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• W1509570622 abstract "This thesis describes methods for simplifying complex polygonal surfaces and optimizing their approximations. Such densely sampled surfaces often arise from range scanning, isosurface extraction, and approximation of smooth curved surfaces. Because of their complexity, these models must often be reduced by orders of magnitude using simplification to allow interactive display, efficient storage and transmission, and faster processing. In this thesis, I describe three different simplification algorithms, each with a different application domain. The first method, memoryless simplification, uses simple geometric heuristics based on changes in volume and area for ordering a sequence of edge collapses and optimizing the positions of the resulting vertices. In order to handle extremely large models that are too complex to fit in main memory, I propose a method for out-of-core simplification. This method is able to process an arbitrarily large mesh in a single pass, while building up an in-core representation of a greatly simplified version. For many computer graphics applications, preserving the visual appearance of the original model during simplification is the most important goal. I present a method for image-driven simplification that uses rendered images of an object and an image metric to directly capture and preserve the visual appearance, which allows the effects of surface attributes, visibility, and rendering parameters and viewing conditions to be explicitly accounted for during simplification. This method produces models of higher visual quality than existing geometry-based algorithms. Based on the image-driven method for simplification, I have developed a new method for mesh optimization which accepts an already simplified mesh and attempts to find a mesh with the same number of vertices that is more globally optimal with respect to an image metric. This method performs multidimensional simultaneous optimization of vertex positions and surface attributes, while also making local connectivity changes to the mesh where appropriate. Finally, I describe a new image metric for image-driven simplification and optimization that is motivated by human visual perception. This metric draws upon previous work in vision and psychology, but has been designed mainly for use in polygonal simplification. (Abstract shortened by UMI.)" @default.
• W1509570622 created "2016-06-24" @default.
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• W1509570622 date "2000-01-01" @default.
• W1509570622 modified "2023-09-26" @default.
• W1509570622 title "Model simplification using image and geometry-based metrics" @default.
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