{"id":"https://openalex.org/W3162878372","doi":"https://doi.org/10.1109/icpr48806.2021.9413135","title":"MANet: Multimodal Attention Network based Point-View Fusion for 3D Shape Recognition","display_name":"MANet: Multimodal Attention Network based Point-View Fusion for 3D Shape Recognition","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3162878372","doi":"https://doi.org/10.1109/icpr48806.2021.9413135","mag":"3162878372"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9413135","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413135","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101532451","display_name":"Yaxin Zhao","orcid":"https://orcid.org/0009-0003-7292-5870"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaxin Zhao","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054785818","display_name":"Jichao Jiao","orcid":"https://orcid.org/0000-0002-1200-5525"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jichao Jiao","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100691181","display_name":"Ning Li","orcid":"https://orcid.org/0000-0003-1025-9641"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Li","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102222671","display_name":"Zhongliang Deng","orcid":"https://orcid.org/0000-0002-1984-8613"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongliang Deng","raw_affiliation_strings":["School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"134","last_page":"141"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9876000285148621,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9851999878883362,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7940840125083923},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7909789085388184},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.700156569480896},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6275266408920288},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.5596762299537659},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.46336930990219116},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45605891942977905},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4460050165653229},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44154390692710876},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4379206895828247},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4333551526069641},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3578299880027771}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940840125083923},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7909789085388184},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.700156569480896},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6275266408920288},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.5596762299537659},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.46336930990219116},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45605891942977905},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4460050165653229},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44154390692710876},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4379206895828247},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4333551526069641},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3578299880027771},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9413135","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9413135","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1514535095","https://openalex.org/W1629010235","https://openalex.org/W1644641054","https://openalex.org/W1920022804","https://openalex.org/W2133564696","https://openalex.org/W2163605009","https://openalex.org/W2211722331","https://openalex.org/W2229637417","https://openalex.org/W2504204199","https://openalex.org/W2518780089","https://openalex.org/W2525778437","https://openalex.org/W2556802233","https://openalex.org/W2558748708","https://openalex.org/W2560609797","https://openalex.org/W2565662353","https://openalex.org/W2734519248","https://openalex.org/W2803620531","https://openalex.org/W2810641456","https://openalex.org/W2884585870","https://openalex.org/W2888486794","https://openalex.org/W2944926369","https://openalex.org/W2962731536","https://openalex.org/W2962928871","https://openalex.org/W2963121255","https://openalex.org/W2963495494","https://openalex.org/W2963719070","https://openalex.org/W2963719584","https://openalex.org/W2963858333","https://openalex.org/W2963925437","https://openalex.org/W2964308564","https://openalex.org/W2979750740","https://openalex.org/W4288349627","https://openalex.org/W4297733535","https://openalex.org/W6630875275","https://openalex.org/W6637108133","https://openalex.org/W6640300118","https://openalex.org/W6684191040","https://openalex.org/W6703933998","https://openalex.org/W6726726311","https://openalex.org/W6739778489","https://openalex.org/W6747904511","https://openalex.org/W6753412334","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W2354322770","https://openalex.org/W3000097931","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W2965546495","https://openalex.org/W1924837940","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W2154495931"],"abstract_inverted_index":{"3D":[0,13,19,28,63,86,160],"shape":[1,64,87,142,161],"recognition":[2,71],"has":[3],"attracted":[4],"more":[5,7,158],"and":[6,113,144,150,172],"attention":[8,83,99],"as":[9],"a":[10,61,77,97,157],"task":[11],"of":[12,18,50,55,92,119,135,178],"vision":[14],"research.":[15],"The":[16],"proliferation":[17],"data":[20,41,44,54],"encourages":[21],"various":[22],"deep":[23,35],"learning":[24,36],"methods":[25],"based":[26,38,80],"on":[27,39,81,168],"data.":[29],"Now":[30],"there":[31],"have":[32,164],"been":[33],"many":[34],"models":[37],"point-cloud":[40,106,148],"or":[42],"multi-view":[43,93,111,129,137,153],"alone.":[45],"However,":[46],"in":[47],"the":[48,70,90,104,110,116,120,127,133,140,147,151,169,176],"era":[49],"big":[51],"data,":[52,94],"integrating":[53],"two":[56,121],"different":[57],"modals":[58],"to":[59,68,108,139,155],"obtain":[60,126,156],"unified":[62],"descriptor":[65],"is":[66],"bound":[67],"improve":[69],"accuracy.":[72],"Therefore,":[73],"this":[74],"paper":[75],"proposes":[76],"fusion":[78,118],"network":[79],"multimodal":[82],"mechanism":[84],"for":[85],"recognition.":[88],"Considering":[89],"limitations":[91],"we":[95,125],"introduce":[96],"soft":[98],"scheme,":[100],"which":[101],"can":[102],"use":[103],"global":[105],"features":[107,130,149,154],"filter":[109],"features,":[112],"then":[114,145],"realize":[115],"effective":[117],"features.":[122],"More":[123],"specifically,":[124],"enhanced":[128,152],"by":[131],"mining":[132],"contribution":[134],"each":[136],"image":[138],"overall":[141],"recognition,":[143],"fuse":[146],"discriminative":[159],"descriptor.":[162],"We":[163],"performed":[165],"relevant":[166],"experiments":[167],"ModelNet40":[170],"dataset,":[171],"experimental":[173],"results":[174],"verify":[175],"effectiveness":[177],"our":[179],"method.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}