X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation

We suggest to represent an X-Field -a set of 2D images taken across different view, time or illumination conditions, i.e., video, light field, reflectance fields or combinations thereof-by learning a neural network (NN) to map their view, time or light coordinates to 2D images. Executing this NN at new coordinates results in joint view, time or light interpolation. The key idea to make this workable is a NN that already knows the "basic tricks" of graphics (lighting, 3D projection, occlusion) in a hard-coded and differentiable form. The NN represents the input to that rendering as an implicit map, that for any view, time, or light coordinate and for any pixel can quantify how it will move if view, time or light coordinates change (Jacobian of pixel position with respect to view, time, illumination, etc.). Our X-Field representation is trained for one scene within minutes, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination

Robust object-based algorithms for direct shadow simulation

En informatique graphique, les algorithmes de générations d'ombres évaluent la quantité de lumière directement perçue par une environnement virtuel. Calculer précisément des ombres est cependant coûteux en temps de calcul. Dans cette dissertation, nous présentons un nouveau système basé objet robuste, qui permet de calculer des ombres réalistes sur des scènes dynamiques et ce en temps interactif. Nos contributions incluent notamment le développement de nouveaux algorithmes de génération d'ombres douces ainsi que leur mise en oeuvre efficace sur processeur graphique. Nous commençons par formaliser la problématique du calcul d'ombres directes. Tout d'abord, nous définissons ce que sont les ombres directes dans le contexte général du transport de la lumière. Nous étudions ensuite les techniques interactives qui génèrent des ombres directes. Suite à cette étude nous montrons que mêmes les algorithmes dit physiquement réalistes se reposent sur des approximations. Nous mettons également en avant, que malgré leur contraintes géométriques, les algorithmes d'ombres basées objet sont un bon point de départ pour résoudre notre problématique de génération efficace et robuste d'ombres directes. Basé sur cette observation, nous étudions alors le système basé objet existant et mettons en avant ses problèmes de robustesse. Nous proposons une nouvelle technique qui améliore la qualité des ombres générées par ce système en lui ajoutant une étape de mélange de pénombres. Malgré des propriétés et des résultats convaincants, les limitations théoriques et de mise en oeuvre limite la qualité générale et les performances de cet algorithme. Nous présentons ensuite un nouvel algorithme d'ombres basées objet. Cet algorithme combine l'efficacité de l'approche basée objet temps réel avec la précision de sa généralisation au rendu hors ligne. Notre algorithme repose sur l'évaluation locale du nombre d'objets entre deux points : la complexité de profondeur. Nous décrivons comment nous utilisons cet algorithme pour échantillonner la complexité de profondeur entre les surfaces visibles d'une scène et une source lumineuse. Nous générons ensuite des ombres à partir de cette information soit en modulant l'éclairage direct soit en intégrant numériquement l'équation d'illumination directe. Nous proposons ensuite une extension de notre algorithme afin qu'il puisse prendre en compte les ombres projetées par des objets semi-opaque. Finalement, nous présentons une mise en oeuvre efficace de notre système qui démontre que des ombres basées objet peuvent être générées de façon efficace et ce même sur une scène dynamique. En rendu temps réel, il est commun de représenter des objets très détaillés encombinant peu de triangles avec des textures qui représentent l'opacité binaire de l'objet. Les techniques de génération d'ombres basées objet ne traitent pas de tels triangles dit "perforés". De par leur nature, elles manipulent uniquement les géométries explicitement représentées par des primitives géométriques. Nous présentons une nouvel algorithme basé objet qui lève cette limitation. Nous soulignons que notre méthode peut être efficacement combinée avec les systèmes existants afin de proposer un système unifié basé objet qui génère des ombres à la fois pour des maillages classiques et des géométries perforées. La mise en oeuvre proposée montre finalement qu'une telle combinaison fournit une solution élégante, efficace et robuste à la problématique générale de l'éclairage direct et ce aussi bien pour des applications temps réel que des applications sensibles à la la précision du résultat.Direct shadow algorithms generate shadows by simulating the direct lighting interaction in a virtual environment. The main challenge with the accurate direct shadow problematic is its computational cost. In this dissertation, we develop a new robust object-based shadow framework that provides realistic shadows at interactive frame rate on dynamic scenes. Our contributions include new robust object-based soft shadow algorithms and efficient interactive implementations. We start, by formalizing the direct shadow problematic. Following the light transport problematic, we first formalize what are robust direct shadows. We then study existing interactive direct shadow techniques and outline that the real time direct shadow simulation remains an open problem. We show that even the so called physically plausible soft shadow algorithms still rely on approximations. Nevertheless we exhibit that, despite their geometric constraints, object-based approaches seems well suited when targeting accurate solutions. Starting from the previous analyze, we investigate the existing object-based shadow framework and discuss about its robustness issues. We propose a new technique that drastically improve the resulting shadow quality by improving this framework with a penumbra blending stage. We present a practical implementation of this approach. From the obtained results, we outline that, despite desirable properties, the inherent theoretical and implementation limitations reduce the overall quality and performances of the proposed algorithm. We then present a new object-based soft shadow algorithm. It merges the efficiency of the real time object-based shadows with the accuracy of its offline generalization. The proposed algorithm lies onto a new local evaluation of the number of occluders between $two$ points (\ie{} the depth complexity). We describe how we use this algorithm to sample the depth complexity between any visible receiver and the light source. From this information, we compute shadows by either modulate the direct lighting or numerically solve the direct illumination with an accuracy depending on the light sampling strategy. We then propose an extension of our algorithm in order to handle shadows cast by semi opaque occluders. We finally present an efficient implementation of this framework that demonstrates that object-based shadows can be efficiently used on complex dynamic environments. In real time rendering, it is common to represent highly detailed objects with few triangles and transmittance textures that encode their binary opacity. Object-based techniques do not handle such perforated triangles. Due to their nature, they can only evaluate the shadows cast by models whose their shape is explicitly defined by geometric primitives. We describe a new robust object-based algorithm that addresses this main limitation. We outline that this method can be efficiently combine with object-based frameworks in order to evaluate approximative shadows or simulate the direct illumination for both common meshes and perforated triangles. The proposed implementation shows that such combination provides a very strong and efficient direct lighting framework, well suited to many domains ranging from quality sensitive to performance critical applications

