Abstract
dc:descriptionModelling by example has arisen as a powerful paradigm for reducing the artistic skill required for computer graphics. Instead of relying on the user's own modelling skills, a system that models by example allows users to reuse the work of others. To date, modelling by example, also known as data-driven modelling, has been mostly limited to the image domain. In this work, we develop a number of methods for modelling 3D objects by example. Jump maps provide fast and flexible reuse of texture imagery in object modelling. Geodesic fans extend the local statistical techniques forming the basis for traditional image-based data-driven methods to 3D surfaces, and directly enable flexible reuse of existing 3D surfaces. We also apply data-driven methods to augment surface editing capabilities, providing new tools for rapid geometry or texture editing and sketch-based 3D object modelling. Finally, as a fundamental operation of any data-driven modelling system is the selection of example data, we develop novel methods for selecting regions or mattes from 3D objects. The resulting methods are very fast, intuitive, and easy to use, and, as selection is a truly fundamental modelling operation, have wide applicability. Thus, we have improved the overall pipeline for modelling objects by example, from sample selection and localization, through novel algorithms for reuse of surface data, to final editing of results.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Zelinka, Stephen David
- Contributors dc:contributor
-
- Michael Garland
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3182436
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81671