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

Bidirectional Texture Functions: Acquisition, Rendering and Quality Evaluation

As one of its primary objectives, Computer Graphics aims at the simulation of fabrics’ complex reflection behaviour. Characteristic surface reflectance of fabrics, such as highlights, anisotropy or retro-reflection arise the difficulty of synthesizing. This problem can be solved by using Bidirectional Texture Functions (BTFs), a 2D-texture under various light and view direction. But the acquisition of Bidirectional Texture Functions requires an expensive setup and the measurement process is very time-consuming. Moreover, the size of BTF data can range from hundreds of megabytes to several gigabytes, as a large number of high resolution pictures have to be used in any ideal cases. Furthermore, the three-dimensional textured models rendered through BTF rendering method are subject to various types of distortion during acquisition, synthesis, compression, and processing. An appropriate image quality assessment scheme is a useful tool for evaluating image processing algorithms, especially algorithms designed to leave the image visually unchanged. In this contribution, we present and conduct an investigation aimed at locating a robust threshold for downsampling BTF images without loosing perceptual quality. To this end, an experimental study on how decreasing the texture resolution influences perceived quality of the rendered images has been presented and discussed. Next, two basic improvements to the use of BTFs for rendering are presented: firstly, the study addresses the cost of BTF acquisition by introducing a flexible low-cost step motor setup for BTF acquisition allowing to generate a high quality BTF database taken at user-defined arbitrary angles. Secondly, the number of acquired textures to the perceptual quality of renderings is adapted so that the database size is not overloaded and can fit better in memory when rendered. Although visual attention is one of the essential attributes of HVS, it is neglected in most existing quality metrics. In this thesis an appropriate objective quality metric based on extracting visual attention regions from images and adequate investigation of the influence of visual attention on perceived image quality assessment, called Visual Attention Based Image Quality Metric (VABIQM), has been proposed. The novel metric indicates that considering visual saliency can offer significant benefits with regard to constructing objective quality metrics to predict the visible quality differences in images rendered by compressed and non-compressed BTFs and also outperforms straightforward existing image quality metrics at detecting perceivable differences

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

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年

Advanced methods for relightable scene representations in image space

The realistic reproduction of visual appearance of real-world objects requires accurate computer graphics models that describe the optical interaction of a scene with its surroundings. Data-driven approaches that model the scene globally as a reflectance field function in eight parameters deliver high quality and work for most material combinations, but are costly to acquire and store. Image-space relighting, which constrains the application to create photos with a virtual, fix camera in freely chosen illumination, requires only a 4D data structure to provide full fidelity. This thesis contributes to image-space relighting on four accounts: (1) We investigate the acquisition of 4D reflectance fields in the context of sampling and propose a practical setup for pre-filtering of reflectance data during recording, and apply it in an adaptive sampling scheme. (2) We introduce a feature-driven image synthesis algorithm for the interpolation of coarsely sampled reflectance data in software to achieve highly realistic images. (3) We propose an implicit reflectance data representation, which uses a Bayesian approach to relight complex scenes from the example of much simpler reference objects. (4) Finally, we construct novel, passive devices out of optical components that render reflectance field data in real-time, shaping the incident illumination into the desired imageDie realistische Wiedergabe der visuellen Erscheinung einer realen Szene setzt genaue Modelle aus der Computergraphik für die Interaktion der Szene mit ihrer Umgebung voraus. Globale Ansätze, die das Verhalten der Szene insgesamt als Reflektanzfeldfunktion in acht Parametern modellieren, liefern hohe Qualität für viele Materialtypen, sind aber teuer aufzuzeichnen und zu speichern. Verfahren zur Neubeleuchtung im Bildraum schränken die Anwendbarkeit auf fest gewählte Kameras ein, ermöglichen aber die freie Wahl der Beleuchtung, und erfordern dadurch lediglich eine 4D - Datenstruktur für volle Wiedergabetreue. Diese Arbeit enthält vier Beiträge zu diesem Thema: (1) wir untersuchen die Aufzeichnung von 4D Reflektanzfeldern im Kontext der Abtasttheorie und schlagen einen praktischen Aufbau vor, der Reflektanzdaten bereits während der Messung vorfiltert. Wir verwenden ihn in einem adaptiven Abtastschema. (2) Wir führen einen merkmalgesteuerten Bildsynthesealgorithmus für die Interpolation von grob abgetasteten Reflektanzdaten ein. (3) Wir schlagen eine implizite Beschreibung von Reflektanzdaten vor, die mit einem Bayesschen Ansatz komplexe Szenen anhand des Beispiels eines viel einfacheren Referenzobjektes neu beleuchtet. (4) Unter der Verwendung optischer Komponenten schaffen wir passive Aufbauten zur Darstellung von Reflektanzfeldern in Echtzeit, indem wir einfallende Beleuchtung direkt in das gewünschte Bild umwandeln