Rendering Antialiased Shadows using Warped Variance Shadow Maps

Shadows contribute significantly to the perceived realism of an image, and provide an important depth cue. Rendering high quality, antialiased shadows efficiently is a difficult problem. To antialias shadows, it is necessary to compute partial visibilities, but computing these visibilities using existing approaches is often too slow for interactive applications. Shadow maps are a widely used technique for real-time shadow rendering. One major drawback of shadow maps is aliasing, because the shadow map data cannot be filtered in the same way as colour textures. In this thesis, I present variance shadow maps (VSMs). Variance shadow maps use a linear representation of the depth distributions in the shadow map, which enables the use of standard linear texture filtering algorithms. Thus VSMs can address the problem of shadow aliasing using the same highly-tuned mechanisms that are available for colour images. Given the mean and variance of the depth distribution, Chebyshev's inequality provides an upper bound on the fraction of a shaded fragment that is occluded, and I show that this bound often provides a good approximation to the true partial occlusion. For more difficult cases, I show that warping the depth distribution can produce multiple bounds, some tighter than others. Based on this insight, I present layered variance shadow maps, a scalable generalization of variance shadow maps that partitions the depth distribution into multiple segments. This reduces or eliminates an artifact - "light bleeding" - that can appear when using the simpler version of variance shadow maps. Additionally, I demonstrate exponential variance shadow maps, which combine moments computed from two exponentially-warped depth distributions. Using this approach, high quality results are produced at a fraction of the storage cost of layered variance shadow maps. These algorithms are easy to implement on current graphics hardware and provide efficient, scalable solutions to the problem of shadow map aliasing

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

Fast View Synthesis with Deep Stereo Vision

Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the large-baseline novel view synthesis problem is far from being "solved". Recent works have attempted to use Convolutional Neural Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of learning scene geometry and interpreting camera motion, CNNs are often unable to generate realistic novel views. In this paper, we present a novel view synthesis approach based on stereo-vision and CNNs that decomposes the problem into two sub-tasks: view dependent geometry estimation and texture inpainting. Both tasks are structured prediction problems that could be effectively learned with CNNs. Experiments on the KITTI Odometry dataset show that our approach is more accurate and significantly faster than the current state-of-the-art. The code and supplementary material will be publicly available. Results could be found here https://youtu.be/5pzS9jc-5t

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

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