{"id":"https://openalex.org/W4220967590","doi":"https://doi.org/10.1109/tnnls.2022.3155282","title":"A Novel Local\u2013Global Graph Convolutional Method for Point Cloud Semantic Segmentation","display_name":"A Novel Local\u2013Global Graph Convolutional Method for Point Cloud Semantic Segmentation","publication_year":2022,"publication_date":"2022-03-14","ids":{"openalex":"https://openalex.org/W4220967590","doi":"https://doi.org/10.1109/tnnls.2022.3155282","pmid":"https://pubmed.ncbi.nlm.nih.gov/35286267"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3155282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3155282","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5069118756","display_name":"Zijin Du","orcid":"https://orcid.org/0000-0001-9380-6174"},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zijin Du","raw_affiliation_strings":["College of Sciences, China Jiliang University, Hangzhou, China","College of Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, China"],"raw_orcid":"https://orcid.org/0000-0001-9380-6174","affiliations":[{"raw_affiliation_string":"College of Sciences, China Jiliang University, Hangzhou, China","institution_ids":["https://openalex.org/I55538621"]},{"raw_affiliation_string":"College of Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, China","institution_ids":["https://openalex.org/I55538621"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023547926","display_name":"Hailiang Ye","orcid":"https://orcid.org/0000-0001-8609-253X"},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hailiang Ye","raw_affiliation_strings":["College of Sciences, China Jiliang University, Hangzhou, China","College of Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, China"],"raw_orcid":"https://orcid.org/0000-0001-8609-253X","affiliations":[{"raw_affiliation_string":"College of Sciences, China Jiliang University, Hangzhou, China","institution_ids":["https://openalex.org/I55538621"]},{"raw_affiliation_string":"College of Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, China","institution_ids":["https://openalex.org/I55538621"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045211553","display_name":"Feilong Cao","orcid":"https://orcid.org/0000-0002-1690-5694"},"institutions":[{"id":"https://openalex.org/I55538621","display_name":"China Jiliang University","ror":"https://ror.org/05v1y0t93","country_code":"CN","type":"education","lineage":["https://openalex.org/I55538621"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feilong Cao","raw_affiliation_strings":["College of Sciences, China Jiliang University, Hangzhou, China","College of Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, China"],"raw_orcid":"https://orcid.org/0000-0002-1690-5694","affiliations":[{"raw_affiliation_string":"College of Sciences, China Jiliang University, Hangzhou, China","institution_ids":["https://openalex.org/I55538621"]},{"raw_affiliation_string":"College of Sciences, China Jiliang University, Hangzhou, Zhejiang 310018, China","institution_ids":["https://openalex.org/I55538621"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069118756"],"corresponding_institution_ids":["https://openalex.org/I55538621"],"apc_list":null,"apc_paid":null,"fwci":13.2905,"has_fulltext":false,"cited_by_count":83,"citation_normalized_percentile":{"value":0.99699663,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"35","issue":"4","first_page":"4798","last_page":"4812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9970999956130981,"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"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/point-cloud","display_name":"Point cloud","score":0.8595645427703857},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7282518148422241},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6345198750495911},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.602776288986206},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5816644430160522},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5201107263565063},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4662318229675293},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45730429887771606},{"id":"https://openalex.org/keywords/convolutional-code","display_name":"Convolutional code","score":0.44057855010032654},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.42753303050994873},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.38084012269973755},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35553231835365295},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2992287874221802},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.18344056606292725}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8595645427703857},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7282518148422241},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6345198750495911},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.602776288986206},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5816644430160522},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5201107263565063},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4662318229675293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45730429887771606},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.44057855010032654},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.42753303050994873},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.38084012269973755},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35553231835365295},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2992287874221802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.18344056606292725},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3155282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3155282","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35286267","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