{"id":"https://openalex.org/W3214982121","doi":"https://doi.org/10.1109/access.2021.3131775","title":"Cascade Convolution Neural Network for Point Set Generation","display_name":"Cascade Convolution Neural Network for Point Set Generation","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3214982121","doi":"https://doi.org/10.1109/access.2021.3131775","mag":"3214982121"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3131775","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3131775","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09631274.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09631274.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006273455","display_name":"Chenmou Wu","orcid":"https://orcid.org/0000-0002-7796-6052"},"institutions":[{"id":"https://openalex.org/I172553438","display_name":"Jeonju National University of Education","ror":"https://ror.org/054e4t190","country_code":"KR","type":"education","lineage":["https://openalex.org/I172553438"]},{"id":"https://openalex.org/I80611190","display_name":"Jeonbuk National University","ror":"https://ror.org/05q92br09","country_code":"KR","type":"education","lineage":["https://openalex.org/I80611190"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chenmou Wu","raw_affiliation_strings":["Department of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, South Korea","institution_ids":["https://openalex.org/I172553438","https://openalex.org/I80611190"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100716029","display_name":"Hyo Jong Lee","orcid":"https://orcid.org/0000-0003-2581-5268"},"institutions":[{"id":"https://openalex.org/I172553438","display_name":"Jeonju National University of Education","ror":"https://ror.org/054e4t190","country_code":"KR","type":"education","lineage":["https://openalex.org/I172553438"]},{"id":"https://openalex.org/I80611190","display_name":"Jeonbuk National University","ror":"https://ror.org/05q92br09","country_code":"KR","type":"education","lineage":["https://openalex.org/I80611190"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyo Jong Lee","raw_affiliation_strings":["Department of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, South Korea and Center for Advanced Image and Information Technology, Jeonbuk National University, Jeonju 54896, South Korea. (e-mail: hlee@chonbuk.ac.kr)","Department of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, South Korea","Center for Advanced Image and Information Technology, Jeonbuk National University, Jeonju 54896, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-2581-5268","affiliations":[{"raw_affiliation_string":"Department of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, South Korea and Center for Advanced Image and Information Technology, Jeonbuk National University, Jeonju 54896, South Korea. (e-mail: hlee@chonbuk.ac.kr)","institution_ids":["https://openalex.org/I80611190"]},{"raw_affiliation_string":"Department of Computer Science & Engineering, Jeonbuk National University, Jeonju 54896, South Korea","institution_ids":["https://openalex.org/I172553438","https://openalex.org/I80611190"]},{"raw_affiliation_string":"Center for Advanced Image and Information Technology, Jeonbuk National University, Jeonju 54896, South Korea","institution_ids":["https://openalex.org/I80611190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100716029"],"corresponding_institution_ids":["https://openalex.org/I172553438","https://openalex.org/I80611190"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.16867982,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"9","issue":null,"first_page":"159371","last_page":"159381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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":0.9998000264167786,"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/T11245","display_name":"Advanced Numerical Analysis Techniques","score":0.9990000128746033,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9969000220298767,"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/cascade","display_name":"Cascade","score":0.7473635673522949},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.731315553188324},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5942836403846741},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5198319554328918},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4560518264770508},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3801383078098297},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.06265461444854736}],"concepts":[{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.7473635673522949},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.731315553188324},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5942836403846741},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5198319554328918},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4560518264770508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3801383078098297},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.06265461444854736},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3131775","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3131775","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09631274.