Innovative SAR & ISAR Signal Processing

This thesis reports on research into the eld of Synthetic Aperture Radar (SAR) and Inverse Synthetic Aperture Radar (ISAR) signal processing. The contributions of this thesis may be divided into two following parts: A new bistatic 3D near eld circular SAR imaging algorithm was devel- oped. High resolution radar imaging is typically obtained by combining wide bandwidth signals and synthetic aperture processing. High range resolution is obtained by using modulated signals whereas high cross range resolution is achieved by coherently processing the target echoes at dierent aspect angles of the target. Anyway, theoretical results have shown that when the aspect angle whereby the target is observed is suf- ciently wide, high resolution target images can be obtained by using continuous wave (CW) radars [2], therefore allowing to reduce hardware costs. In a similar way, three dimensional radar imaging can be per- formed by coherently processing the backscattered eld as a function of two rotation angles about two orthogonal axes [3].Three dimensional tar- get radar imaging can be eciently obtained by means of a 3D Fourier Transform, when the far-eld (planar wave) approximation holds. Oth- erwise, the wavefront curvature has to be accounted for. For this reason, a new algorithm based on a near eld spherical wave illumination that takes into account the wavefront curvature by adopting a planar piece- wise approximation was designed. This means that the wavefront is as- sumed to be locally planar around a given point on the target. The oper- ator that the algorithm uses for the focusing procedure is a space variant focusing function which aims at compensating the propagation losses and the wavefront curvature. The algorithm has been developed under the Microwave Electronic Imaging Security and Safety Access (MELISSA) project. The system MELISSA is a body scanner whose purpose is the detection of concealed objects. The added value of the system is the capability to provide an electromagnetic image of the concealed objects. The author would like to thank all people that worked at the project, all LabRass colleagues, all people who designed and acquired real data, all people that permitted the drafting of the rst part of this thesis. The developed algorithm was presented in the chapter 1. The goal of this work was the system design concerning the imaging point of view, by simulating and therefore predicting the system performance by means of the developed algorithm. In the chapter 2 was shown how the design was achieved. Finally, in the chapter 3, the results on real data measured in anechoic chamber with a system with characteristics very close to the nal system prototype MELISSA, was presented. A new way of ISAR processing has been dened, by applying the tradi- tional ISAR processing to data acquired from passive radars. Purpose of the ISAR processing is to extract an electromagnetic bi-dimensional im- age of the target in order to determine the main geometric features of the target, allowing (when possible) recognition and classication. Passive radars are able to detect and track targets by exploiting illuminators of opportunity (IOs). In this work of thesis, it will be proven that the same concept can be extended to allow for Passive Inverse Synthetic Aperture Radar (P-ISAR) imaging. A suitable signal processing is detailed that is able to form P-ISAR images starting from range-Doppler maps, which represent the output of a passive radar signal processing. Multiple chan- nels Digital Video Broadcasting - Terrestrial (DVB-T) signals are used to demonstrate the concept as they provide enough range resolution to form meaningful ISAR images. The problem of grating lobes, generated by DVB-T signal, is also addressed and solved by proposing an innovative P-ISAR technique. The second part of this thesis has been developed un- der the Array Passive ISAR adaptive processing (APIS) project. APIS is dened as a multichannel, bi-static single receiver for array passive radar, capable of detecting targets and generating ISAR images of the detected targets for classication purposes. The author would like to thank all people that worked at the project, all LabRass colleagues, all people who designed, built the prototype and acquired real data, all people that per- mitted the drafting of the second part of this thesis. In the chapter 4, the basics on Passive Bistatic Radar (PBR) was brie y recalled, the P-ISAR processor was detailed and the new algorithm per the Grating Lobes Cancellation was presented. In the chapter 5, some numerical results on simulated data was shown, in order to demonstrate the potentiality of the P-ISAR, for the imaging and classication purpose. In fact, by using more than three adjacent channels and by observing the signal for a long time, ner range and cross-range resolutions, respectively, could be achieved. Finally, the obtained results on real data was discussed in the chapter 6

