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

Tensor approximation in visualization and graphics

In this course, we will introduce the basic concepts of tensor approximation (TA) – a higher-order generalization of the SVD and PCA methods – as well as its applications to visual data representation, analysis and visualization, and bring the TA framework closer to visualization and computer graphics researchers and practitioners. The course will cover the theoretical background of TA methods, their properties and how to compute them, as well as practical applications of TA methods in visualization and computer graphics contexts. In a first theoretical part, the attendees will be instructed on the necessary mathematical background of TA methods to learn the basics skills of using and applying these new tools in the context of the representation of large multidimensional visual data. Specific and very noteworthy features of the TA framework are highlighted which can effectively be exploited for spatio-temporal multidimensional data representation and visualization purposes. In two application oriented sessions, compact TA data representation in scientific visualization and computer graphics as well as decomposition and reconstruction algorithms will be demonstrated. At the end of the course, the participants will have a good basic knowledge of TA methods along with a practical understanding of its potential application in visualization and graphics related projects

Visual Prototyping of Cloth

Realistic visualization of cloth has many applications in computer graphics. An ongoing research problem is how to best represent and capture appearance models of cloth, especially when considering computer aided design of cloth. Previous methods can be used to produce highly realistic images, however, possibilities for cloth-editing are either restricted or require the measurement of large material databases to capture all variations of cloth samples. We propose a pipeline for designing the appearance of cloth directly based on those elements that can be changed within the production process. These are optical properties of fibers, geometrical properties of yarns and compositional elements such as weave patterns. We introduce a geometric yarn model, integrating state-of-the-art textile research. We further present an approach to reverse engineer cloth and estimate parameters for a procedural cloth model from single images. This includes the automatic estimation of yarn paths, yarn widths, their variation and a weave pattern. We demonstrate that we are able to match the appearance of original cloth samples in an input photograph for several examples. Parameters of our model are fully editable, enabling intuitive appearance design. Unfortunately, such explicit fiber-based models can only be used to render small cloth samples, due to large storage requirements. Recently, bidirectional texture functions (BTFs) have become popular for efficient photo-realistic rendering of materials. We present a rendering approach combining the strength of a procedural model of micro-geometry with the efficiency of BTFs. We propose a method for the computation of synthetic BTFs using Monte Carlo path tracing of micro-geometry. We observe that BTFs usually consist of many similar apparent bidirectional reflectance distribution functions (ABRDFs). By exploiting structural self-similarity, we can reduce rendering times by one order of magnitude. This is done in a process we call non-local image reconstruction, which has been inspired by non-local means filtering. Our results indicate that synthesizing BTFs is highly practical and may currently only take a few minutes for small BTFs. We finally propose a novel and general approach to physically accurate rendering of large cloth samples. By using a statistical volumetric model, approximating the distribution of yarn fibers, a prohibitively costly, explicit geometric representation is avoided. As a result, accurate rendering of even large pieces of fabrics becomes practical without sacrificing much generality compared to fiber-based techniques

Compression, Modeling, and Real-Time Rendering of Realistic Materials and Objects

The realism of a scene basically depends on the quality of the geometry, the illumination and the materials that are used. Whereas many sources for the creation of three-dimensional geometry exist and numerous algorithms for the approximation of global illumination were presented, the acquisition and rendering of realistic materials remains a challenging problem. Realistic materials are very important in computer graphics, because they describe the reflectance properties of surfaces, which are based on the interaction of light and matter. In the real world, an enormous diversity of materials can be found, comprising very different properties. One important objective in computer graphics is to understand these processes, to formalize them and to finally simulate them. For this purpose various analytical models do already exist, but their parameterization remains difficult as the number of parameters is usually very high. Also, they fail for very complex materials that occur in the real world. Measured materials, on the other hand, are prone to long acquisition time and to huge input data size. Although very efficient statistical compression algorithms were presented, most of them do not allow for editability, such as altering the diffuse color or mesostructure. In this thesis, a material representation is introduced that makes it possible to edit these features. This makes it possible to re-use the acquisition results in order to easily and quickly create deviations of the original material. These deviations may be subtle, but also substantial, allowing for a wide spectrum of material appearances. The approach presented in this thesis is not based on compression, but on a decomposition of the surface into several materials with different reflection properties. Based on a microfacette model, the light-matter interaction is represented by a function that can be stored in an ordinary two-dimensional texture. Additionally, depth information, local rotations, and the diffuse color are stored in these textures. As a result of the decomposition, some of the original information is inevitably lost, therefore an algorithm for the efficient simulation of subsurface scattering is presented as well. Another contribution of this work is a novel perception-based simplification metric that includes the material of an object. This metric comprises features of the human visual system, for example trichromatic color perception or reduced resolution. The proposed metric allows for a more aggressive simplification in regions where geometric metrics do not simplif

