Utilisation de l'Apparence pour le Rendu et l'édition efficaces de scènes capturées

Computer graphics strives to render synthetic images identical to real photographs. Multiple rendering algorithms have been developed for the better part of the last half-century. Traditional algorithms use 3D assets manually generated by artists to render a scene. While the initial scenes were quite simple, the field has developed complex representations of geometry, material and lighting: the three basic components of a 3D scene. Generating such complex assets is hard and requires significant time and skills by professional 3D artists. In addition to asset generation, the rendering algorithms themselves involve complex simulation techniques to solve for global light transport in a scene which costs more time.As the ease of capturing photographs improved, Image-based Rendering (IBR) emerged as an alternative to traditional rendering. Using captured images as input became much faster than generating traditional scene assets. Initial IBR algorithms focused on creating a scene model using the input images to interpolate or warp them and enable free-viewpoint navigation of captured scenes. With time the scene models became more complex and using a geometric proxy computed from the input images became an integral part of IBR. Today using a mesh reconstructed using Structure-from-Motion (SfM) and Multi-view Stereo (MVS) techniques is widely used in IBR even though they introduce significant artifacts due to noisy reconstruction.In this thesis we first propose a novel image-based rendering algorithm, which focuses on rendering a captured scene with good quality at interactive frame rates}. We study different artifacts from previous IBR algorithms and propose an algorithm which builds upon previous work to remove such artifacts. The algorithm utilizes surface appearance in order to treat view-dependent regions differently than diffuse regions. Our Hybrid-IBR algorithm performs favorably against classical and modern IBR approaches for a wide variety of scenes in terms of quality and/or speed.While IBR provides solutions to render a scene, editing them is hard. Editing scenes require estimating a scene's geometry, material appearance and illumination. As our second contribution \textbf{we explicitly estimate \emph{scene-scale} material parameters from a set of captured photographs to enable scene editing}. While commercial photogrammetry solutions recover diffuse texture to aid 3D artists in generating material assets manually, we aim to \emph{automatically} create material texture atlases from captured images of a scene. We take advantage of the visual cues provided by the multi-view observations. Feeding it to a Convolutional Neural Network (CNN) we obtain material maps for each view. Using the predicted maps we create multi-view consistent material texture atlases by aggregating the information in texture space. Using our automatically generated material texture atlases we demonstrate relighting and object insertion in real scenes.Learning-based tasks require large amounts of data with variety to learn the task efficiently. Using synthetic datasets to train is the norm but using traditional rendering to render large datasets is time consuming providing limited variability. We propose \textbf{a new neural rendering-based approach that learns a neural scene representation with variability and use it to generate large amounts of data at a significantly faster rate on the fly}. We demonstrate the advantage of using neural rendering as compared to traditional rendering in terms of speed of generating dataset as well as learning auxiliary tasks given the same computational budget.L’informatique graphique a pour but de rendre des images de synthèse semblables à des photographies. Plusieurs algorithmes de rendu ont été développés au cours du dernier demi-siècle, principalement pour restituer des scènes à base d'éléments 3D créés par des artistes. Alors que les scènes initiales étaient assez simples, des représentations plus complexes de la géométrie, des matériaux et de l'éclairage ont été développés. Créer des scènes aussi complexes nécessite beaucoup de travail et de compétences de la part d'artistes 3D professionnels. Au même temps, les algorithmes de rendu impliquent des techniques de simulation complexes coûteuses en temps, pour résoudre le transport global de la lumière dans une scène.Avec la popularité grandissante de la photo numérique, le rendu basé image (IBR) a émergé comme une alternative au rendu traditionnel. Avec cette approche, l'utilisation de photos comme données d'entrée est devenue beaucoup plus rapide que la génération de scènes classiques. Les algorithmes IBR se sont d’abord concentrés sur la restitution de scènes pour en permettre une exploration libre. Au fil du temps, les modèles de scène sont devenus plus complexes et l'utilisation d'un proxy géométrique inféré à partir d’images est devenue la norme. Aujourd'hui, l'utilisation d'un maillage reconstruit à l'aide des techniques Structure-from-Motion (SfM) et Multi-view Stereo (MVS) est courante en IBR, bien que cette utilisation introduit des artefacts importants. Nous proposons d'abord un nouvel algorithme de rendu basé image, qui se concentre sur le rendu de qualité et en temps interactif d'une scène capturée}. Nous étudions différentes faiblesses des travaux précédents et proposons un algorithme qui s'appuie sur ces travaux pour obtenir de meilleurs résultats. Notre algorithme se base sur l'apparence de la surface pour traiter les régions dont l'apparence dépend de l'angle de vue différemment des régions diffuses. Hybrid-IBR obtient des résultats favorables par rapport aux approches concurrentes pour une grande variété de scènes en termes de qualité et/ou de vitesse.Bien que l'IBR soit une bonne solution de rendu, l'édition de celle-ci est difficile sans une décomposition en différents éléments : la géométrie, l'apparence des matériaux et l'éclairage de la scène. Pour notre deuxième contribution, \textbf{nous estimons explicitement les paramètres de matériaux à \emph{l'échelle de la scène} à partir d'un ensemble de photographies, pour permettre l'édition de la scène}. Alors que les solutions de photogrammétrie commerciales calculent la texture diffuse pour assister la création manuelle de matériaux, nous visons à créer \emph{automatiquement} des atlas de texture de matériaux à partir d'un ensemble d'images d'une scène. Nous nous appuyons sur les informations fournis par ces images et les transmettons à un réseau neuronal convolutif pour obtenir des cartes de matériaux pour chaque vue. En utilisant toutes ces prédictions, nous créons des atlas de texture de matériau cohérents pour toutes les vues en agrégeant les informations dans l'espace texture. Nous démontrons l'utilisation de notre atlas de texture de matériaux généré automatiquement pour rendre des scènes réelles avec un changement d’illumination et avec des objets virtuels insérés.L'apprentissage profond nécessite de grandes quantités de données variées. L'utilisation de données synthétiques est courante, mais l'utilisation du rendu traditionnel pour créer ces données prend du temps et offre une variabilité limitée. Nous proposons \textbf{une nouvelle approche basée sur le rendu neuronal qui apprend une représentation de scène neuronale avec paramètres variables, et l'utilise pour générer au vol de grandes quantités de données à un rythme beaucoup plus rapide}. Nous démontrons l'avantage d'utiliser le rendu neuronal par rapport au rendu traditionnel en termes de budget de temps, ainsi que pour l'apprentissage de tâches auxiliaires avec le même budget de calcul

