{"id":"https://openalex.org/W4402351263","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651382","title":"Attention-Based Deep Neural Network for Point Cloud Learning","display_name":"Attention-Based Deep Neural Network for Point Cloud Learning","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351263","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651382"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651382","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5075474564","display_name":"Tianlei Wang","orcid":"https://orcid.org/0000-0002-4498-4326"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianlei Wang","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101430780","display_name":"Keyu Chen","orcid":"https://orcid.org/0000-0003-0808-2533"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Keyu Chen","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102948018","display_name":"Ma Luo","orcid":"https://orcid.org/0009-0006-8700-9092"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ma Luo","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021975286","display_name":"Hong Qu","orcid":"https://orcid.org/0000-0001-6114-3441"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Qu","raw_affiliation_strings":["University of Electronic Science and Technology of China,Chengdu,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China,Chengdu,China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15050706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"212","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9987000226974487,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7519051432609558},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6506298780441284},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6242060661315918},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5877012014389038},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47192859649658203},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4530089795589447},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.07249811291694641}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7519051432609558},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6506298780441284},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6242060661315918},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5877012014389038},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47192859649658203},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4530089795589447},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.07249811291694641}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651382","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1644641054","https://openalex.org/W1920022804","https://openalex.org/W2093353037","https://openalex.org/W2301363727","https://openalex.org/W2560609797","https://openalex.org/W2888486794","https://openalex.org/W2947689748","https://openalex.org/W2963121255","https://openalex.org/W2963509914","https://openalex.org/W2964253930","https://openalex.org/W2979750740","https://openalex.org/W2981440248","https://openalex.org/W3049667295","https://openalex.org/W3111535274","https://openalex.org/W3113005610","https://openalex.org/W3114160010","https://openalex.org/W3118806719","https://openalex.org/W3153465022","https://openalex.org/W3157424380","https://openalex.org/W3162137573","https://openalex.org/W3201205318","https://openalex.org/W3202756173","https://openalex.org/W3203890361","https://openalex.org/W3205586691","https://openalex.org/W3211528229","https://openalex.org/W4206697475","https://openalex.org/W4214755140","https://openalex.org/W4289752563","https://openalex.org/W4306175953","https://openalex.org/W4312270234","https://openalex.org/W4312569019","https://openalex.org/W4385245566","https://openalex.org/W6739778489","https://openalex.org/W6790453339","https://openalex.org/W6801659397","https://openalex.org/W6809998732","https://openalex.org/W6846166657","https://openalex.org/W6857820246"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"Point":[0],"cloud":[1,50,127],"learning":[2,22,42,51],"is":[3],"a":[4,27,31,80,90],"vital":[5],"task":[6],"in":[7,40,48,89],"the":[8,15,62,108,111,118,121,125,143,158,166,184,192],"field":[9],"of":[10,19,64,110],"computer":[11],"vision.":[12],"Due":[13],"to":[14,124],"irregularity":[16],"and":[17,43,61,86,103,113,149,161,173,187,199],"disorderliness":[18],"point":[20,49,126,176,202],"clouds,":[21],"their":[23],"features":[24,88],"has":[25],"been":[26,37],"challenging":[28],"issue":[29],"for":[30],"long":[32],"time.":[33],"Attention":[34],"mechanisms":[35],"have":[36,44],"widely":[38],"applied":[39],"deep":[41],"achieved":[45],"good":[46],"results":[47,140],"tasks.":[52],"However,":[53],"existing":[54],"models":[55,130],"mainly":[56],"focus":[57],"on":[58,94,133,157,165,183,191],"local":[59,85],"features,":[60],"stacking":[63],"numerous":[65],"attention":[66,115],"layers":[67],"requires":[68],"significant":[69],"computational":[70,150],"resources.":[71],"To":[72],"address":[73],"these":[74],"problems,":[75],"this":[76,95],"study":[77],"first":[78],"introduces":[79,120],"framework":[81],"that":[82,142],"considers":[83],"both":[84],"global":[87],"parallel":[91],"manner.":[92],"Based":[93],"framework,":[96],"we":[97],"propose":[98],"two":[99,134],"practical":[100],"implementations,":[101],"PCAttn":[102,106,152],"PCTrans.":[104],"The":[105],"explores":[107],"effectiveness":[109],"channel":[112],"spatial":[114],"mechanism,":[116],"while":[117],"PCTrans":[119,178],"self-attention":[122],"mechanism":[123],"learning.":[128],"These":[129],"are":[131],"tested":[132],"publicly":[135],"available":[136],"benchmark":[137],"datasets.":[138],"Experimental":[139],"demonstrate":[141],"proposed":[144],"methods":[145],"exhibit":[146],"high":[147],"performance":[148],"efficiency.":[151],"achieves":[153,179],"93.2%":[154],"overall":[155,163,181,189],"accuracy":[156,164,182,190],"ModelNet40":[159,185],"dataset":[160,168,186,194],"84.6%":[162],"ScanObjectNN":[167,193],"with":[169,195],"only":[170,196],"0.61M":[171],"parameters":[172,198],"3.4G":[174],"floating":[175,201],"operations.":[177,203],"93.1%":[180],"83.3%":[188],"0.68M":[197],"0.35G":[200]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
