Sampled and Prefiltered Anti-Aliasing on Parallel Hardware

Information

• Publication Type: PhD-Thesis
• Workgroup(s)/Project(s):
  • Rendering and Modeling
• Date: 2015
• Date (Start): 2010
• Date (End): 2015
• 1st Reviewer: Jirí Bittner
• 2nd Reviewer: Michael Wimmer
• Rigorosum: 12. May 2015
• First Supervisor: Michael Wimmer

Abstract

A 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 Moire-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.

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BibTeX

@phdthesis{auzinger-2015-phd,
  title =      "Sampled and Prefiltered Anti-Aliasing on Parallel Hardware",
  author =     "Thomas Auzinger",
  year =       "2015",
  abstract =   "A 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 Moire-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.",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2015/auzinger-2015-phd/",
}