Downsampling methods for medical datasets

Volume visualization software usually has to deal with datasets that are larger than the GPUs may hold. This is especially true in one of the most popular application scenarios: medical visualization. Typically, medical datasets are available for different personnel, but only radiologists have high-end systems that are able to cope with large data. For the rest of physicians, usually low-end systems are only available. As a result, most volume rendering packages downsample the data prior to uploading to the GPU. The most common approach consists in performing iterative subsampling along the longest axis, until the model fits inside the GPU memory. This causes important information loss that affects the final rendering. Some cleverer techniques may be developed to preserve the volumetric information. In this paper we explore the quality of different downsampling methods and present a new approach that produces smooth lower-resolution representations, yet still preserves small features that are prone to disappear with other approaches.Peer ReviewedPostprint (published version

Doctor of Philosophy

dissertationRay tracing presents an efficient rendering algorithm for scientific visualization using common visualization tools and scales with increasingly large geometry counts while allowing for accurate physically-based visualization and analysis, which enables enhanced rendering and new visualization techniques. Interactivity is of great importance for data exploration and analysis in order to gain insight into large-scale data. Increasingly large data sizes are pushing the limits of brute-force rasterization algorithms present in the most widely-used visualization software. Interactive ray tracing presents an alternative rendering solution which scales well on multicore shared memory machines and multinode distributed systems while scaling with increasing geometry counts through logarithmic acceleration structure traversals. Ray tracing within existing tools also provides enhanced rendering options over current implementations, giving users additional insight from better depth cues while also enabling publication-quality rendering and new models of visualization such as replicating photographic visualization techniques

Exploiting spatial and temporal coherence in GPU-based volume rendering

Effizienz spielt eine wichtige Rolle bei der Darstellung von Volumendaten, selbst wenn leistungsstarke Grafikhardware zur Verfügung steht, da steigende Datensatzgrößen und höhere Anforderungen an Visualisierungstechniken Fortschritte bei Grafikprozessoren ausgleichen. In dieser Dissertation wird untersucht, wie räumliche und zeitliche Kohärenz in Volumendaten zur Optimierung von Volumenrendering genutzt werden kann. Es werden mehrere neue Ansätze für statische und zeitvariante Daten eingeführt, die verschieden Arten von Kohärenz in verschiedenen Stufen der Volumenrendering-Pipeline ausnutzen. Zu den vorgestellten Beschleunigungstechniken gehört Empty Space Skipping mittels Occlusion Frustums, eine auf Slabs basierende Cachestruktur für Raycasting und ein verlustfreies Kompressionsscheme für zeitvariante Daten. Die Algorithmen wurden zur Verwendung mit GPU-basiertem Volumen-Raycasting entworfen und nutzen die Fähigkeiten moderner Grafikprozessoren, insbesondere Stream Processing. Efficiency is a key aspect in volume rendering, even if powerful graphics hardware is employed, since increasing data set sizes and growing demands on visualization techniques outweigh improvements in graphics processor performance. This dissertation examines how spatial and temporal coherence in volume data can be used to optimize volume rendering. Several new approaches for static as well as for time-varying data sets are introduced, which exploit different types of coherence in different stages of the volume rendering pipeline. The presented acceleration algorithms include empty space skipping using occlusion frustums, a slab-based cache structure for raycasting, and a lossless compression scheme for time-varying data. The algorithms were designed for use with GPU-based volume raycasting and to efficiently exploit the features of modern graphics processors, especially stream processing

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