objects and modeling of depth, which enables 3D effects and post-exposure camera control. In our representation, we assume that motion and depth are piecewise-smooth, and encode them sparsely as a set of points tracked over time. The video mesh is a triangulation over this point set and per-pixel information is obtained by interpolation. To handle occlusions and detailed object boundaries, we rely on the user to rotoscope the scene at a sparse set of frames using spline curves. We introduce an algorithm to robustly and automatically cut the mesh into local layers with proper occlusion topology, and propagate the splines to the remaining frames. Object boundaries are refined with per-pixel alpha mattes. At its core, the video mesh is a collection of texture-mapped triangles, which we can edit and render interactively using graphics hardware. We demonstrate the effectiveness of our representation with special effects such as 3D viewpoint changes, object insertion, depthof- field manipulation, and 2D to 3D video conversion.by Jiawen Chen.Ph.D

Projective surface refinement for free-viewpoint video

This paper introduces a novel method of surface refinement for free-viewpoint video of dynamic scenes. Unlike previous approaches, the method presented here uses both visual hull and silhouette contours to constrain refinement of viewdependent depth maps from wide baseline views. A technique for extracting silhouette contours as rims in 3D from the view-dependent visual hull (VDVH) is presented. A new method for improving correspondence is introduced, where refinement of the VDVH is posed as a global problem in projective ray space. Artefacts of global optimisations are reduced by incorporating rims as constraints. Real time rendering of virtual views in a free-viewpoint video system is achieved using an image+depth representation for each real view. Results illustrate the high quality of rendered views achieved through this refinement technique

Representation and coding of 3D video data

Livrable D4.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.1 du projet

Real-time GPU-accelerated Out-of-Core Rendering and Light-field Display Visualization for Improved Massive Volume Understanding

Nowadays huge digital models are becoming increasingly available for a number of different applications ranging from CAD, industrial design to medicine and natural sciences. Particularly, in the field of medicine, data acquisition devices such as MRI or CT scanners routinely produce huge volumetric datasets. Currently, these datasets can easily reach dimensions of 1024^3 voxels and datasets larger than that are not uncommon. This thesis focuses on efficient methods for the interactive exploration of such large volumes using direct volume visualization techniques on commodity platforms. To reach this goal specialized multi-resolution structures and algorithms, which are able to directly render volumes of potentially unlimited size are introduced. The developed techniques are output sensitive and their rendering costs depend only on the complexity of the generated images and not on the complexity of the input datasets. The advanced characteristics of modern GPGPU architectures are exploited and combined with an out-of-core framework in order to provide a more flexible, scalable and efficient implementation of these algorithms and data structures on single GPUs and GPU clusters. To improve visual perception and understanding, the use of novel 3D display technology based on a light-field approach is introduced. This kind of device allows multiple naked-eye users to perceive virtual objects floating inside the display workspace, exploiting the stereo and horizontal parallax. A set of specialized and interactive illustrative techniques capable of providing different contextual information in different areas of the display, as well as an out-of-core CUDA based ray-casting engine with a number of improvements over current GPU volume ray-casters are both reported. The possibilities of the system are demonstrated by the multi-user interactive exploration of 64-GVoxel datasets on a 35-MPixel light-field display driven by a cluster of PCs. ------------------------------------------------------------------------------------------------------ Negli ultimi anni si sta verificando una proliferazione sempre più consistente di modelli digitali di notevoli dimensioni in campi applicativi che variano dal CAD e la progettazione industriale alla medicina e le scienze naturali. In modo particolare, nel settore della medicina, le apparecchiature di acquisizione dei dati come RM o TAC producono comunemente dei dataset volumetrici di grosse dimensioni. Questi dataset possono facilmente raggiungere taglie dell’ordine di 10243 voxels e dataset di dimensioni maggiori possono essere frequenti. Questa tesi si focalizza su metodi efficienti per l’esplorazione di tali grossi volumi utilizzando tecniche di visualizzazione diretta su piattaforme HW di diffusione di massa. Per raggiungere tale obiettivo si introducono strutture specializzate multi-risoluzione e algoritmi in grado di visualizzare volumi di dimensioni potenzialmente infinite. Le tecniche sviluppate sono “ouput sensitive” e la loro complessità di rendering dipende soltanto dalle dimensioni delle immagini generate e non dalle dimensioni dei dataset di input. Le caratteristiche avanzate delle architetture moderne GPGPU vengono inoltre sfruttate e combinate con un framework “out-of-core” in modo da offrire una implementazione di questi algoritmi e strutture dati più flessibile, scalabile ed efficiente su singole GPU o cluster di GPU. Per migliorare la percezione visiva e la comprensione dei dati, viene introdotto inoltre l’uso di tecnologie di display 3D di nuova generazione basate su un approccio di tipo light-field. Questi tipi di dispositivi consentono a diversi utenti di percepire ad occhio nudo oggetti che galleggiano all’interno dello spazio di lavoro del display, sfruttando lo stereo e la parallasse orizzontale. Si descrivono infine un insieme di tecniche illustrative interattive in grado di fornire diverse informazioni contestuali in diverse zone del display, così come un motore di “ray-casting out-of-core” basato su CUDA e contenente una serie di miglioramenti rispetto agli attuali metodi GPU di “ray-casting” di volumi. Le possibilità del sistema sono dimostrate attraverso l’esplorazione interattiva di dataset di 64-GVoxel su un display di tipo light-field da 35-MPixel pilotato da un cluster di PC

Motion parallax for 360° RGBD video

We present a method for adding parallax and real-time playback of 360° videos in Virtual Reality headsets. In current video players, the playback does not respond to translational head movement, which reduces the feeling of immersion, and causes motion sickness for some viewers. Given a 360° video and its corresponding depth (provided by current stereo 360° stitching algorithms), a naive image-based rendering approach would use the depth to generate a 3D mesh around the viewer, then translate it appropriately as the viewer moves their head. However, this approach breaks at depth discontinuities, showing visible distortions, whereas cutting the mesh at such discontinuities leads to ragged silhouettes and holes at disocclusions. We address these issues by improving the given initial depth map to yield cleaner, more natural silhouettes. We rely on a three-layer scene representation, made up of a foreground layer and two static background layers, to handle disocclusions by propagating information from multiple frames for the first background layer, and then inpainting for the second one. Our system works with input from many of today''s most popular 360° stereo capture devices (e.g., Yi Halo or GoPro Odyssey), and works well even if the original video does not provide depth information. Our user studies confirm that our method provides a more compelling viewing experience than without parallax, increasing immersion while reducing discomfort and nausea

Computational Multimedia for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras

Image-based rendering and synthesis

Multiview imaging (MVI) is currently the focus of some research as it has a wide range of applications and opens up research in other topics and applications, including virtual view synthesis for three-dimensional (3D) television (3DTV) and entertainment. However, a large amount of storage is needed by multiview systems and are difficult to construct. The concept behind allowing 3D scenes and objects to be visualized in a realistic way without full 3D model reconstruction is image-based rendering (IBR). Using images as the primary substrate, IBR has many potential applications including for video games, virtual travel and others. The technique creates new views of scenes which are reconstructed from a collection of densely sampled images or videos. The IBR concept has different classification such as knowing 3D models and the lighting conditions and be rendered using conventional graphic techniques. Another is lightfield or lumigraph rendering which depends on dense sampling with no or very little geometry for rendering without recovering the exact 3D-models.published_or_final_versio