{"id":"https://openalex.org/W3031112623","doi":"https://doi.org/10.1109/tip.2020.3048623","title":"Fine-Grained 3D Shape Classification With Hierarchical Part-View Attention","display_name":"Fine-Grained 3D Shape Classification With Hierarchical Part-View Attention","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3031112623","doi":"https://doi.org/10.1109/tip.2020.3048623","mag":"3031112623","pmid":"https://pubmed.ncbi.nlm.nih.gov/33417547"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2020.3048623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3048623","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2005.12541","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xinhai Liu","orcid":"https://orcid.org/0000-0003-4200-4862"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinhai Liu","raw_affiliation_strings":["School of Software, BNRist, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhizhong Han","orcid":"https://orcid.org/0000-0001-9540-9973"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhizhong Han","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yu-Shen Liu","orcid":"https://orcid.org/0000-0001-7305-1915"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu-Shen Liu","raw_affiliation_strings":["School of Software, BNRist, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":null,"display_name":"Matthias Zwicker","orcid":"https://orcid.org/0000-0001-8630-5515"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthias Zwicker","raw_affiliation_strings":["University of Maryland, College Park, MD, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":8.1852,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":{"value":0.98860201,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"30","issue":null,"first_page":"1744","last_page":"1758"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9574999809265137,"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":0.9574999809265137,"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":0.004900000058114529,"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"}},{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.0035000001080334187,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7657999992370605},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5526999831199646},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5282999873161316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5267999768257141},{"id":"https://openalex.org/keywords/shape-analysis","display_name":"Shape analysis (program analysis)","score":0.4878000020980835},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4641999900341034},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4634000062942505},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4108000099658966}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7657999992370605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7488999962806702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6913999915122986},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5526999831199646},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5282999873161316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5267999768257141},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.4878000020980835},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4641999900341034},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4108000099658966},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.39559999108314514},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3391999900341034},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3215999901294708},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.29649999737739563},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.2687999904155731},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2653000056743622},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2020.3048623","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2020.3048623","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:33417547","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33417547","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 image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:2005.12541","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.12541","pdf_url":"https://arxiv.org/pdf/2005.12541","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2005.12541","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.12541","pdf_url":"https://arxiv.org/pdf/2005.12541","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3174516060","display_name":null,"funder_award_id":"62072268","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":73,"referenced_works":["https://openalex.org/W56385144","https://openalex.org/W1496650988","https://openalex.org/W1629010235","https://openalex.org/W1644641054","https://openalex.org/W1985907520","https://openalex.org/W2003940193","https://openalex.org/W2050204708","https://openalex.org/W2053008628","https://openalex.org/W2071188031","https://openalex.org/W2081580037","https://openalex.org/W2099789128","https://openalex.org/W2102605133","https://openalex.org/W2104657103","https://openalex.org/W2106723645","https://openalex.org/W2108598243","https://openalex.org/W2138011018","https://openalex.org/W2157331557","https://openalex.org/W2169541125","https://openalex.org/W2207849498","https://openalex.org/W2211722331","https://openalex.org/W2289708887","https://openalex.org/W2338532005","https://openalex.org/W2461132349","https://openalex.org/W2517836489","https://openalex.org/W2553307952","https://openalex.org/W2560609797","https://openalex.org/W2565662353","https://openalex.org/W2572117954","https://openalex.org/W2773003563","https://openalex.org/W2796426482","https://openalex.org/W2810447190","https://openalex.org/W2890018557","https://openalex.org/W2893477965","https://openalex.org/W2921891839","https://openalex.org/W2945774199","https://openalex.org/W2961368225","https://openalex.org/W2962724911","https://openalex.org/W2962731536","https://openalex.org/W2962778872","https://openalex.org/W2963048248","https://openalex.org/W2963123724","https://openalex.org/W2963300078","https://openalex.org/W2963393555","https://openalex.org/W2963443993","https://openalex.org/W2963509914","https://openalex.org/W2963719584","https://openalex.org/W2964074439","https://openalex.org/W2964342398","https://openalex.org/W2965157662","https://openalex.org/W2972931660","https://openalex.org/W2979750740","https://openalex.org/W3009750677","https://openalex.org/W3034493208","https://openalex.org/W3035541121","https://openalex.org/W3082170814","https://openalex.org/W3083861253","https://openalex.org/W3092946149","https://openalex.org/W3093319304","https://openalex.org/W6618372016","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6637556369","https://openalex.org/W6638319203","https://openalex.org/W6638677478","https://openalex.org/W6640300118","https://openalex.org/W6641404545","https://openalex.org/W6687484953","https://openalex.org/W6729001083","https://openalex.org/W6736612982","https://openalex.org/W6739778489","https://openalex.org/W6748208425","https://openalex.org/W6774801324","https://openalex.org/W6779923959"],"related_works":[],"abstract_inverted_index":{"Fine-grained":[0],"3D":[1,23,36,48,114,127,186,256],"shape":[2,7,24,37,49,115,172,257],"classification":[3,25,116],"is":[4,270],"important":[5,210],"for":[6],"understanding":[8],"and":[9,66,195],"analysis,":[10],"which":[11,58,180],"poses":[12],"a":[13,46,75,111,137,162,170,235],"challenging":[14],"research":[15],"problem.":[16],"However,":[17],"the":[18,21,32,95,101,122,144,152,182,200,209,216,220,225,228,242,254],"studies":[19],"on":[20],"fine-grained":[22,35,55,76,84,113,123,255],"have":[26],"rarely":[27],"been":[28],"explored,":[29],"due":[30],"to":[31,79,120,142,168,198,240],"lack":[33],"of":[34,60,71,126,154,185,202,227],"benchmarks.":[38],"To":[39,104],"address":[40],"this":[41,83,106],"issue,":[42],"we":[43,86,108,134,160,233],"first":[44,135],"introduce":[45],"new":[47],"dataset":[50,258,269],"(named":[51],"FG3D":[52,268],"dataset)":[53],"with":[54],"class":[56],"labels,":[57],"consists":[59,70],"three":[61],"categories":[62],"including":[63],"airplane,":[64],"car":[65],"chair.":[67],"Each":[68],"category":[69],"several":[72],"subcategories":[73,99],"at":[74,272],"level.":[77],"According":[78],"our":[80,203,261],"experiments":[81],"under":[82,151,253],"dataset,":[85],"find":[87],"that":[88,260],"state-of-the-art":[89,265],"methods":[90],"are":[91],"significantly":[92],"limited":[93],"by":[94,174],"small":[96],"variance":[97],"among":[98,223,245],"in":[100,212],"same":[102,229],"category.":[103],"resolve":[105],"problem,":[107],"further":[109],"propose":[110],"novel":[112],"method":[117,262],"named":[118],"FG3D-Net":[119],"capture":[121,241],"local":[124,183],"details":[125,184],"shapes":[128],"from":[129,248],"multiple":[130,149],"rendered":[131],"views.":[132],"Specifically,":[133],"train":[136],"Region":[138],"Proposal":[139],"Network":[140,238],"(RPN)":[141],"detect":[143],"generally":[145,155,176],"semantic":[146,156,177],"parts":[147,211],"inside":[148],"views":[150,222,226,247],"benchmark":[153],"part":[157,178],"detection.":[158],"Then,":[159],"design":[161],"hierarchical":[163],"part-view":[164,189],"attention":[165,190,197,207,218],"aggregation":[166],"module":[167,191],"learn":[169],"global":[171],"representation":[173],"aggregating":[175],"features,":[179],"preserves":[181],"shapes.":[187],"The":[188,205,267],"hierarchically":[192],"leverages":[193],"part-level":[194,206],"view-level":[196,217],"increase":[199],"discriminability":[201],"features.":[204],"highlights":[208,219],"each":[213],"view":[214],"while":[215],"discriminative":[221],"all":[224],"object.":[230],"In":[231],"addition,":[232],"integrate":[234],"Recurrent":[236],"Neural":[237],"(RNN)":[239],"spatial":[243],"relationships":[244],"sequential":[246],"different":[249],"viewpoints.":[250],"Our":[251],"results":[252],"show":[259],"outperforms":[263],"other":[264],"methods.":[266],"available":[271],"https://github.com/liuxinhai/FG3D-Net.":[273]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2020-06-05T00:00:00"}