Sampled and Prefiltered Anti-Aliasing on Parallel Hardware

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dc.contributor.authorThomas, Auzinger
dc.date.accessioned2015-12-02T16:06:29Z
dc.date.available2015-12-02T16:06:29Z
dc.date.issued2015-05
dc.description.abstractA fundamental task in computer graphics is the generation of two-dimensional images. Prominent examples are the conversion of text or three-dimensional scenes to formats that can be presented on a raster display. Such a conversion process—often referred to as rasterization or sampling—underlies inherent limitations due to the nature of the output format. This causes not only a loss of information in the rasterization result, which manifests as reduced image sharpness, but also causes corruption of the retained information in form of aliasing artifacts. Commonly observed examples in the final image are staircase artifacts along object silhouettes or Moiré-like patterns. The main focus of this thesis is on the effective removal of such artifacts—a process that is generally referred to as anti-aliasing. This is achieved by removing the offending input information in a filtering step during rasterization. In this thesis, we present different approaches that either minimize computational effort or emphasize output quality. We follow the former objective in the context of an applied scenario from medical visualization. There, we support the investigation of the interiors of blood vessels in complex arrangements by allowing for unrestricted view orientation. Occlusions of overlapping blood vessels are minimized by automatically generating cut-aways with the help of an occlusion cost function. Furthermore, we allow for suitable extensions of the vessel cuts into the surrounding tissue. Utilizing a level of detail approach, these cuts are gradually smoothed with increasing distance from their respective vessels. Since interactive response is a strong requirement for a medical application, we employ fast sample-based anti-aliasing methods in the form of visibility sampling, shading supersampling, and post-process filtering. We then take a step back and develop the theoretical foundations for anti-aliasing methods that follow the second objective of providing the highest degree of output quality. As the main contribution in this context, we present exact anti-aliasing in the form of prefiltering. By computing closed-form solutions of the filter convolution integrals in the continuous domain, we circumvent any issues that are caused by numerical integration and provide mathematically correct results. Together with a parallel hidden-surface elimination, which removes all occluded object parts when rasterizing three-dimensional scenes, we present a ground-truth solution for this setting with exact anti-aliasing. We allow for complex illumination models and perspective-correct shading by combining visibility prefiltering with shading sampling and generate a ground-truth solution for multisampling anti-aliasing. All our aforementioned methods exhibit highly parallel workloads. Throughout the thesis, we present their mapping to massively parallel hardware architectures in the form of graphics processing units. Since our approaches do not map to conventional graphics pipelines, we implement our approach using general-purpose computing concepts. This results in decreased runtime of our methods and makes all of them interactive.en_US
dc.description.sponsorshipAustrian Science Fund (FWF) Projects P20768-N13 and P23700-N23.en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10
dc.language.isoenen_US
dc.subjectanti-aliasingen_US
dc.subjectprefilteringen_US
dc.subjectrasterizationen_US
dc.subjectmedical visualizationen_US
dc.subjectvisibilityen_US
dc.subjectGPGPUen_US
dc.subjectparallelen_US
dc.subjectsamplingen_US
dc.titleSampled and Prefiltered Anti-Aliasing on Parallel Hardwareen_US
dc.typeThesisen_US
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