{"id":"https://openalex.org/W4221154304","doi":"https://doi.org/10.1109/icme52920.2022.9859996","title":"MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation","display_name":"MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4221154304","doi":"https://doi.org/10.1109/icme52920.2022.9859996"},"language":"en","primary_location":{"id":"doi:10.1109/icme52920.2022.9859996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859996","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.14662","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109202133","display_name":"Min Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["CN","SE"],"is_corresponding":true,"raw_author_name":"Min Zhong","raw_affiliation_strings":["Peking University,Key Laboratory on Machine Perception","Huawei Noah's Ark Lab","Key Laboratory on Machine Perception, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Key Laboratory on Machine Perception","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Huawei Noah's Ark Lab","institution_ids":["https://openalex.org/I4210159102"]},{"raw_affiliation_string":"Key Laboratory on Machine Perception, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101443405","display_name":"Xinghao Chen","orcid":"https://orcid.org/0000-0002-4832-6830"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["CN","SE"],"is_corresponding":false,"raw_author_name":"Xinghao Chen","raw_affiliation_strings":["Huawei Noah&#x0027;s Ark Lab","Peking University,Key Laboratory on Machine Perception"],"affiliations":[{"raw_affiliation_string":"Huawei Noah&#x0027;s Ark Lab","institution_ids":["https://openalex.org/I4210159102"]},{"raw_affiliation_string":"Peking University,Key Laboratory on Machine Perception","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101412833","display_name":"Xiaokang Chen","orcid":"https://orcid.org/0000-0002-6188-5821"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]},{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN","SE"],"is_corresponding":false,"raw_author_name":"Xiaokang Chen","raw_affiliation_strings":["Huawei Noah&#x0027;s Ark Lab","Peking University,Key Laboratory on Machine Perception","Key Laboratory on Machine Perception, Peking University"],"affiliations":[{"raw_affiliation_string":"Huawei Noah&#x0027;s Ark Lab","institution_ids":["https://openalex.org/I4210159102"]},{"raw_affiliation_string":"Peking University,Key Laboratory on Machine Perception","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Key Laboratory on Machine Perception, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103205993","display_name":"Gang Zeng","orcid":"https://orcid.org/0000-0002-9575-4651"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Zeng","raw_affiliation_strings":["Peking University,Key Laboratory on Machine Perception","Key Laboratory on Machine Perception, Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University,Key Laboratory on Machine Perception","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Key Laboratory on Machine Perception, Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100727355","display_name":"Yunhe Wang","orcid":"https://orcid.org/0000-0002-0013-4530"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Yunhe Wang","raw_affiliation_strings":["Huawei Noah&#x0027;s Ark Lab"],"affiliations":[{"raw_affiliation_string":"Huawei Noah&#x0027;s Ark Lab","institution_ids":["https://openalex.org/I4210159102"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5109202133"],"corresponding_institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210159102"],"apc_list":null,"apc_paid":null,"fwci":7.8616,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.9770195,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.996999979019165,"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/masking","display_name":"Masking (illustration)","score":0.7258663177490234},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.698983371257782},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.53125},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5215487480163574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5090557932853699},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4452385902404785},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1566842794418335}],"concepts":[{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.7258663177490234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.698983371257782},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.53125},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5215487480163574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5090557932853