{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83421"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83421","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"D4ar- 4 Dimensional Augmented Reality - Models or Automation and Interactive Visualization of Construction Progress Monitoring","abstract":"The resulting D4AR models overcome the challenges of current progress monitoring practice and further enable AEC professionals to conduct various decision-making tasks in virtual environments rather than the real world where it is time-consuming and costly. To that extent, the underlying hypotheses and algorithms for generation of integrated 4D as-built and as-planned models as well as automated progress monitoring are presented. Promising experimental results are demonstrated on several challenging building construction datasets under different lighting conditions and sever occlusions. This marks the D 4AR modeling approach to be the first of its kind to take advantage of existing construction photo collections for the purpose of automated monitoring and visualization of performance deviations. Unlike other methods that focus on application of laser scanners or time-lapse photography, this approach is able to use existing information without adding burden of explicit data collection on project management and reports competitive accuracies compared to those reported with laser scanners especially in presence of sever occlusions.","abstract_html":"The resulting D4AR models overcome the challenges of current progress monitoring practice and further enable AEC professionals to conduct various decision-making tasks in virtual environments rather than the real world where it is time-consuming and costly. To that extent, the underlying hypotheses and algorithms for generation of integrated 4D as-built and as-planned models as well as automated progress monitoring are presented. Promising experimental results are demonstrated on several challenging building construction datasets under different lighting conditions and sever occlusions. This marks the D 4AR modeling approach to be the first of its kind to take advantage of existing construction photo collections for the purpose of automated monitoring and visualization of performance deviations. Unlike other methods that focus on application of laser scanners or time-lapse photography, this approach is able to use existing information without adding burden of explicit data collection on project management and reports competitive accuracies compared to those reported with laser scanners especially in presence of sever occlusions.","abstract_has_math":false,"creators":["Golparvar Fard, Mani"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Liang Liu","Peña-Mora, Feniosky","Silvio Savarese"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-22T22:26:21Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3601058"],"render_values":[{"text":"(MiAaPQ)AAI3601058","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83421","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang Liu","Peña-Mora, Feniosky","Silvio Savarese"]},{"key":"dc:creator","label":"Author","values":["Golparvar Fard, Mani"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2010","2015-09-25T21:04:48Z","10000-01-01"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"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/2142/83421","(MiAaPQ)AAI3601058"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The resulting D4AR models overcome the challenges of current progress monitoring practice and further enable AEC professionals to conduct various decision-making tasks in virtual environments rather than the real world where it is time-consuming and costly. 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