Understanding uncertainty to inform conservation: tools to protect the endangered black-throated finch southern subspecies

Juan Mula Laguna used the case of the southern black-throated finch to apply tools to inform the conservation of rare and poorly-known species. Mula Laguna created a list of investment priorities for a due update of the current Recovery Plan, a spatial classification of habitat value, and refuted hypotheses about a diet-driven decline

Advances in Biogas Desulfurization

Global concern about climate change caused by the exploitation of fossil fuels is encouraging the use of renewable energies. For instance, the European Union aims to be climate neutral by 2050. Biogas is an interesting renewable energy source due to its high calorific value. Today, biogas is mainly used for the production of electricity and heat by a combined heat and power engine. However, before its valorization, biogas needs to be desulfurized (H2S removal) to avoid corrosion and sulfur oxides emissions during its combustion. Biogas can be upgraded (CO2 removal) and used as vehicle fuel or injected into the natural gas grid. In the last 15 years, significant advances have occurred in the development of biological desulfurization processes. In this book with five chapters, the reader can find some of the latest advances in the biogas desulfurization and an overview of the state-of-the-art research. Three of them are research studies and two are reviews concerning the current state of biogas desulfurization technologies, economic analysis of alternatives, and the microbial ecology in biofiltration units. Biogas desulfurization is considered to be essential by many stakeholders (biogas producers, suppliers of biogas upgrading devices, gas traders, researchers, etc.) all around the world

Scene Monitoring With A Forest Of Cooperative Sensors

In this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual intervention, site models and camera calibration, and provides adaptability to changing environmental conditions. For object detection and categorization in the video stream, a two step detection procedure is used. First, regions of interest are determined using a novel hierarchical background subtraction algorithm that uses color and gradient information for interest region detection. Second, objects are located and classified from within these regions using a weakly supervised learning mechanism based on co-training that employs motion and appearance features. The main contribution of this approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The advantage of this approach is that it requires only a few initial training samples and can automatically adjust its parameters online to improve the detection and classification performance. Once objects are detected and classified they are tracked in individual cameras. Single camera tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence in individual cameras. The tracker has the capability to handle multiple occluded objects. Next, the objects are tracked across a forest of cameras with non-overlapping views. This is a hard problem because of two reasons. First, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, the system learns the inter-camera relationships to constrain track correspondences. These relationships are learned in the form of multivariate probability density of space-time variables (object entry and exit locations, velocities, and inter-camera transition times) using Parzen windows. To handle the appearance change of an object as it moves from one camera to another, we show that all color transfer functions from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both the location and appearance cues. Extensive experiments and deployment of this system in realistic scenarios has demonstrated the robustness of the proposed methods. The proposed system was able to detect and classify targets, and seamlessly tracked them across multiple cameras. It also generated a summary in terms of key frames and textual description of trajectories to a monitoring officer for final analysis and response decision. This level of interpretation was the goal of our research effort, and we believe that it is a significant step forward in the development of intelligent systems that can deal with the complexities of real world scenarios

Simultaneous removal of ortho-dichlorobenzene and ethanol vapors in a biotrickling filter : experimental results and process modeling