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

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

Path manipulation strategies for rendering dynamic environments.

The current work introduces path manipulation as a tool that extends bidirectional path tracing to reuse paths in the temporal domain. Defined as an apparatus of sampling and reuse strategies, path manipulation reconstructs the subpaths that compose the light transport paths and addresses the restriction of static geometry commonly associated with Monte Carlo light transport simulations. By reconstructing and reusing subpaths, the path manipulation algorithm obviates the regeneration of the entire path collection, reduces the computational load of the original algorithm and supports scene dynamism. Bidirectional path tracing relies on local path sampling techniques to generate the paths of light in a synthetic environment. By using the information localized at path vertices, like the probability distribution, the sampling techniques construct paths progressively with distinct probability densities. Each probability density corresponds to a particular sampling technique, which accounts for specific illumination effects. Bidirectional path tracing uses multiple importance sampling to combine paths sampled with different techniques in low-variance estimators. The path sampling techniques and multiple importance sampling are the keys to the efficacy of bidirectional path tracing. However, the sampling techniques gained little attention beyond the generation and evaluation of paths. Bidirectional path tracing was designed for static scenes and thus it discards the generated paths immediately after the evaluation of their contributions. Limiting the lifespan of paths to a generation-evaluation cycle imposes a static use of paths and of sampling techniques. The path manipulation algorithm harnesses the potential of the sampling techniques to supplant the static manipulation of paths with a generation-evaluation-reuse cycle. An intra-subpath connectivity strategy was devised to reconnect the segregated chains of the subpaths invalidated by the scene alterations. Successful intra-subpath connections generate subpaths in multiple pieces by reusing subpath chains from prior frames. Subpaths are reconstructed generically, regardless of the subpath or scene dynamism type and without the need for predefined animation paths. The result is the extension of bidirectional path tracing to the temporal domain

Inverse tone mapping

The introduction of High Dynamic Range Imaging in computer graphics has produced a novelty in Imaging that can be compared to the introduction of colour photography or even more. Light can now be captured, stored, processed, and finally visualised without losing information. Moreover, new applications that can exploit physical values of the light have been introduced such as re-lighting of synthetic/real objects, or enhanced visualisation of scenes. However, these new processing and visualisation techniques cannot be applied to movies and pictures that have been produced by photography and cinematography in more than one hundred years. This thesis introduces a general framework for expanding legacy content into High Dynamic Range content. The expansion is achieved avoiding artefacts, producing images suitable for visualisation and re-lighting of synthetic/real objects. Moreover, it is presented a methodology based on psychophysical experiments and computational metrics to measure performances of expansion algorithms. Finally, a compression scheme, inspired by the framework, for High Dynamic Range Textures, is proposed and evaluated