The motivation for this work is to study methods of estimating appropriate level-of-detail (LoD) object models by quantifying appearance errors prior to image synthesis. Visualization systems have been developed that employ LoD objects, however, the criteria are often based on heuristics that restrict the form of the object model and rendering method. Also, object illumination is not considered in the LoD selection. This dissertation proposes an image-based scene learning pre-process to determine appropriate LoD for each object in a scene. Scene learning employs sample images of an object, from many views and with a range of geometric representations, to produce a profile of the LoD image error as a function of viewing distance. Signal processing techniques are employed to quantify how images change with respect to object model resolution, viewing distance, and lighting direction. A frequency-space analysis is presented which includes use of the vision system?s contrast sensitivity to evaluate perceptible image differences with error metrics. The initial development of scene learning is directed to sampling the object?s appearance as a function of viewing distance and object geometry in scene space. A second phase allows local lighting to be incorporated in the scene learning pre-process. Two methods for re-lighting are presented that differ in accuracy and overhead; both allow properties of an object?s image to be computed without rendering. In summary, full-resolution objects pro-duce the best image since the 3D scene is as real as possible. A less realistic 3D scene with simpler objects produces a different appearance in an image, but by what amount? My the-sis is such can be had. Namely that object fidelity in the 3D scene can be loosened further than has previously been shown without introducing significant appearance change in an object and that the relationship between 3D object realism and appearance can be expressed quantitatively.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4943 |
| Date | 10 May 2003 |
| Creators | Scoggins, Randy Keith |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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