{"id":"https://openalex.org/W4304142091","doi":"https://doi.org/10.1109/icac55051.2022.9911175","title":"Deep Learning Based 3D Point Clouds Recognition for Robotic Manufacturing","display_name":"Deep Learning Based 3D Point Clouds Recognition for Robotic Manufacturing","publication_year":2022,"publication_date":"2022-09-01","ids":{"openalex":"https://openalex.org/W4304142091","doi":"https://doi.org/10.1109/icac55051.2022.9911175"},"language":"en","primary_location":{"id":"doi:10.1109/icac55051.2022.9911175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icac55051.2022.9911175","pdf_url":null,"source":{"id":"https://openalex.org/S4363608428","display_name":"2022 27th International Conference on Automation and Computing (ICAC)","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 27th International Conference on Automation and Computing (ICAC)","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/A5100718334","display_name":"Tiancheng Zhang","orcid":"https://orcid.org/0000-0001-6902-9299"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiancheng Zhang","raw_affiliation_strings":["Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100406696","display_name":"Quan Zhang","orcid":"https://orcid.org/0000-0003-0250-7174"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Zhang","raw_affiliation_strings":["Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033755146","display_name":"Eng Gee Lim","orcid":"https://orcid.org/0000-0003-0199-7386"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enggee Lim","raw_affiliation_strings":["Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123","institution_ids":["https://openalex.org/I69356397"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100618850","display_name":"Jie Sun","orcid":"https://orcid.org/0000-0001-5611-1672"},"institutions":[{"id":"https://openalex.org/I69356397","display_name":"Xi\u2019an Jiaotong-Liverpool University","ror":"https://ror.org/03zmrmn05","country_code":"CN","type":"education","lineage":["https://openalex.org/I69356397"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Sun","raw_affiliation_strings":["Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an Jiaotong-Liverpool University, 111 Ren&#x2019;ai Road, Suzhou Industrial Park,School of Advanced Technolohgy,Suzhou,China,215123","institution_ids":["https://openalex.org/I69356397"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I69356397"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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":0.9976000189781189,"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/T10705","display_name":"Additive Manufacturing Materials and Processes","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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":0.8679863214492798},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8035933375358582},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7323198914527893},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5851460695266724},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.5644122362136841},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.5129734873771667},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48858246207237244},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.46239718794822693},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4342598021030426},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43145036697387695},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.42419999837875366},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39975810050964355},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39225250482559204},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06287732720375061}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8679863214492798},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8035933375358582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7323198914527893},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5851460695266724},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.5644122362136841},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5129734873771667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48858246207237244},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.46239718794822693},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4342598021030426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43145036697387695},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39975810050964355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39225250482559204},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06287732720375061},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icac55051.2022.9911175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icac55051.2022.9911175","pdf_url":null,"source":{"id":"https://openalex.org/S4363608428","display_name":"2022 27th International Conference on Automation and Computing (ICAC)","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 27th International Conference on Automation and Computing (ICAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4300000071525574,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W156975732","https://openalex.org/W1522301498","https://openalex.org/W2560609797","https://openalex.org/W2591641731","https://openalex.org/W2887784216","https://openalex.org/W2899771611","https://openalex.org/W2963188159","https://openalex.org/W2964249569","https://openalex.org/W3017086313","https://openalex.org/W3036823336","https://openalex.org/W3169493307","https://openalex.org/W6606399993","https://openalex.org/W6631190155","https://openalex.org/W6745401828","https://openalex.org/W6756040250","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W2556085923","https://openalex.org/W3025966514","https://openalex.org/W4290774832","https://openalex.org/W3083345301","https://openalex.org/W1537622850","https://openalex.org/W2200925278","https://openalex.org/W2330829846","https://openalex.org/W2130925704","https://openalex.org/W3185561939","https://openalex.org/W118033603"],"abstract_inverted_index":{"The":[0,89],"present":[1],"work":[2,141],"aims":[3],"to":[4,45,121,132],"develop":[5],"a":[6,59,123],"light-weight":[7],"deep":[8],"learning":[9],"algorithm":[10,86],"for":[11,27,71],"3D":[12,24],"vision":[13,25],"based":[14],"on":[15],"point":[16,66],"cloud":[17,67],"perception.":[18],"We":[19],"first":[20],"analyzed":[21],"the":[22,37,56,64,81,102,149],"current":[23],"models":[26],"object":[28],"classification":[29,49],"and":[30,34,50,74],"pose":[31,51],"estimation":[32,52],"purposes,":[33],"proposed":[35,85],"that":[36,128,138,145],"global":[38],"features":[39],"extracted":[40],"by":[41,54,101,147],"PointNet":[42,61],"was":[43,87],"able":[44],"facilitate":[46],"performing":[47],"both":[48,72],"tasks":[53],"visualizing":[55],"activations":[57],"of":[58,83,92,104,129,135,142,151],"trained":[60],"model.":[62],"Then,":[63],"customized":[65],"datasets":[68],"were":[69,95],"produced":[70],"training":[73],"testing":[75],"through":[76],"Blensor.":[77],"With":[78],"our":[79,84,93,117,136],"datasets,":[80],"performance":[82,110],"evaluated.":[88],"predicted":[90,100],"results":[91],"model":[94],"also":[96],"compared":[97],"with":[98,112],"those":[99],"framework":[103,118,137,150],"[4],":[105],"which":[106],"generally":[107],"showed":[108],"similar":[109],"but":[111],"slightly":[113],"lower":[114],"accuracy.":[115],"Nevertheless,":[116],"is":[119],"considered":[120],"have":[122],"much":[124],"better":[125],"efficiency":[126],"than":[127],"[5],":[130],"due":[131],"simpler":[133],"structure":[134],"avoid":[139],"repetitive":[140],"feature":[143],"extraction":[144],"experienced":[146],"using":[148],"[5].":[152]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
