Browsing by Subject "Computer Graphics"

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    Expressive spatial Interfaces for scientific visualization and artistic 3D Modeling
    (2014-07) Jackson, Bret Lowell
    This dissertation explores spatial human-computer interaction techniques to improve the control and expressiveness of 3D interactions. It investigates the requirements necessary for users to work more effectively with next-generation spatial interfaces, specifically in the context of scientific visualization and artistic 3D modeling where users currently struggle to express complex spatial concepts.Examples of expressive spatial interfaces are presented and evaluated. In particular, we present new techniques for combining multi-touch with free-hand gestures for navigating visualizations and performing 3D surface modeling operations. Techniques for selecting and filtering volumetric data using lightweight props as well as active force-feedback are also introduced. Additionally, we present a spatial modeling interface for artistic 3D modeling using contextual interpretation of the user's input. Several conclusions are drawn from these examples. Rich, parallel input and output streams enabled by recent advances in tracking hardware are particularly important for expressive interfaces. Additionally, there is a need for tighter integration of two and three-dimensional data and input. Contextual interpretation of user input enables users to specify more complex 3D concepts. Finally, many spatial tasks require immediate feedback to be expressive.The primary contribution of this dissertation is a new class of interaction techniques called Expressive Spatial Interfaces that advance beyond the limited pointing and rotating interactions common in current-generation spatial interfaces. The techniques presented here can have a powerful impact on shaping the future of expressive spatial human-computer interaction with 3D graphics.
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    Rendering of Teeth and Dental Restorations using Robust Statistical Estimation Techniques
    (2016-02) Jung, Jin Woo
    Robust image estimation and progressive rendering techniques are introduced, and these novel methods are used to simulate the appearance of teeth and dental restorations. The realistic visualization of these translucent objects is essential for computer-aided processes in the field of dentistry, because a successful dental treatment is dependent on the recovery not only of the tooth's function, but also its appearance. However, due to the heterogeneity of the tooth structure and the coupled subsurface scatterings that this causes, simulating the translucency of these objects presents a difficult computational challenge. A Monte-Carlo ray tracing system is employed to model the complex interactions of light within the material and to develop the robust image estimation and progressive rendering techniques. Because low probability samples are infrequently encountered in an image, for standard Monte-Carlo estimation these samples can become noise. Robust image estimation techniques are suggested as a way to suppress these low probability samples, and it is demonstrated that for a given sample size robust estimation techniques can produce less noisy renderings. In other words, the sample size necessary to satisfy a certain user requirement will decrease, and an improvement in rendering speed can be obtained. The robust estimation techniques are discussed in both pixel and image space, and their statistical analysis is provided. This analysis determines the inclusion rate for sample probabilities and is thus able to specify the sample probability thresholds necessary to discard or include samples. The statistical analysis also makes it possible to determine the performance boundaries in terms of the number of disclosed low probability samples in an image; as a result, a sample size for a given user requirement can be identified. A progressive approach for rendering translucent objects based on volume photon mapping is also presented. Because conventional volume photon mapping requires long preprocessing to build up a complete volume photon map, it is not able to support progressive rendering. Even worse, due to the limited memory space in a given computer system, the rendering results suffer from a potentially incorrect volume photon map. Progressive volume photon mapping uses a subset of volume photons for rendering, so it provides a high frame rate for preview rendering. In addition, by recycling the volume photons used for previous image estimation, progressive volume photon mapping does not suffer from memory restriction. It is therefore able to use a virtually unlimited number of volume photons and this makes exact rendering plausible. Although these methods were developed to realistically visualize teeth and dental restorations, they are effective in any rendering situation that suffers from noise, restricted computational performance, and limited memory space; as a consequence, these procedures are expected to be useful for many other types of realistic image synthesis including motion picture special effects and video games. The statistical interpretation developed for robust estimation is based on the pixel radiance sample probability. This allows the image synthesis sampling problem to be studied in a manner similar to how it would be treated in other established fields of science and engineering: in terms of the statistical properties of the signal to be sampled. This approach can provide the groundwork for further stochastic analyses in the context of computer graphic rendering.