This study investigated the possibility of simultaneous removal of vapors of dissimilar volatile organic compounds from air streams in a biotrickling filter (BTF). Using a rricrobial consortium known to utilize ortho-dichlorobenzene (o-DCB) as sole carbon and energy source, a culture was developed on o-DCB/ethanol mixtures and a BTF unit was developed and operated with air streams carrying o-DCB and ethanol vapors. Simultaneous removal of the two compounds was observed in experiments that span a period of over three years. Experiments were performed at air residence times ranging from 4 to 6.5 min, liquid flow rates from 3.6 to 9 Lh-1, and inlet air concentrations from 0.85 to 4.5 gm-3 and 0.95 to I I gM-3 for o-DCB and ethanol, respectively. The maximum removal rate was 40 and 150 gm-3 packing h-1 for o-DCB and ethanol, respectively. It was possible to duplicate results in experiments performed under a given set of operating conditions at time intervals as far as 9 months apart from one another. The presence of the readily degradable ethanol at relatively high concentrations led to the formation of significant amounts of biomass in the liquid recirculating through the BTF resulting in the removal of o-DCB and ethanol both in the liquid phase and the biomass attached to the packing material. The readily biodegradable ethanol led to better coverage of the packing with b1ofilm, resulting in a positive effect on the removal of o-DCB vapor. However, every four months an abrupt increase in pressure drop build-up and concornitant loss in BTF performance was observed over a 5-day period. Once excess biomass was removed from the BTF unit, normal operation was recovered within 3-4 days. Steady state operation of the unit was mathematically described with a model involving mass balances for o-DCB, ethanol, and oxygen in three phases: air, recirculating liquid, and biofilm. The model accounts for reaction in both the liquid and biofilm phases. Through the introduction of the notion of effectiveness factors for o-DCB and ethanol, the equations for the b1ofilm were decoupled from those for the air and liquid allowing for easier numerical solution of the original complex model. The model was found capable of predicting the data on removal rates with a less than 10% - and oftentimes less than 5% error. Independent kinetic experiments led to the following conclusions: o-DCB and ethanol were biodegraded following Andrews self-inhibitory kinetics; no kinetic interactions occurred over the concentration ranges tested in the BTF; there was no biomass diversification along the length of the BTF unit

