{"id":"https://openalex.org/W3000575179","doi":"https://doi.org/10.1109/lra.2020.2965389","title":"Learning to Optimally Segment Point Clouds","display_name":"Learning to Optimally Segment Point Clouds","publication_year":2020,"publication_date":"2020-01-09","ids":{"openalex":"https://openalex.org/W3000575179","doi":"https://doi.org/10.1109/lra.2020.2965389","mag":"3000575179"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2020.2965389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lra.2020.2965389","pdf_url":"https://ieeexplore.ieee.org/ielx7/7083369/8932682/08954778.pdf","source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/7083369/8932682/08954778.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101509590","display_name":"Peiyun Hu","orcid":"https://orcid.org/0000-0002-8498-0653"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peiyun Hu","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":"https://orcid.org/0000-0002-8498-0653","affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037048516","display_name":"David Held","orcid":"https://orcid.org/0000-0003-0537-1508"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Held","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":"https://orcid.org/0000-0003-0537-1508","affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004353237","display_name":"Deva Ramanan","orcid":"https://orcid.org/0009-0008-9180-8983"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deva Ramanan","raw_affiliation_strings":["Argo AI, Pittsburgh, USA","Robotics Institute, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Argo AI, Pittsburgh, USA","institution_ids":[]},{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.1346,"has_fulltext":true,"cited_by_count":20,"citation_normalized_percentile":{"value":0.72705304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"5","issue":"2","first_page":"875","last_page":"882"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.821495771408081},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7484404444694519},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.6883482933044434},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6665648221969604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6549311280250549},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6449198722839355},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.507276713848114},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.47357046604156494},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.46766746044158936},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44778522849082947},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4297111928462982},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41521745920181274},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3688724637031555},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20566925406455994},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10856208205223083},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1053631603717804}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.821495771408081},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7484404444694519},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.6883482933044434},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6665648221969604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6549311280250549},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6449198722839355},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.507276713848114},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47357046604156494},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.46766746044158936},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44778522849082947},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4297111928462982},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41521745920181274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3688724637031555},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20566925406455994},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10856208205223083},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1053631603717804},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2020.2965389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lra.2020.2965389","pdf_url":"https://ieeexplore.ieee.org/ielx7/7083369/8932682/08954778.pdf","source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/lra.2020.2965389","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lra.2020.2965389","pdf_url":"https://ieeexplore.ieee.org/ielx7/7083369/8932682/08954778.pdf","source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.46000000834465027,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3000575179.pdf","grobid_xml":"https://content.openalex.org/works/W3000575179.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W800520748","https://openalex.org/W1993846506","https://openalex.org/W1994114209","https://openalex.org/W1999478155","https://openalex.org/W2055207897","https://openalex.org/W2074658631","https://openalex.org/W2121806728","https://openalex.org/W2121947440","https://openalex.org/W2128715914","https://openalex.org/W2132360065","https://openalex.org/W2143516773","https://openalex.org/W2148852318","https://openalex.org/W2152845613","https://openalex.org/W2154458843","https://openalex.org/W2156721269","https://openalex.org/W2295581249","https://openalex.org/W2406067508","https://openalex.org/W2414551140","https://openalex.org/W2473418274","https://openalex.org/W2555618208","https://openalex.org/W2594519801","https://openalex.org/W2601243251","https://openalex.org/W2778026434","https://openalex.org/W2798930779","https://openalex.org/W2897529137","https://openalex.org/W2902302021","https://openalex.org/W2949708697","https://openalex.org/W2953941229","https://openalex.org/W2956985883","https://openalex.org/W2962928871","https://openalex.org/W2963121255","https://openalex.org/W2963231572","https://openalex.org/W2963400571","https://openalex.org/W2963727135","https://openalex.org/W2964062501","https://openalex.org/W2964347345","https://openalex.org/W2968296999","https://openalex.org/W2979750740","https://openalex.org/W3114753236","https://openalex.org/W4235587621","https://openalex.org/W4293682399","https://openalex.org/W6735308777","https://openalex.org/W6739778489","https://openalex.org/W6756639113","https://openalex.org/W6756795685","https://openalex.org/W6757384202","https://openalex.org/W6760812498","https://openalex.org/W6765565478"],"related_works":["https://openalex.org/W2114282491","https://openalex.org/W2592395359","https://openalex.org/W2045342254","https://openalex.org/W2535231171","https://openalex.org/W1501331687","https://openalex.org/W4255512592","https://openalex.org/W2501551404","https://openalex.org/W2326647871","https://openalex.org/W4205247302","https://openalex.org/W2468652214"],"abstract_inverted_index":{"We":[0,13,48,83],"focus":[1],"on":[2,119],"the":[3,57,71,90],"problem":[4],"of":[5,9,29,46,80],"class-agnostic":[6],"instance":[7],"segmentation":[8,55,74,101,113],"LiDAR":[10],"point":[11,121],"clouds.":[12,122],"propose":[14],"an":[15,66,76,86],"approach":[16],"that":[17,50,69,106],"combines":[18],"graph-theoretic":[19],"search":[20],"with":[21],"data-driven":[22,43],"learning:":[23],"it":[24],"searches":[25],"over":[26],"a":[27,42,54,100],"set":[28],"candidate":[30,81],"segmentations":[31],"and":[32,103,115],"returns":[33],"one":[34],"where":[35],"individual":[36,62],"segments":[37],"score":[38,53],"well":[39],"according":[40],"to":[41],"point-based":[44],"model":[45],"\u201cobjectness\u201d.":[47],"prove":[49],"if":[51],"we":[52,94],"by":[56],"worst":[58],"objectness":[59],"among":[60,75],"its":[61],"segments,":[63],"there":[64],"is":[65],"efficient":[67,87],"algorithm":[68,88],"finds":[70],"optimal":[72],"worst-case":[73],"exponentially":[77],"large":[78],"number":[79],"segmentations.":[82],"also":[84],"present":[85],"for":[89],"average-case.":[91],"For":[92],"evaluation,":[93],"repurpose":[95],"KITTI":[96],"3D":[97],"detection":[98],"as":[99],"benchmark":[102],"empirically":[104],"demonstrate":[105],"our":[107],"algorithms":[108,118],"significantly":[109],"outperform":[110],"past":[111],"bottom-up":[112],"approaches":[114],"top-down":[116],"object-based":[117],"segmenting":[120]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
