{"id":"https://openalex.org/W3033419496","doi":"https://doi.org/10.1145/3366785","title":"Unsupervised Detection of Distinctive Regions on 3D Shapes","display_name":"Unsupervised Detection of Distinctive Regions on 3D Shapes","publication_year":2020,"publication_date":"2020-05-31","ids":{"openalex":"https://openalex.org/W3033419496","doi":"https://doi.org/10.1145/3366785","mag":"3033419496"},"language":"en","primary_location":{"id":"doi:10.1145/3366785","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3366785","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-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/A5101763340","display_name":"Xianzhi Li","orcid":"https://orcid.org/0000-0002-0231-720X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xianzhi Li","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-0231-720X","affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012581106","display_name":"Lequan Yu","orcid":"https://orcid.org/0000-0002-9315-6527"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Lequan Yu","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054382056","display_name":"Chi\u2010Wing Fu","orcid":"https://orcid.org/0000-0002-5238-593X"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chi-Wing Fu","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036688260","display_name":"Daniel Cohen\u2010Or","orcid":"https://orcid.org/0000-0001-6777-7445"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Daniel Cohen-Or","raw_affiliation_strings":["Tel Aviv University, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tel Aviv University, Israel","institution_ids":["https://openalex.org/I16391192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032708386","display_name":"Pheng\u2010Ann Heng","orcid":"https://orcid.org/0000-0003-3055-5034"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Pheng-Ann Heng","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.8496,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.94628726,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"39","issue":"5","first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9965000152587891,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9945999979972839,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.741763174533844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7342060804367065},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.7257600426673889},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.691766619682312},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6381720304489136},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5869314670562744},{"id":"https://openalex.org/keywords/optimal-distinctiveness-theory","display_name":"Optimal distinctiveness theory","score":0.5541210770606995},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5515518188476562},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4992530345916748},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.43959134817123413},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3374084234237671}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.741763174533844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7342060804367065},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7257600426673889},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.691766619682312},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6381720304489136},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5869314670562744},{"id":"https://openalex.org/C47385372","wikidata":"https://www.wikidata.org/wiki/Q7098943","display_name":"Optimal distinctiveness theory","level":2,"score":0.5541210770606995},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5515518188476562},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4992530345916748},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.43959134817123413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3374084234237671},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3366785","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3366785","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W398859631","https://openalex.org/W569478347","https://openalex.org/W2010209818","https://openalex.org/W2031878977","https://openalex.org/W2033832873","https://openalex.org/W2055132753","https://openalex.org/W2063513338","https://openalex.org/W2099789128","https://openalex.org/W2116323031","https://openalex.org/W2117183049","https://openalex.org/W2132303710","https://openalex.org/W2132914434","https://openalex.org/W2138621090","https://openalex.org/W2139468722","https://openalex.org/W2145185417","https://openalex.org/W2151132226","https://openalex.org/W2154278348","https://openalex.org/W2221121938","https://openalex.org/W2295107390","https://openalex.org/W2295382923","https://openalex.org/W2520774990","https://openalex.org/W2776207810","https://openalex.org/W2788158258","https://openalex.org/W2795374598","https://openalex.org/W2796426482","https://openalex.org/W2798777114","https://openalex.org/W2798991696","https://openalex.org/W2808920083","https://openalex.org/W2884154111","https://openalex.org/W2884585870","https://openalex.org/W2894798093","https://openalex.org/W2904174163","https://openalex.org/W2960986959","https://openalex.org/W2962858109","https://openalex.org/W2963053547","https://openalex.org/W2963158438","https://openalex.org/W2963264709","https://openalex.org/W2979750740","https://openalex.org/W2998584800","https://openalex.org/W3023742835","https://openalex.org/W4243953400","https://openalex.org/W4245551996","https://openalex.org/W4301045096","https://openalex.org/W7015786368"],"related_works":["https://openalex.org/W4225552138","https://openalex.org/W3107204728","https://openalex.org/W4287591324","https://openalex.org/W2032875729","https://openalex.org/W3046185751","https://openalex.org/W4226420367","https://openalex.org/W2980176872","https://openalex.org/W2477273251","https://openalex.org/W2947839263","https://openalex.org/W3120400911"],"abstract_inverted_index":{"This":[0],"article":[1],"presents":[2],"a":[3,41,63,79],"novel":[4],"approach":[5,128],"to":[6,51,62,71,112],"learn":[7,52,72,113],"and":[8,39,54,90,101,129,138],"detect":[9],"distinctive":[10],"regions":[11],"on":[12,30,117],"3D":[13,35,142],"shapes.":[14,120],"Unlike":[15],"previous":[16],"works,":[17],"which":[18],"require":[19],"labeled":[20],"data,":[21],"our":[22,127],"method":[23],"is":[24],"unsupervised.":[25],"We":[26,121],"conduct":[27],"the":[28,69,97,103,110,114,118],"analysis":[29],"point":[31,115],"sets":[32],"sampled":[33],"from":[34],"shapes,":[36,89],"then":[37],"formulate":[38],"train":[40],"deep":[42],"neural":[43],"network":[44,70,111],"for":[45,57,95,133],"an":[46,74,85,91],"unsupervised":[47,75],"shape":[48,65,135],"clustering":[49],"task":[50],"local":[53],"global":[55],"features":[56],"distinguishing":[58],"shapes":[59],"with":[60,84],"respect":[61],"given":[64],"set.":[66],"To":[67],"drive":[68],"in":[73,141],"manner,":[76],"we":[77,108],"design":[78],"clustering-based":[80],"nonparametric":[81],"softmax":[82],"classifier":[83],"iterative":[86],"re-clustering":[87],"of":[88,126],"adapted":[92],"contrastive":[93],"loss":[94],"enhancing":[96],"feature":[98],"embedding":[99],"quality":[100],"stabilizing":[102],"learning":[104],"process.":[105],"By":[106],"then,":[107],"encourage":[109],"distinctiveness":[116],"input":[119],"extensively":[122],"evaluate":[123],"various":[124],"aspects":[125],"present":[130],"its":[131],"applications":[132],"distinctiveness-guided":[134],"retrieval,":[136],"sampling,":[137],"view":[139],"selection":[140],"scenes.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}