{"id":"https://openalex.org/W4376455312","doi":"https://doi.org/10.1109/tiv.2023.3274949","title":"Scene-Adaptive 3D Semantic Segmentation Based on Multi-Level Boundary-Semantic-Enhancement for Intelligent Vehicles","display_name":"Scene-Adaptive 3D Semantic Segmentation Based on Multi-Level Boundary-Semantic-Enhancement for Intelligent Vehicles","publication_year":2023,"publication_date":"2023-05-10","ids":{"openalex":"https://openalex.org/W4376455312","doi":"https://doi.org/10.1109/tiv.2023.3274949"},"language":"en","primary_location":{"id":"doi:10.1109/tiv.2023.3274949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2023.3274949","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039079852","display_name":"Peizhou Ni","orcid":"https://orcid.org/0000-0002-1684-9936"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peizhou Ni","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1684-9936","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100699659","display_name":"Xu Li","orcid":"https://orcid.org/0000-0003-2772-7114"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Li","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-2772-7114","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057379701","display_name":"Dong Kong","orcid":"https://orcid.org/0000-0002-1864-423X"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Kong","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1864-423X","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101908382","display_name":"Xiaoqing Yin","orcid":"https://orcid.org/0000-0001-6916-1919"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqing Yin","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":2.8718,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.92659192,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"9","issue":"1","first_page":"1722","last_page":"1732"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9991000294685364,"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":0.9987999796867371,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7984704971313477},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7388006448745728},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.6996912360191345},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.673667311668396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5604617595672607},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4784536361694336},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.45291659235954285},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4465493857860565},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.44457677006721497},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39833903312683105},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3537752330303192},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13151466846466064}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7984704971313477},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7388006448745728},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.6996912360191345},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.673667311668396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5604617595672607},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4784536361694336},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.45291659235954285},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4465493857860565},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.44457677006721497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39833903312683105},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3537752330303192},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13151466846466064},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tiv.2023.3274949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2023.3274949","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G7294854559","display_name":null,"funder_award_id":"61973079","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2520369361","https://openalex.org/W2896992394","https://openalex.org/W2953399169","https://openalex.org/W2954258401","https://openalex.org/W2962793481","https://openalex.org/W2963073217","https://openalex.org/W2963073614","https://openalex.org/W2963125977","https://openalex.org/W2963182550","https://openalex.org/W2963727135","https://openalex.org/W2971265822","https://openalex.org/W2990142864","https://openalex.org/W2990613095","https://openalex.org/W2991216808","https://openalex.org/W2991471181","https://openalex.org/W3012120793","https://openalex.org/W3012494314","https://openalex.org/W3021631187","https://openalex.org/W3035275207","https://openalex.org/W3039448353","https://openalex.org/W3084505175","https://openalex.org/W3093434340","https://openalex.org/W3094428708","https://openalex.org/W3107518100","https://openalex.org/W3109154950","https://openalex.org/W3119659463","https://openalex.org/W3128306016","https://openalex.org/W3128514315","https://openalex.org/W3132455321","https://openalex.org/W3137481847","https://openalex.org/W3157315619","https://openalex.org/W3167522598","https://openalex.org/W3174926460","https://openalex.org/W3176159010","https://openalex.org/W3186678659","https://openalex.org/W3201731711","https://openalex.org/W3203217490","https://openalex.org/W3206164009","https://openalex.org/W3206676377","https://openalex.org/W4210349466","https://openalex.org/W4226343873","https://openalex.org/W6763422710","https://openalex.org/W6765299845"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W2387995142","https://openalex.org/W4380714744","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2057775761","https://openalex.org/W3112772842","https://openalex.org/W1522196789"],"abstract_inverted_index":{"3D":[0,19],"semantic":[1,99,176],"segmentation":[2,20,53,139,177],"is":[3,129,145],"a":[4,142,167],"key":[5],"technology":[6],"of":[7,47,81,122,151,214],"scene":[8],"understanding":[9],"in":[10,26,34,88,106,179,218],"the":[11,79,120,125,137,149,156,159,171,180,212,215],"self-driving":[12],"field,":[13],"which":[14,101],"remains":[15],"challenging":[16],"problems.":[17],"Recent":[18],"methods":[21,49],"have":[22],"achieved":[23],"competitive":[24],"results":[25,178,210],"indoor":[27],"or":[28],"typical":[29],"urban":[30,197],"traffic":[31],"scenes.":[32],"However,":[33],"complex":[35],"and":[36,44,58,67,76,85,161,184,192,203,222],"changeable":[37],"scenarios":[38],"where":[39],"structured":[40,191],"features":[41,105],"are":[42,112,187],"sparse":[43],"irregular,":[45],"few":[46],"these":[48],"could":[50],"achieve":[51],"well":[52],"results,":[54],"especially":[55],"causing":[56],"blurry":[57],"inaccurate":[59],"boundary":[60,74,104,110,126,134,152],"distinctions":[61],"between":[62,158],"inter-class":[63],"objects,":[64],"drivable":[65],"areas,":[66],"backgrounds.":[68],"In":[69],"order":[70],"to":[71,131,147,174],"fully":[72],"harvest":[73],"information":[75,135,153],"accurately":[77],"distinguish":[78],"category":[80],"points":[82],"on":[83,189],"road":[84],"object":[86],"boundaries":[87],"real-time,":[89],"we":[90,165],"present":[91],"an":[92],"efficient":[93],"multi-level":[94],"boundary-semantic-enhanced":[95],"model":[96],"for":[97],"LiDAR":[98,116],"segmentation,":[100],"comprehensively":[102],"discover":[103],"three":[107],"aspects:":[108],"first,":[109],"channels":[111],"extracted":[113],"directly":[114],"from":[115],"range":[117],"images":[118],"as":[119],"inputs":[121],"boundary-branch;":[123],"second,":[124],"attention":[127],"module":[128,169],"designed":[130],"deeply":[132],"fuse":[133],"into":[136],"main":[138],"branch;":[140],"third,":[141],"modified":[143],"discriminator":[144,173],"utilized":[146],"raise":[148],"perception":[150],"by":[154],"minimizing":[155],"gap":[157],"predicted":[160],"true":[162],"boundaries.":[163],"Besides,":[164],"add":[166],"semantic-enhanced":[168],"using":[170],"similar":[172],"optimize":[175],"output":[181],"end.":[182],"Quantitative":[183],"qualitative":[185],"evaluations":[186],"performed":[188],"both":[190],"unstructured":[193,205],"real-world":[194],"datasets":[195],"including":[196],"dataset":[198,201],"SemanticKITTI,":[199],"off-road":[200],"Rellis3D":[202],"our":[204],"test":[206],"set.":[207],"The":[208],"experimental":[209],"validate":[211],"effectiveness":[213],"proposed":[216],"methodology":[217],"improving":[219],"efficiency,":[220],"accuracy":[221],"scene-adaptivity.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
