Robust and Efficient Processing Techniques for Staticand Dynamic Geometric Data
Generating high quality geometric representations from real-world objects is a fundamental problem in computer graphics which is motivated bymanifold applications. They comprise image synthesis for movie productionor computer games but also industrial applications such as quality assurance in mechanical engineering, the preservation of cultural heritage and the medical adaptation of prostheses or orthoses. Common demands of these applications on their underlying algorithms are robustness and efficiency. In addition, technological improvements of scanning devices and cameras which allow for the acquisition of new data types such as dynamic geometric data, create novel requirements which rise new challenges for processing algorithms.This dissertation focuses on these aspects and presents differentcontributions for flexible, efficient and robust processing of staticand time-varying geometric data. Two techniques focus on the problemof denoising. A statistical filtering algorithm for point cloud databuilding on non-parametric density estimation is introduced as well asa neighborhood filter for static and time-varying range data which isbased on a novel non-local similarity measure. The third contributionunifies partition of unity decomposition and a global surfacereconstruction algorithm based on the Fast Fourier Transformwhich results in a novel, robust and efficient reconstructiontechnique. Concluding, two flexible and versatile tools for designingscalar fields on meshes are presented which are useful to facilitate acontrollable quadrangular remeshing.