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Surface analysis and visualization from multi-light image collections

Multi-Light Image Collections (MLICs) are stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination that provides large amounts of visual and geometric information. Over the last decades, a wide variety of methods have been devised to extract information from MLICs and have shown its use in different application domains to support daily activities. In this thesis, we present methods that leverage a MLICs for surface analysis and visualization. First, we provide background information: acquisition setup, light calibration and application areas where MLICs have been successfully used for the research of daily analysis work. Following, we discuss the use of MLIC for surface visualization and analysis and available tools used to support the analysis. Here, we discuss methods that strive to support the direct exploration of the captured MLIC, methods that generate relightable models from MLIC, non-photorealistic visualization methods that rely on MLIC, methods that estimate normal map from MLIC and we point out visualization tools used to do MLIC analysis. In chapter 3 we propose novel benchmark datasets (RealRTI, SynthRTI and SynthPS) that can be used to evaluate algorithms that rely on MLIC and discusses available benchmark for validation of photometric algorithms that can be also used to validate other MLIC-based algorithms. In chapter 4, we evaluate the performance of different photometric stereo algorithms using SynthPS for cultural heritage applications. RealRTI and SynthRTI have been used to evaluate the performance of (Neural)RTI method. Then, in chapter 5, we present a neural network-based RTI method, aka NeuralRTI, a framework for pixel-based encoding and relighting of RTI data. In this method using a simple autoencoder architecture, we show that it is possible to obtain a highly compressed representation that better preserves the original information and provides increased quality of virtual images relighted from novel directions, particularly in the case of challenging glossy materials. Finally, in chapter 6, we present a method for the detection of crack on the surface of paintings from multi-light image acquisitions and that can be used as well on single images and conclude our presentation