Realistic Visualization of Animated Virtual Cloth

Photo-realistic rendering of real-world objects is a broad research area with applications in various different areas, such as computer generated films, entertainment, e-commerce and so on. Within photo-realistic rendering, the rendering of cloth is a subarea which involves many important aspects, ranging from material surface reflection properties and macroscopic self-shadowing to animation sequence generation and compression. In this thesis, besides an introduction to the topic plus a broad overview of related work, different methods to handle major aspects of cloth rendering are described. Material surface reflection properties play an important part to reproduce the look & feel of materials, that is, to identify a material only by looking at it. The BTF (bidirectional texture function), as a function of viewing and illumination direction, is an appropriate representation of reflection properties. It captures effects caused by the mesostructure of a surface, like roughness, self-shadowing, occlusion, inter-reflections, subsurface scattering and color bleeding. Unfortunately a BTF data set of a material consists of hundreds to thousands of images, which exceeds current memory size of personal computers by far. This work describes the first usable method to efficiently compress and decompress a BTF data for rendering at interactive to real-time frame rates. It is based on PCA (principal component analysis) of the BTF data set. While preserving the important visual aspects of the BTF, the achieved compression rates allow the storage of several different data sets in main memory of consumer hardware, while maintaining a high rendering quality. Correct handling of complex illumination conditions plays another key role for the realistic appearance of cloth. Therefore, an upgrade of the BTF compression and rendering algorithm is described, which allows the support of distant direct HDR (high-dynamic-range) illumination stored in environment maps. To further enhance the appearance, macroscopic self-shadowing has to be taken into account. For the visualization of folds and the life-like 3D impression, these kind of shadows are absolutely necessary. This work describes two methods to compute these shadows. The first is seamlessly integrated into the illumination part of the rendering algorithm and optimized for static meshes. Furthermore, another method is proposed, which allows the handling of dynamic objects. It uses hardware-accelerated occlusion queries for the visibility determination. In contrast to other algorithms, the presented algorithm, despite its simplicity, is fast and produces less artifacts than other methods. As a plus, it incorporates changeable distant direct high-dynamic-range illumination. The human perception system is the main target of any computer graphics application and can also be treated as part of the rendering pipeline. Therefore, optimization of the rendering itself can be achieved by analyzing human perception of certain visual aspects in the image. As a part of this thesis, an experiment is introduced that evaluates human shadow perception to speedup shadow rendering and provides optimization approaches. Another subarea of cloth visualization in computer graphics is the animation of the cloth and avatars for presentations. This work also describes two new methods for automatic generation and compression of animation sequences. The first method to generate completely new, customizable animation sequences, is based on the concept of finding similarities in animation frames of a given basis sequence. Identifying these similarities allows jumps within the basis sequence to generate endless new sequences. Transmission of any animated 3D data over bandwidth-limited channels, like extended networks or to less powerful clients requires efficient compression schemes. The second method included in this thesis in the animation field is a geometry data compression scheme. Similar to the BTF compression, it uses PCA in combination with clustering algorithms to segment similar moving parts of the animated objects to achieve high compression rates in combination with a very exact reconstruction quality.Realistische Visualisierung von animierter virtueller Kleidung Das photorealistisches Rendering realer Gegenstände ist ein weites Forschungsfeld und hat Anwendungen in vielen Bereichen. Dazu zählen Computer generierte Filme (CGI), die Unterhaltungsindustrie und E-Commerce. Innerhalb dieses Forschungsbereiches ist das Rendern von photorealistischer Kleidung ein wichtiger Bestandteil. Hier reichen die wichtigen Aspekte, die es zu berücksichtigen gilt, von optischen Materialeigenschaften über makroskopische Selbstabschattung bis zur Animationsgenerierung und -kompression. In dieser Arbeit wird, neben der Einführung in das Thema, ein weiter Überblick über ähnlich gelagerte Arbeiten gegeben. Der Schwerpunkt der Arbeit liegt auf den wichtigen Aspekten der virtuellen Kleidungsvisualisierung, die oben beschrieben wurden. Die optischen Reflektionseigenschaften von Materialoberflächen spielen eine wichtige Rolle, um das so genannte look & feel von Materialien zu charakterisieren. Hierbei kann ein Material vom Nutzer identifiziert werden, ohne dass er es direkt anfassen muss. Die BTF (bidirektionale Texturfunktion)ist eine Funktion die abhängig von der Blick- und