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6becf7f19979409384e62989f4fe2d84","is_oa":true,"landing_page_url":"https://doaj.org/article/6becf7f19979409384e62989f4fe2d84","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 159371-159381 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3131775","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3131775","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09631274.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G1800205709","display_name":null,"funder_award_id":"GR 2019R1D1A3A03103736","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G2461460502","display_name":null,"funder_award_id":"2019R1D1A3A03103736","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G8186305352","display_name":null,"funder_award_id":"GR2019R1D1A3A03103736","funder_id":"https://openalex.org/F4320311687","funder_display_name":"Ministry of Education"},{"id":"https://openalex.org/G912010720","display_name":null,"funder_award_id":"GR2019R1D1A3A03103736","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320311687","display_name":"Ministry of Education","ror":"https://ror.org/03m01yf64"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3214982121.pdf","grobid_xml":"https://content.openalex.org/works/W3214982121.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1499798934","https://openalex.org/W1533861849","https://openalex.org/W1644641054","https://openalex.org/W1677182931","https://openalex.org/W1974053251","https://openalex.org/W1979266466","https://openalex.org/W2026642053","https://openalex.org/W2117007522","https://openalex.org/W2143668817","https://openalex.org/W2342277278","https://openalex.org/W2464708700","https://openalex.org/W2546066744","https://openalex.org/W2560609797","https://openalex.org/W2560722161","https://openalex.org/W2738551266","https://openalex.org/W2895596173","https://openalex.org/W2944579304","https://openalex.org/W2949671016","https://openalex.org/W2962778872","https://openalex.org/W2962912205","https://openalex.org/W2962928871","https://openalex.org/W2963121255","https://openalex.org/W2963453931","https://openalex.org/W2963527086","https://openalex.org/W2963563548","https://openalex.org/W2963719584","https://openalex.org/W2979750740","https://openalex.org/W3043770819","https://openalex.org/W3179877418","https://openalex.org/W4394671432","https://openalex.org/W6629687487","https://openalex.org/W6631943919","https://openalex.org/W6687484953","https://openalex.org/W6703956885","https://openalex.org/W6729001083","https://openalex.org/W6739778489","https://openalex.org/W6746285158","https://openalex.org/W6748208425","https://openalex.org/W6750179221"],"related_works":["https://openalex.org/W2153719181","https://openalex.org/W1971748923","https://openalex.org/W1566155057","https://openalex.org/W2060986072","https://openalex.org/W2052574922","https://openalex.org/W64588465","https://openalex.org/W3120641340","https://openalex.org/W2117825986","https://openalex.org/W3134067061","https://openalex.org/W2079855347"],"abstract_inverted_index":{"Automatic":[0],"and":[1,145,162],"efficient":[2],"3D":[3,28,64,127,160,171],"object":[4,120,141,161],"modeling":[5],"has":[6,20],"become":[7],"critical":[8],"in":[9,97],"industrial":[10],"applications.":[11],"The":[12,76,147],"advancement":[13],"of":[14,56,95,138,150,184],"deep":[15,53],"convolutional":[16],"neural":[17],"networks":[18],"(CNNs)":[19],"prompted":[21],"researchers":[22],"to":[23,81,91],"use":[24],"CNNs":[25,41,68],"for":[26,45,156,167],"learning":[27],"geometry":[29,183],"information":[30,55,71,129],"directly":[31,38],"from":[32,130,191],"images.":[33],"However,":[34],"the":[35,57,83,98,118,125,131,136,139,169,182,185,202],"feature":[36],"maps":[37],"extracted":[39],"by":[40,187],"are":[42],"more":[43,52,122,143],"suitable":[44],"image":[46,133],"processing":[47],"tasks":[48,66],"because":[49],"they":[50],"contain":[51],"texture":[54,87],"entire":[58],"2D":[59,132],"image.":[60],"Compared":[61],"with":[62],"this,":[63],"reconstruction":[65],"using":[67],"demand":[69],"geometric":[70,84,128],"about":[72],"a":[73,109,151,158,163,177],"specific":[74],"object.":[75,102,172],"existing":[77,207],"architecture":[78],"mainly":[79],"tries":[80],"infer":[82],"structure":[85],"through":[86],"information,":[88],"which":[89],"leads":[90],"an":[92],"uneven":[93],"distribution":[94,137],"points":[96],"generated":[99],"point":[100,111,152,164],"cloud":[101],"To":[103],"address":[104],"this":[105],"problem,":[106],"we":[107,175],"propose":[108],"cascade":[110],"set":[112,153],"generation":[113,154],"network":[114],"(CPSGN)":[115],"that":[116,135,180,201],"deforms":[117],"predicted":[119],"while":[121],"effectively":[123],"inferring":[124],"object\u2019s":[126],"so":[134],"final":[140],"becomes":[142],"uniform":[144],"denser.":[146],"CPSGN":[148],"consists":[149],"part":[155,166],"producing":[157],"basic":[159,170],"deformation":[165],"fine-tuning":[168],"In":[173],"addition,":[174],"designed":[176],"projection":[178],"loss":[179],"optimizes":[181],"model":[186,205],"measuring":[188],"shape":[189],"differences":[190],"multiple":[192],"perspectives.":[193],"Experimental":[194],"results":[195],"on":[196],"different":[197],"benchmark":[198],"datasets":[199],"indicate":[200],"produced":[203],"point-based":[204],"outperforms":[206],"approaches.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-26T07:53:14.480251","created_date":"2025-10-10T00:00:00"}