Dynamic data structures and saliency-influenced rendering

With increasing heterogeneity of modern hardware, different requirements for 3d applications arise. Despite the fact that real-time rendering of photo-realistic images is possible using today’s graphics cards, still large computational effort is required. Furthermore, smart-phones or computers with older, less powerful graphics cards may not be able to reproduce these results. To retain interactive rendering, usually the detail of a scene is reduced, and so less data needs to be processed. This removal of data, however, may introduce errors, so called artifacts. These artifacts may be distracting for a human spectator when gazing at the display. Thus, the visual quality of the presented scene is reduced. This is counteracted by identifying features of an object that can be removed without introducing artifacts. Most methods utilize geometrical properties, such as distance or shape, to rate the quality of the performed reduction. This information used to generate so called Levels Of Detail (LODs), which are made available to the rendering system. This reduces the detail of an object using the precalculated LODs, e.g. when it is moved into the back of the scene. The appropriate LOD is selected using a metric, and it is replaced with the current displayed version. This exchange must be made smoothly, requiring both LOD-versions to be drawn simultaneously during a transition. Otherwise, this exchange will introduce discontinuities, which are easily discovered by a human spectator. After completion of the transition, only the newly introduced LOD-version is drawn and the previous overhead removed. These LOD-methods usually operate with discrete levels and exploit limitations of both the display and the spectator: the human. Humans are limited in their vision. This ranges from being unable to distinct colors at varying illumination scenarios to the limitation to focus only at one location at a time. Researchers have developed many applications to exploit these limitations to increase the quality of an applied compression. Some popular methods of vision-based compression are MPEG or JPEG. For example, a JPEG compression exploits the reduced sensitivity of humans regarding color and so encodes colors with a lower resolution. Also, other fields, such as auditive perception, allow the exploitation of human limitations. The MP3 compression, for example, reduces the quality of stored frequencies if other frequencies are masking it. For representation of perception various computer models exist. In our rendering scenario, a model is advantageous that cannot be influenced by a human spectator, such as the visual salience or saliency. Saliency is a notion from psycho-physics that determines how an object “pops out” of its surrounding. These outstanding objects (or features) are important for the human vision and are directly evaluated by our Human Visual System (HVS). Saliency combines multiple parts of the HVS and allows an identification of regions where humans are likely to look at. In applications, saliency-based methods have been used to control recursive or progressive rendering methods. Especially expensive display methods, such as pathtracing or global illumination calculations, benefit from a perceptual representation as recursions or calculations can be aborted if only small or unperceivable errors are expected to occur. Yet, saliency is commonly applied to 2d images, and an extension towards 3d objects has only partially been presented. Some issues need to be addressed to accomplish a complete transfer. In this work, we present a smart rendering system that not only utilizes a 3d visual salience model but also applies the reduction in detail directly during rendering. As opposed to normal LOD-methods, this detail reduction is not limited to a predefined set of levels, but rather a dynamic and continuous LOD is created. Furthermore, to apply this reduction in a human-oriented way, a universal function to compute saliency of a 3d object is presented. The definition of this function allows to precalculate and store object-related visual salience information. This stored data is then applicable in any illumination scenario and allows to identify regions of interest on the surface of a 3d object. Unlike preprocessed methods, which generate a view-independent LOD, this identification includes information of the scene as well. Thus, we are able to define a perception-based, view-specific LOD. Performance measures of a prototypical implementation on computers with modern graphic cards achieved interactive frame rates, and several tests have proven the validity of the reduction. The adaptation of an object is performed with a dynamic data structure, the TreeCut. It is designed to operate on hierarchical representations, which define a multi-resolution object. In such a hierarchy, the leaf nodes contain the highest detail while inner nodes are approximations of their respective subtree. As opposed to classical hierarchical rendering methods, a cut is stored and re-traversal of a tree during rendering is avoided. Due to the explicit cut representation, the TreeCut can be altered using only two core operations: refine and coarse. The refine-operation increases detail by replacing a node of the tree with its children while the coarse-operation removes the node along with its siblings and replaces them with their parent node. These operations do not rely on external information and can be performed in a local manner. These only require direct successor or predecessor information. Different strategies to evolve the TreeCut are presented, which adapt the representation using only information given by the current cut. These evaluate the cut by assigning either a priority or a target-level (or bucket) to each cut-node. The former is modelled as an optimization problem that increases the average priority of a cut while being restricted in some way, e.g. in size. The latter evolves the cut to match a certain distribution. This is applied in cases where a prioritization of nodes is not applicable. Both evaluation strategies operate with linear time complexity with respect to the size of the current TreeCut. The data layout is chosen to separate rendering data and hierarchy to enable multi-threaded evaluation and display. The object is adapted over multiple frames while the rendering is not interrupted by the used evaluation strategy. Therefore, we separate the representation of the hierarchy from the rendering data. Due to its design, this overhead imposed to the TreeCut data structure does not influence rendering performance, and a linear time complexity for rendering is retained. The TreeCut is not only limited to alter geometrical detail of an object. The TreeCut has successfully been applied to create a non-photo-realistic stippling display, which draws the object with equal sized points in varying density. In this case the bucket-based evaluation strategy is utilized, which determines the distribution of the cut based on local illumination information. As an alternative, an attention drawing mechanism is proposed, which applies the TreeCut evaluation strategies to define the display style of a notification icon. A combination of external priorities is used to derive the appropriate icon version. An application for this mechanism is a messaging system that accounts for