Abstract
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 algorithms of surface reconstruction to real-world cross-sections data. Lastly, we propose the Variational Implicit Point Set Surface which allows us to robustly handle noisy, sparse and non-uniform inputs, such as samples from spatial curves.
Committee Chair
Tao Ju
Committee Members
Nathan Carr, Ayan Chakrabarti, Ulugbek Kamilov, Caitlin Kelleher,
Degree
Doctor of Philosophy (PhD)
Author's Department
Computer Science & Engineering
Document Type
Dissertation
Date of Award
Spring 5-15-2019
Language
English (en)
DOI
https://doi.org/7936/fz30-pq36
Author's ORCID
http://orcid.org/0000-0002-9470-4353
Recommended Citation
Huang, Zhiyang, "Toward Controllable and Robust Surface Reconstruction from Spatial Curves" (2019). McKelvey School of Engineering Theses & Dissertations. 448.
The definitive version is available at https://doi.org/7936/fz30-pq36
Comments
Permanent URL: https://doi.org/7936/fz30-pq36