Open Access

The Render Cache [1,2] allows the interactive display of very large scenes, rendered with complex global illumination models, by decoupling camera movement from the costly scene sampling process. In this paper, the distributed execution of the individual components of the Render Cache on a PC cluster is shown to be a viable alternative to the shared memory implementation.As the processing power of an entire node can be dedicated to a single component, more advanced algorithms may be examined. Modular functional units also lead to increased flexibility, useful in research as well as industrial applications.We introduce a new strategy for view-driven scene sampling, as well as support for multiple camera viewpoints generated from the same cache. Stereo display and a CAVE multi-camera setup have been implemented.The use of the highly portable and inter-operable CORBA networking API simplifies the integration of most existing pixel-based renderers. So far, three renderers (C++ and Java) have been adapted to function within our framework.

"Visual Computing" (VC) fasst als hochgradig aktuelles Forschungsgebiet verschiedene Bereiche der Informatik zusammen, denen gemeinsam ist, dass sie sich mit der Erzeugung und Auswertung visueller Signale befassen. Im Fachbereich Informatik der FH Bonn-Rhein-Sieg nimmt dieser Aspekt eine zentrale Rolle in Lehre und Forschung innerhalb des Studienschwerpunktes Medieninformatik ein. Drei wesentliche Bereiche des VC werden besonders in diversen Lehreinheiten und verschiedenen Projekten vermittelt: Computergrafik, Bildverarbeitung und Hypermedia-Anwendungen. Die Aktivitäten in diesen drei Bereichen fließen zusammen im Kontext immersiver virtueller Visualisierungsumgebungen.

Advances in computer graphics enable us to create digital images of astonishing complexity and realism. However, processing resources are still a limiting factor. Hence, many costly but desirable aspects of realism are often not accounted for, including global illumination, accurate depth of field and motion blur, spectral effects, etc. especially in real‐time rendering. At the same time, there is a strong trend towards more pixels per display due to larger displays, higher pixel densities or larger fields of view. Further observable trends in current display technology include more bits per pixel (high dynamic range, wider color gamut/fidelity), increasing refresh rates (better motion depiction), and an increasing number of displayed views per pixel (stereo, multi‐view, all the way to holographic or lightfield displays). These developments cause significant unsolved technical challenges due to aspects such as limited compute power and bandwidth. Fortunately, the human visual system has certain limitations, which mean that providing the highest possible visual quality is not always necessary. In this report, we present the key research and models that exploit the limitations of perception to tackle visual quality and workload alike. Moreover, we present the open problems and promising future research targeting the question of how we can minimize the effort to compute and display only the necessary pixels while still offering a user full visual experience.

In western societies a huge percentage of the population suffers from some kind of back pain at least once in their life. There are several approaches addressing back pain by postural modifications. Postural training and activity can be tracked by various wearable devices most of which are based on accelerometers. We present research on the accuracy of accelerometer-based posture measurements. To this end, we took simultaneous recordings using an optical motion capture system and a system consisting of five accelerometers in three different settings: On a test robot, in a template, and on actual human backs. We compare the accelerometer-based spine curve reconstruction against the motion capture data. Results show that tilt values from the accelerometers are captured highly accurate, and the spine curve reconstruction works well.

Improving data acquisition techniques and rising computational power keep producing more and larger data sets that need to be analyzed. These data sets usually do not fit into a GPU's memory. To interactively visualize such data with direct volume rendering, sophisticated techniques for problem domain decomposition, memory management and rendering have to be used. The volume renderer Volt is used to show how CUDA is efficiently utilised to manage the volume data and a GPU's memory with the aim of low opacity volume renderings of large volumes at interactive frame rates.

In contrast to projection-based systems, large, high resolution multi-display systems offer a high pixel density on a large visualization area. This enables users to step up to the displays and see a small but highly detailed area. If the users move back a few steps they don't perceive details at pixel level but will instead get an overview of the whole visualization. Rendering techniques for design evaluation and review or for visualizing large volume data (e.g. Big Data applications) often use computationally expensive ray-based methods. Due to the number of pixels and the amount of data, these methods often do not achieve interactive frame rates. A view direction based (VDB) rendering technique renders the user's central field of view in high quality whereas the surrounding is rendered with a level-of-detail approach depending on the distance to the user's central field of view. This approach mimics the physiology of the human eye and conserves the advantage of highly detailed information when standing close to the multi-display system as well as the general overview of the whole scene. In this paper we propose a prototype implementation and evaluation of a focus-based rendering technique based on a hybrid ray tracing/sparse voxel octree rendering approach.