35286267","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G2542213581","display_name":null,"funder_award_id":"62006215","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2973092727","display_name":null,"funder_award_id":"62032022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8381135209","display_name":null,"funder_award_id":"62176244","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W1903029394","https://openalex.org/W1920022804","https://openalex.org/W1988115241","https://openalex.org/W2116341502","https://openalex.org/W2460657278","https://openalex.org/W2553307952","https://openalex.org/W2556802233","https://openalex.org/W2560609797","https://openalex.org/W2594519801","https://openalex.org/W2606202972","https://openalex.org/W2614059183","https://openalex.org/W2737234477","https://openalex.org/W2789544120","https://openalex.org/W2797997528","https://openalex.org/W2895472109","https://openalex.org/W2907492528","https://openalex.org/W2920026315","https://openalex.org/W2950635152","https://openalex.org/W2960986959","https://openalex.org/W2962731536","https://openalex.org/W2963053547","https://openalex.org/W2963073614","https://openalex.org/W2963091558","https://openalex.org/W2963125977","https://openalex.org/W2963182550","https://openalex.org/W2963226018","https://openalex.org/W2963231572","https://openalex.org/W2963281829","https://openalex.org/W2963316559","https://openalex.org/W2963509914","https://openalex.org/W2963517242","https://openalex.org/W2963830382","https://openalex.org/W2964253930","https://openalex.org/W2973511309","https://openalex.org/W2979750740","https://openalex.org/W2981199548","https://openalex.org/W2990045899","https://openalex.org/W2990613095","https://openalex.org/W2991084087","https://openalex.org/W3012494314","https://openalex.org/W3014902535","https://openalex.org/W3024644820","https://openalex.org/W3025802147","https://openalex.org/W3034239841","https://openalex.org/W3034482224","https://openalex.org/W3034591723","https://openalex.org/W3039448353","https://openalex.org/W3043238202","https://openalex.org/W3080980548","https://openalex.org/W3103796199","https://openalex.org/W3109154950","https://openalex.org/W3111535274","https://openalex.org/W3119315021","https://openalex.org/W3153465022","https://openalex.org/W3162787701","https://openalex.org/W3164338400","https://openalex.org/W3165639664","https://openalex.org/W3176802407","https://openalex.org/W3206604724","https://openalex.org/W4385245566","https://openalex.org/W6684191040","https://openalex.org/W6685562342","https://openalex.org/W6726873649","https://openalex.org/W6739778489","https://openalex.org/W6753266022","https://openalex.org/W6767716835","https://openalex.org/W6769516661","https://openalex.org/W6910779650"],"related_works":["https://openalex.org/W2610189143","https://openalex.org/W2159424856","https://openalex.org/W4293226380","https://openalex.org/W4389574804","https://openalex.org/W2161474341","https://openalex.org/W3016928466","https://openalex.org/W2936725271","https://openalex.org/W4302615923","https://openalex.org/W3150655618","https://openalex.org/W2964954556"],"abstract_inverted_index":{"Although":[0],"convolutional":[1,39],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5],"shown":[6],"good":[7],"performance":[8],"on":[9,49,218],"grid":[10],"data,":[11],"they":[12],"are":[13,142],"limited":[14],"in":[15],"the":[16,36,58,77,88,94,99,114,120,123,130,134,152,163,196,206,209,222,228],"semantic":[17,186],"segmentation":[18,187],"of":[19,60,76,81,90,98,116,122,133,165,174,188,195,199,208,221],"irregular":[20],"point":[21,50,101,190,210,224],"clouds.":[22,51,191],"This":[23],"article":[24],"proposes":[25],"a":[26,72,105,146],"novel":[27],"and":[28,46,84,86,160,177],"effective":[29],"graph":[30,38,79],"CNN":[31],"framework,":[32,172],"referred":[33],"to":[34,54,92,118,155,203],"as":[35],"local-global":[37],"method":[40],"(LGGCM),":[41],"which":[42],"can":[43,212],"achieve":[44],"short-":[45],"long-range":[47,157],"dependencies":[48],"The":[52,66,137,170,192],"key":[53],"this":[55],"framework":[56,230],"is":[57,111,180,201],"design":[59,67],"local":[61,78,100],"spatial":[62,95,148],"attention":[63,149],"convolution":[64,124],"(LSA-Conv).":[65],"includes":[68],"two":[69],"parts:":[70],"generating":[71],"weighted":[73],"adjacency":[74],"matrix":[75],"composed":[80],"neighborhood":[82],"points,":[83],"updating":[85],"aggregating":[87],"features":[89,97,141,166,207],"nodes":[91],"obtain":[93],"geometric":[96],"cloud.":[102],"In":[103],"addition,":[104],"smooth":[106],"module":[107,150],"for":[108,185],"central":[109],"points":[110,135],"incorporated":[112],"into":[113,145],"process":[115],"LSA-Conv":[117,140,200],"enhance":[119],"robustness":[121],"against":[125],"noise":[126],"interference":[127],"by":[128],"adjusting":[129],"position":[131],"coordinates":[132],"adaptively.":[136],"learned":[138],"robust":[139],"then":[143],"fed":[144],"global":[147],"with":[151],"gated":[153],"unit":[154],"extract":[156],"contextual":[158],"information":[159],"dynamically":[161],"adjust":[162],"weights":[164],"from":[167],"different":[168],"stages.":[169],"proposed":[171,229],"consisting":[173],"both":[175],"encoding":[176],"decoding":[178],"branches,":[179],"an":[181],"end-to-end":[182],"trainable":[183],"network":[184],"3-D":[189,223],"theoretical":[193],"analysis":[194],"approximation":[197],"capabilities":[198],"discussed":[202],"determine":[204],"whether":[205],"cloud":[211,225],"be":[213],"accurately":[214],"represented.":[215],"Experimental":[216],"results":[217],"challenging":[219],"benchmarks":[220],"demonstrate":[226],"that":[227],"achieves":[231],"excellent":[232],"performance.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":27},{"year":2024,"cited_by_count":33},{"year":2023,"cited_by_count":20}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}