699},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4452385902404785},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1566842794418335},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icme52920.2022.9859996","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme52920.2022.9859996","pdf_url":null,"source":{"id":"https://openalex.org/S4363607799","display_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2203.14662","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14662","pdf_url":"https://arxiv.org/pdf/2203.14662","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2203.14662","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.14662","pdf_url":"https://arxiv.org/pdf/2203.14662","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W2460657278","https://openalex.org/W2565639579","https://openalex.org/W2594519801","https://openalex.org/W2769312834","https://openalex.org/W2799123546","https://openalex.org/W2886904239","https://openalex.org/W2904332125","https://openalex.org/W2914845116","https://openalex.org/W2922266320","https://openalex.org/W2948410735","https://openalex.org/W2959771705","https://openalex.org/W2963182550","https://openalex.org/W2964166085","https://openalex.org/W2964266557","https://openalex.org/W2988715931","https://openalex.org/W2990129662","https://openalex.org/W2995182785","https://openalex.org/W3004300126","https://openalex.org/W3034430142","https://openalex.org/W3034550906","https://openalex.org/W3034949383","https://openalex.org/W3037420957","https://openalex.org/W3042283226","https://openalex.org/W3089315073","https://openalex.org/W3118038951","https://openalex.org/W3170500059","https://openalex.org/W3172351327","https://openalex.org/W3196869447","https://openalex.org/W3204034406","https://openalex.org/W3212035326","https://openalex.org/W4214773923","https://openalex.org/W4287020244","https://openalex.org/W6753610190","https://openalex.org/W6763229141","https://openalex.org/W6781380455","https://openalex.org/W6799925391","https://openalex.org/W6800377202","https://openalex.org/W6803823638"],"related_works":["https://openalex.org/W3081694532","https://openalex.org/W1969211203","https://openalex.org/W1517958729","https://openalex.org/W2092272653","https://openalex.org/W4387002515","https://openalex.org/W1992704972","https://openalex.org/W271627879","https://openalex.org/W2349444258","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"the":[3,22,31,53,89,148,155,161,164,176,181,190],"3D":[4,25,54],"instance":[5,73,119],"segmentation":[6,149],"problem,":[7,43],"which":[8,101,121,185],"has":[9],"a":[10,46,82,132,172],"variety":[11],"of":[12,24,28,33,142,163],"real-world":[13],"applications":[14],"such":[15],"as":[16],"robotics":[17],"and":[18,51,67,157],"augmented":[19],"reality.":[20],"Since":[21],"surroundings":[23],"objects":[26,35],"are":[27,96,116],"high":[29],"complexity,":[30],"separating":[32],"different":[34,128],"is":[36,122,135,186],"very":[37],"difficult.":[38],"To":[39,75],"address":[40],"this":[41],"challenging":[42],"we":[44,58,80],"propose":[45,81],"novel":[47,133],"framework":[48],"to":[49,70,87,108,137],"group":[50,77],"refine":[52],"instances.":[55],"In":[56,130],"practice,":[57],"first":[59],"learn":[60],"an":[61],"offset":[62],"vector":[63],"for":[64,118,124,145],"each":[65],"point":[66,140],"shift":[68],"it":[69],"its":[71],"predicted":[72],"center.":[74],"better":[76],"these":[78,143],"points,":[79],"Hierarchical":[83],"Point":[84],"Grouping":[85],"algorithm":[86],"merge":[88,109],"centrally":[90],"aggregated":[91],"points":[92,95],"progressively.":[93],"All":[94],"grouped":[97],"into":[98,110],"small":[99],"clusters,":[100],"further":[102,146],"gradually":[103],"undergo":[104],"another":[105],"clustering":[106],"procedure":[107],"larger":[111],"groups.":[112],"These":[113],"multi-scale":[114],"groups":[115,144],"exploited":[117],"prediction,":[120],"beneficial":[123],"predicting":[125],"instances":[126],"with":[127,175],"scales.":[129],"addition,":[131],"MaskScoreNet":[134],"developed":[136],"produce":[138],"binary":[139],"masks":[141],"refining":[147],"results.":[150],"Extensive":[151],"experiments":[152],"conducted":[153],"on":[154,180],"ScanNetV2":[156,182],"S3DIS":[158],"benchmarks":[159],"demonstrate":[160],"effectiveness":[162],"proposed":[165],"method.":[166,192],"For":[167],"instance,":[168],"our":[169],"MaskGroup":[170],"achieves":[171],"66.4%":[173],"mAP":[174],"0.5":[177],"IoU":[178],"threshold":[179],"test":[183],"set,":[184],"1.9%":[187],"higher":[188],"than":[189],"state-of-the-art":[191]},"counts_by_year":[{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2022-04-03T00:00:00"}
