{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108150"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108150","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"3D point cloud learning: a survey and a toolbox","abstract":"The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. However, while deep learning methods obtained remarkable success in 2D image tasks, deep models on point clouds still suffer from unique challenges in processing unstructured points with deep neural networks. This thesis reviews milestones and recent progress in different areas of point cloud learning, and proposes a uniform toolbox to help performance evaluation across models.","abstract_html":"The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. However, while deep learning methods obtained remarkable success in 2D image tasks, deep models on point clouds still suffer from unique challenges in processing unstructured points with deep neural networks. This thesis reviews milestones and recent progress in different areas of point cloud learning, and proposes a uniform toolbox to help performance evaluation across models.","abstract_has_math":false,"creators":["Lu, Haoming"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Shi, Humphrey"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T23:58:37Z","date_published":"2020-08-26T23:58:37Z","updated_at":"2026-07-22T22:24:47Z","subjects":["3D Point Cloud, Deep Learning"],"languages":["en"],"rights":["Copyright 2020 Haoming Lu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108150","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shi, Humphrey"]},{"key":"dc:creator","label":"Author","values":["Lu, Haoming"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T23:58:37Z","2022-08-26T23:58:55Z","2020-05-11","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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 Point Cloud, Deep Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Haoming Lu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108150"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. 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However, while deep learning methods obtained remarkable success in 2D image tasks, deep models on point clouds still suffer from unique challenges in processing unstructured points with deep neural networks. This thesis reviews milestones and recent progress in different areas of point cloud learning, and proposes a uniform toolbox to help performance evaluation across models.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01","The student, Haoming Lu, accepted the attached license on 2020-05-05 at 09:58.","The student, Haoming Lu, submitted this Thesis for approval on 2020-05-05 at 10:07.","This Thesis was approved for publication on 2020-05-11 at 07:03.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15192 on 2020-08-25 at 17:29:37","Made available in DSpace on 2020-08-26T23:58:37Z (GMT). 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