May 25, 2018
Jolley Hall, Room 309
"Some Algorithms for Surface Reconstruction from Spatial Curves"
Adviser: Tao Ju
Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying algorithm of surface reconstruction to real-world cross-sections data. Lastly, we propose to use deep learning for providing a robust pipeline of generating surfaces from noise and incomplete curves.