Adaptive Surface Reconstruction for 3D CT-Data based on Geometric Modelling

Thesis Abstract

Aim of this thesis is to develop an efficient and accurate method to extract material interfaces from high resolution volume images. It is planned to apply this method to computed tomography scans of oil and air filters.

The main challenge of these images is that they contain many large, flat areas as well as some highly curved regions. Currently used algorithms either produce large triangulations, which cannot be processed by subsequent simulation software, or they condone a substantial loss of accuracy.

As a new approach, this thesis will explore the use of B-spline tensor product surfaces to represent isosurfaces. The computation of B-spline surfaces from volume images is closely related to approximating point clouds with B-splines. Therefore, methods for point cloud approximation will serve as template. Once an accurate B-spline isosurface can be computed, an adaptive triangulation needs to be generated. If additional geometric data is available (e.g. CAD models that where used to fabricate the scanned objects), it might also be worth to investigate whether it can be used to improve the quality of the results.