Toward Controllable and Robust Surface Reconstruction from Spatial Curves

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science & Engineering

Year dc:date.available

2019

Author dc:creator

• Huang, Zhiyang

Contributors dc:contributor

• Tao Ju
• Nathan Carr, Ayan Chakrabarti, Ulugbek Kamilov, Caitlin Kelleher,

• Computational Geometry
• Curves
• Surface Reconstruction
• Computer Engineering
• Computer Sciences

Statement dc:rights

• I have not registered my thesis with the U.S. Copyright Office, but intend to later.

Language dc:language

English (en)

Identifier

https://openscholarship.wustl.edu/eng_etds/448

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:eng_etds-1493