{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124422"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124422","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"ENS-CVXNet: Convex decomposition of complex scenes","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Jain, Seemandhar"],"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"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["3d Reconstruction","3d Vision","Convex Decomposition","Ray Tracing"],"languages":["en","eng"],"rights":["Copyright 2024 Seemandhar Jain"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124422","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forsyth, David"]},{"key":"dc:creator","label":"Author","values":["Jain, Seemandhar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-30"]},{"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":["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":["3d Reconstruction","3d Vision","Convex Decomposition","Ray Tracing"]}]},{"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 2024 Seemandhar Jain"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124422"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Seemandhar Jain, accepted the attached license on 2024-04-26 at 17:17.","The student, Seemandhar Jain, submitted this Thesis for approval on 2024-04-26 at 17:17.","This Thesis was approved for publication on 2024-04-30 at 15:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20674 on 2024-09-16 at 00:37:11","Generating accurate convex decompositions of indoor scenes from single RGB images is a challenging task with numerous applications in computer graphics. Current state-of-the-art methods employ encoder-decoder neural networks to convert RGB images into a fixed number of simple primitives (e.g., parallelepipeds). However, these approaches have limitations in capturing long-range dependencies and transferring global information, which can impact their accuracy and generalization capability across diverse indoor environments. To address these limitations, we propose ENS-CVXNet, an ensemble approach that leverages the strengths of various convex decomposition techniques and incorporates additional geometric information as summaries. Our core analysis explores well-established algorithms such as VHACD, COACD, and BSP-Net, as well as the integration of global features extracted by PointNet from the input point cloud. By combining multiple models trained with different summaries and configurations, ENS-CVXNet selects the best-performing model for each input image based on its ability to generate accurate depth predictions, evaluated against ground truth depth maps or predictions from state-of-the-art depth predictors. Through extensive experiments and evaluations on the NYUv2 dataset, we demonstrate that ENS-CVXNet outperforms the baseline method, achieving a 20% decrease in AbsRel from 0.093 to 0.0744, and improving the overall precision and quality of convex decomposition for indoor scenes. Our ensemble approach effectively combines the strengths of various techniques, leveraging geometric summaries and adapting to diverse scene characteristics, results in more accurate and robust 3D geometric reconstruction from single RGB images."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["ENS-CVXNet: Convex decomposition of complex scenes"]}]}],"canonical_facts":{"dc:contributor":["Forsyth, David"],"dc:creator":["Jain, Seemandhar"],"dc:date":["2024-05","2024-04-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Seemandhar Jain, accepted the attached license on 2024-04-26 at 17:17.","The student, Seemandhar Jain, submitted this Thesis for approval on 2024-04-26 at 17:17.","This Thesis was approved for publication on 2024-04-30 at 15:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20674 on 2024-09-16 at 00:37:11","Generating accurate convex decompositions of indoor scenes from single RGB images is a challenging task with numerous applications in computer graphics. Current state-of-the-art methods employ encoder-decoder neural networks to convert RGB images into a fixed number of simple primitives (e.g., parallelepipeds). However, these approaches have limitations in capturing long-range dependencies and transferring global information, which can impact their accuracy and generalization capability across diverse indoor environments. To address these limitations, we propose ENS-CVXNet, an ensemble approach that leverages the strengths of various convex decomposition techniques and incorporates additional geometric information as summaries. Our core analysis explores well-established algorithms such as VHACD, COACD, and BSP-Net, as well as the integration of global features extracted by PointNet from the input point cloud. By combining multiple models trained with different summaries and configurations, ENS-CVXNet selects the best-performing model for each input image based on its ability to generate accurate depth predictions, evaluated against ground truth depth maps or predictions from state-of-the-art depth predictors. Through extensive experiments and evaluations on the NYUv2 dataset, we demonstrate that ENS-CVXNet outperforms the baseline method, achieving a 20% decrease in AbsRel from 0.093 to 0.0744, and improving the overall precision and quality of convex decomposition for indoor scenes. Our ensemble approach effectively combines the strengths of various techniques, leveraging geometric summaries and adapting to diverse scene characteristics, results in more accurate and robust 3D geometric reconstruction from single RGB images."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124422"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Seemandhar Jain"],"dc:subject":["3d Reconstruction","3d Vision","Convex Decomposition","Ray Tracing"],"dc:title":["ENS-CVXNet: Convex decomposition of complex scenes"],"dc:type":["text"],"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":"2026-07-22T22:25:00Z"}