the current user situation. When optimizing an object or scene, perceptual methods allow to account for or exploit human limitations. Therefore, visual salience approaches derive a saliency map, which encodes regions of interest in a 2d map. Rendering algorithms extract importance from such a map and adapt the rendering accordingly, e.g. abort a recursion when the current location is unsalient. The visual salience depends on multiple factors including the view and the illumination of the scene. We extend the existing definition of the 2d saliency and propose a universal function for 3d visual salience: the Bidirectional Saliency Weight Distribution Function (BSWDF). Instead of extracting the saliency from 2d image and approximate 3d information, we directly compute this information using the 3d data. We derive a list of equivalent features for the 3d scenario and add them to the BSWDF. As the BSWDF is universal, also 2d images are covered with the BSWDF, and the calculation of the important regions within images is possible. To extract the individual features that contribute to visual salience, capabilities of modern graphics card in combination with an accumulation method for rendering is utilized. Inspired from point-based rendering methods local features are summed up in a single surface element (surfel) and are compared with their surround to determine whether they “pop out”. These operations are performed with a shader-program that is executed on the Graphics Processing Unit (GPU) and has direct access to the 3d data. This increases processing speed because no transfer of the data is required. After computation, each of these object-specific features can be combined to derive a saliency map for this object. Surface specific information, e.g. color or curvature, can be preprocessed and stored onto disk. We define a sampling scheme to determine the views that need to be evaluated for each object. With these schemes, the features can be interpolated for any view that occurs during rendering, and the according surface data is reconstructed. These sampling schemes compose a set of images in form of a lookup table. This is similar to existing rendering techniques, which extract illumination information from a lookup. The size of the lookup table increases only with the number of samples or the image size used for creation as the images are of equal size. Thus, the quality of the saliency data is independent of the object’s geometrical complexity. The computation of a BSWDF can be performed either on a Central Processing Unit (CPU) or a GPU, and an implementation requires only a few instructions when using a shader program. If the surface features have been stored during a preprocess, a reprojection of the data is performed and combined with the current information of the object. Once the data is available, the computation of the saliency values is done using a specialized illumination model, and a priority for each primitive is extracted. If the GPU is used, the calculated data has to be transferred from the graphics card. We therefore use the “transform feedback” capabilities, which allow high transfer rates and preserve the order of processed primitives. So, an identification of regions of interest based on the currently used primitives is achieved. The TreeCut evaluation strategies are then able to optimize the representation in an perception-based manner. As the adaptation utilizes information of the current scene, each change to an object can result in new visual salience information. So, a self-optimizing system is defined: the Feedback System. The output generated by this system converges towards a perception-optimized solution. To proof the saliency information to be useful, user tests have been performed with the results generated by the proposed Feedback System. We compared a saliency-enhanced object compression to a pure geometrical approach, common for LOD-generation. One result of the tests is that saliency information allows to increase compression even further as possible with the pure geometrical methods. The participants were not able to distinguish between objects even if the saliency-based compression had only 60% of the size of the geometrical reduced object. If the size ratio is greater, saliency-based compression is rated, on average, with higher score and these results have a high significance using statistical tests. The Feedback System extends an 3d object with the capability of self-optimization. Not only geometrical detail but also other properties can be limited and optimized using the TreeCut in combination with a BSWDF. We present a dynamic animation, which utilizes a Software Development Kit (SDK) for physical simulations. This was chosen, on the one hand, to show the universal applicability of the proposed system, and on the other hand, to focus on the connection between the TreeCut and the SDK. We adapt the existing framework, and include the SDK within our design. In this case, the TreeCut-operations not only alter geometrical but also simulation detail. This increases calculation performance because both the rendering and the SDK operate on less data after the reduction has been completed. The selected simulation type is a soft-body simulation. Soft-bodies are deformable in a certain degree but retain their internal connection. An example is a piece of cloth that smoothly fits the underlying surface without tearing apart. Other types are rigid bodies, i.e. idealistic objects that cannot be deformed, and fluids or gaseous materials, which are well suited for point-based simulations. Any of these simulations scales with the number of simulation nodes used, and a reduction of detail increases performance significantly. We define a specialized BSWDF to evaluate simulation specific features, such as motion. The Feedback System then increases detail in highly salient regions, e.g. those with large motion, and saves computation time by reducing detail in static parts of the simulation. So, detail of the simulation is preserved while less nodes are simulated. The incorporation of perception in real-time rendering is an important part of recent research. Today, the HVS is well understood, and valid computer models have been derived. These models are frequently used in commercial and free software, e.g. JPEG compression. Within this thesis, the Tree-Cut is presented to change the LOD of an object in a dynamic and continuous manner. No definition of the individual levels in advance is required, and the transitions are performed locally. Furthermore, in combination with an identification of important regions by the BSWDF, a perceptual evaluation of a 3d object is achieved. As opposed to existing methods, which approximate data from 2d images, the perceptual information is directly acquired from 3d data. Some of this data can be preprocessed if necessary, to defer additional computations during rendering. The Feedback System, created by the TreeCut and the BSWDF, optimizes the representation and is not limited to visual data alone. We have shown with our prototype that interactive frame rates can be achieved with modern hardware, and we have proven the validity of the reductions by performing several user tests. However, the presented system only focuses on specific aspects, and more research is required to capture even more capabilities that a perception-based rendering system can provide