{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117609"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117609","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Convex decomposition of indoor scenes","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Vavilala, Vaibhav"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Forsyth, David A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Deep Learning","Tensorflow","Scene Parsing"],"languages":["en","eng"],"rights":["Copyright 2022 Vaibhav Vavilala"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117609","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forsyth, David A"]},{"key":"dc:creator","label":"Author","values":["Vavilala, Vaibhav"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Deep Learning","Tensorflow","Scene Parsing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Vaibhav Vavilala"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117609"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Vaibhav Vavilala, accepted the attached license on 2022-12-08 at 13:21.","The student, Vaibhav Vavilala, submitted this Thesis for approval on 2022-12-08 at 13:52.","This Thesis was approved for publication on 2022-12-08 at 14:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18780 on 2023-04-12 at 11:37:40","We describe a method to parse a complex, cluttered indoor scene into primitives which offer a parsimonious abstraction of scene structure. Our primitives are simple convexes. Our method uses a learned regression procedure to predict a parse into a fixed number of convexes from a depth map, but does not require labeled data. The result is then polished with a descent method which adjusts the convexes to produce a very good fit, and greedily removes superfluous primitives. Because the entire scene is parsed, we can evaluate using traditional depth and normal error metrics. Our evaluation procedure demonstrates that the error in our primitive representation is comparable to that of predicting depth from a single image. We show that our primitives segment the scene well, without ever having seen segmentation labels. Finally, we show that our primitive representation is stable under change of viewpoint."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Convex decomposition of indoor scenes"]}]}],"canonical_facts":{"dc:contributor":["Forsyth, David A"],"dc:creator":["Vavilala, Vaibhav"],"dc:date":["2022-12","2022-12-08"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Vaibhav Vavilala, accepted the attached license on 2022-12-08 at 13:21.","The student, Vaibhav Vavilala, submitted this Thesis for approval on 2022-12-08 at 13:52.","This Thesis was approved for publication on 2022-12-08 at 14:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18780 on 2023-04-12 at 11:37:40","We describe a method to parse a complex, cluttered indoor scene into primitives which offer a parsimonious abstraction of scene structure. Our primitives are simple convexes. Our method uses a learned regression procedure to predict a parse into a fixed number of convexes from a depth map, but does not require labeled data. The result is then polished with a descent method which adjusts the convexes to produce a very good fit, and greedily removes superfluous primitives. Because the entire scene is parsed, we can evaluate using traditional depth and normal error metrics. Our evaluation procedure demonstrates that the error in our primitive representation is comparable to that of predicting depth from a single image. We show that our primitives segment the scene well, without ever having seen segmentation labels. Finally, we show that our primitive representation is stable under change of viewpoint."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117609"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Vaibhav Vavilala"],"dc:subject":["Deep Learning","Tensorflow","Scene Parsing"],"dc:title":["Convex decomposition of indoor scenes"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}