{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81671"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81671","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Object Modelling by Example","abstract":"Modelling 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.","abstract_html":"Modelling 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.","abstract_has_math":false,"creators":["Zelinka, Stephen David"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Michael Garland"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:19:58Z","date_published":"2015-09-25T20:19:58Z","updated_at":"2026-07-22T22:26:16Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3182436"],"render_values":[{"text":"(MiAaPQ)AAI3182436","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81671","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Michael Garland"]},{"key":"dc:creator","label":"Author","values":["Zelinka, Stephen David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:19:58Z","10000-01-01","2005"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81671","(MiAaPQ)AAI3182436"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Modelling by example has arisen as a powerful paradigm for reducing the artistic skill required for computer graphics. 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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.","Made available in DSpace on 2015-09-25T20:19:58Z (GMT). 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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. 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