Beleuchtungsrichtung ist. Daher ist sie eine angemessene Repräsentation von Reflektionseigenschaften. Sie enthält Effekte wie Rauheit, Selbstabschattungen, Verdeckungen, Interreflektionen, Streuung und Farbbluten, die durch die Mesostruktur der Oberfläche hervorgerufen werden. Leider besteht ein BTF Datensatz eines Materials aus hunderten oder tausenden von Bildern und sprengt damit herkömmliche Hauptspeicher in Computern bei weitem. Diese Arbeit beschreibt die erste praktikable Methode, um BTF Daten effizient zu komprimieren, zu speichern und für Echtzeitanwendungen zum Visualisieren wieder zu dekomprimieren. Die Methode basiert auf der Principal Component Analysis (PCA), die Daten nach Signifikanz ordnet. Während die PCA die entscheidenen visuellen Aspekte der BTF erhält, können mit ihrer Hilfe Kompressionsraten erzielt werden, die es erlauben mehrere BTF Materialien im Hauptspeicher eines Consumer PC zu verwalten. Dies erlaubt ein High-Quality Rendering. Korrektes Verwenden von komplexen Beleuchtungssituationen spielt eine weitere, wichtige Rolle, um Kleidung realistisch erscheinen zu lassen. Daher wird zudem eine Erweiterung des BTF Kompressions- und Renderingalgorithmuses erläutert, die den Einsatz von High-Dynamic Range (HDR) Beleuchtung erlaubt, die in environment maps gespeichert wird. Um die realistische Erscheinung der Kleidung weiter zu unterstützen, muss die makroskopische Selbstabschattung integriert werden. Für die Visualisierung von Falten und den lebensechten 3D Eindruck ist diese Art von Schatten absolut notwendig. Diese Arbeit beschreibt daher auch zwei Methoden, diese Schatten schnell und effizient zu berechnen. Die erste ist nahtlos in den Beleuchtungspart des obigen BTF Renderingalgorithmuses integriert und für statische Geometrien optimiert. Die zweite Methode behandelt dynamische Objekte. Dazu werden hardwarebeschleunigte Occlusion Queries verwendet, um die Sichtbarkeitsberechnung durchzuführen. Diese Methode ist einerseits simpel und leicht zu implementieren, anderseits ist sie schnell und produziert weniger Artefakte, als vergleichbare Methoden. Zusätzlich ist die Verwendung von veränderbarer, entfernter HDR Beleuchtung integriert. Das menschliche Wahrnehmungssystem ist das eigentliche Ziel jeglicher Anwendung in der Computergrafik und kann daher selbst als Teil einer erweiterten Rendering Pipeline gesehen werden. Daher kann das Rendering selbst optimiert werden, wenn man die menschliche Wahrnehmung verschiedener visueller Aspekte der berechneten Bilder analysiert. Teil der vorliegenden Arbeit ist die Beschreibung eines Experimentes, das menschliche Schattenwahrnehmung untersucht, um das Rendern der Schatten zu beschleunigen. Ein weiteres Teilgebiet der Kleidungsvisualisierung in der Computergrafik ist die Animation der Kleidung und von Avataren für Präsentationen. Diese Arbeit beschreibt zwei neue Methoden auf diesem Teilgebiet. Einmal ein Algorithmus, der für die automatische Generierung neuer Animationssequenzen verwendet werden kann und zum anderen einen Kompressionsalgorithmus für eben diese Sequenzen. Die automatische Generierung von völlig neuen, anpassbaren Animationen basiert auf dem Konzept der Ähnlichkeitssuche. Hierbei werden die einzelnen Schritte von gegebenen Basisanimationen auf Ähnlichkeiten hin untersucht, die zum Beispiel die Geschwindigkeiten einzelner Objektteile sein können. Die Identifizierung dieser Ähnlichkeiten erlaubt dann Sprünge innerhalb der Basissequenz, die dazu benutzt werden können, endlose, neue Sequenzen zu erzeugen. Die Übertragung von animierten 3D Daten über bandbreitenlimitierte Kanäle wie ausgedehnte Netzwerke, Mobilfunk oder zu sogenannten thin clients erfordert eine effiziente Komprimierung. Die zweite, in dieser Arbeit vorgestellte Methode, ist ein Kompressionsschema für Geometriedaten. Ähnlich wie bei der Kompression von BTF Daten wird die PCA in Verbindung mit Clustering benutzt, um die animierte Geometrie zu analysieren und in sich ähnlich bewegende Teile zu segmentieren. Diese erkannten Segmente lassen sich dann hoch komprimieren. Der Algorithmus arbeitet automatisch und erlaubt zudem eine sehr exakte Rekonstruktionsqualität nach der Dekomprimierung

BRDF Representation and Acquisition

Photorealistic rendering of real world environments is important in a range of different areas; including Visual Special effects, Interior/Exterior Modelling, Architectural Modelling, Cultural Heritage, Computer Games and Automotive Design. Currently, rendering systems are able to produce photorealistic simulations of the appearance of many real-world materials. In the real world, viewer perception of objects depends on the lighting and object/material/surface characteristics, the way a surface interacts with the light and on how the light is reflected, scattered, absorbed by the surface and the impact these characteristics have on material appearance. In order to re-produce this, it is necessary to understand how materials interact with light. Thus the representation and acquisition of material models has become such an active research area. This survey of the state-of-the-art of BRDF Representation and Acquisition presents an overview of BRDF (Bidirectional Reflectance Distribution Function) models used to represent surface/material reflection characteristics, and describes current acquisition methods for the capture and rendering of photorealistic materials