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

Fast and interactive ray-based rendering

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDespite their age, ray-based rendering methods are still a very active field of research with many challenges when it comes to interactive visualization. In this thesis, we present our work on Guided High-Quality Rendering, Foveated Ray Tracing for Head Mounted Displays and Hash-based Hierarchical Caching and Layered Filtering. Our system for Guided High-Quality Rendering allows for guiding the sampling rate of ray-based rendering methods by a user-specified Region of Interest (RoI). We propose two interaction methods for setting such an RoI when using a large display system and a desktop display, respectively. This makes it possible to compute images with a heterogeneous sample distribution across the image plane. Using such a non-uniform sample distribution, the rendering performance inside the RoI can be significantly improved in order to judge specific image features. However, a modified scheduling method is required to achieve sufficient performance. To solve this issue, we developed a scheduling method based on sparse matrix compression, which has shown significant improvements in our benchmarks. By filtering the sparsely sampled image appropriately, large brightness variations in areas outside the RoI are avoided and the overall image brightness is similar to the ground truth early in the rendering process. When using ray-based methods in a VR environment on head-mounted display de vices, it is crucial to provide sufficient frame rates in order to reduce motion sickness. This is a challenging task when moving through highly complex environments and the full image has to be rendered for each frame. With our foveated rendering sys tem, we provide a perception-based method for adjusting the sample density to the user’s gaze, measured with an eye tracker integrated into the HMD. In order to avoid disturbances through visual artifacts from low sampling rates, we introduce a reprojection-based rendering pipeline that allows for fast rendering and temporal accumulation of the sparsely placed samples. In our user study, we analyse the im pact our system has on visual quality. We then take a closer look at the recorded eye tracking data in order to determine tracking accuracy and connections between different fixation modes and perceived quality, leading to surprising insights. For previewing global illumination of a scene interactively by allowing for free scene exploration, we present a hash-based caching system. Building upon the concept of linkless octrees, which allow for constant-time queries of spatial data, our frame work is suited for rendering such previews of static scenes. Non-diffuse surfaces are supported by our hybrid reconstruction approach that allows for the visualization of view-dependent effects. In addition to our caching and reconstruction technique, we introduce a novel layered filtering framework, acting as a hybrid method between path space and image space filtering, that allows for the high-quality denoising of non-diffuse materials. Also, being designed as a framework instead of a concrete filtering method, it is possible to adapt most available denoising methods to our layered approach instead of relying only on the filtering of primary hitpoints

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

Learning Representations for Controllable Image Restoration

Deep Convolutional Neural Networks have sparked a renaissance in all the sub-fields of computer vision. Tremendous progress has been made in the area of image restoration. The research community has pushed the boundaries of image deblurring, super-resolution, and denoising. However, given a distorted image, most existing methods typically produce a single restored output. The tasks mentioned above are inherently ill-posed, leading to an infinite number of plausible solutions. This thesis focuses on designing image restoration techniques capable of producing multiple restored results and granting users more control over the restoration process. Towards this goal, we demonstrate how one could leverage the power of unsupervised representation learning. Image restoration is vital when applied to distorted images of human faces due to their social significance. Generative Adversarial Networks enable an unprecedented level of generated facial details combined with smooth latent space. We leverage the power of GANs towards the goal of learning controllable neural face representations. We demonstrate how to learn an inverse mapping from image space to these latent representations, tuning these representations towards a specific task, and finally manipulating latent codes in these spaces. For example, we show how GANs and their inverse mappings enable the restoration and editing of faces in the context of extreme face super-resolution and the generation of novel view sharp videos from a single motion-blurred image of a face. This thesis also addresses more general blind super-resolution, denoising, and scratch removal problems, where blur kernels and noise levels are unknown. We resort to contrastive representation learning and first learn the latent space of degradations. We demonstrate that the learned representation allows inference of ground-truth degradation parameters and can guide the restoration process. Moreover, it enables control over the amount of deblurring and denoising in the restoration via manipulation of latent degradation features

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

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