{"id":"https://openalex.org/W2149542945","doi":"https://doi.org/10.1109/cvpr.2013.403","title":"Label Propagation from ImageNet to 3D Point Clouds","display_name":"Label Propagation from ImageNet to 3D Point Clouds","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2149542945","doi":"https://doi.org/10.1109/cvpr.2013.403","mag":"2149542945"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2013.403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2013.403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-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/A5100322995","display_name":"Yan Wang","orcid":"https://orcid.org/0009-0002-0054-967X"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yan Wang","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016080094","display_name":"Rongrong Ji","orcid":"https://orcid.org/0000-0001-9163-2932"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rongrong Ji","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108217852","display_name":"Shih-Fu Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shih-Fu Chang","raw_affiliation_strings":["Department of Electrical Engineering, Columbia University, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Columbia University, USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100322995"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":11.3604,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.98165869,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3135","last_page":"3142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9987999796867371,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9984999895095825,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8343656063079834},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7983216047286987},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7150806188583374},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5731610059738159},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5602418184280396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5523326396942139},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5418409109115601},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5328254103660583},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5234046578407288},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.522809624671936},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.47377967834472656},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4175681173801422},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39483359456062317},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0971006453037262}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8343656063079834},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7983216047286987},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7150806188583374},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5731610059738159},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5602418184280396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5523326396942139},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5418409109115601},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5328254103660583},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5234046578407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.522809624671936},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.47377967834472656},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4175681173801422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39483359456062317},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0971006453037262},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2013.403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2013.403","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.394.1316","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.394.1316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ee.columbia.edu/ln/dvmm/publications/13/cvpr_pclabel.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.672.3882","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.3882","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Wang_Label_Propagation_from_2013_CVPR_paper.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1525032837","https://openalex.org/W1581592866","https://openalex.org/W1755850324","https://openalex.org/W1989684337","https://openalex.org/W2008706659","https://openalex.org/W2017814585","https://openalex.org/W2035652042","https://openalex.org/W2047221353","https://openalex.org/W2061629163","https://openalex.org/W2067191022","https://openalex.org/W2097374608","https://openalex.org/W2098355199","https://openalex.org/W2099940712","https://openalex.org/W2106471914","https://openalex.org/W2106723645","https://openalex.org/W2107023258","https://openalex.org/W2108598243","https://openalex.org/W2110764733","https://openalex.org/W2127911825","https://openalex.org/W2128715914","https://openalex.org/W2129091674","https://openalex.org/W2148820580","https://openalex.org/W2158305599","https://openalex.org/W2161969291","https://openalex.org/W2163384417","https://openalex.org/W2165698076","https://openalex.org/W2536208356","https://openalex.org/W2913759905","https://openalex.org/W2975698860","https://openalex.org/W4234358918","https://openalex.org/W4236508656","https://openalex.org/W4256017923","https://openalex.org/W4294240437","https://openalex.org/W6631562094","https://openalex.org/W6635091746","https://openalex.org/W6638172780","https://openalex.org/W6674646328","https://openalex.org/W6675895156","https://openalex.org/W6676297131","https://openalex.org/W6679152795","https://openalex.org/W6682324061"],"related_works":["https://openalex.org/W4244478748","https://openalex.org/W4223488648","https://openalex.org/W2134969820","https://openalex.org/W2251605416","https://openalex.org/W4389340727","https://openalex.org/W3150465815","https://openalex.org/W1997222214","https://openalex.org/W2802581102","https://openalex.org/W4205786897","https://openalex.org/W2070395303"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2],"witnessed":[3],"a":[4,15,70,97],"growing":[5],"interest":[6],"in":[7,14,32,159],"understanding":[8],"the":[9,30,51,93,108,118,134,162,166],"semantics":[10],"of":[11,18,80],"point":[12,21,36],"clouds":[13],"wide":[16],"variety":[17],"applications.":[19,128],"However,":[20],"cloud":[22],"labeling":[23],"remains":[24],"an":[25],"open":[26],"problem,":[27],"due":[28],"to":[29],"difficulty":[31],"acquiring":[33],"sufficient":[34],"3D":[35,104,150],"labels":[37],"towards":[38,125],"training":[39,115,151,163],"effective":[40],"classifiers.":[41],"In":[42],"this":[43,47],"paper,":[44],"we":[45],"overcome":[46],"challenge":[48],"by":[49],"utilizing":[50],"existing":[52],"massive":[53],"2D":[54],"semantic":[55],"labeled":[56],"datasets":[57],"from":[58,117,165],"decade-long":[59],"community":[60],"efforts,":[61],"such":[62],"as":[63],"Image":[64],"Net":[65],"and":[66,69,96,144],"Label":[67],"Me,":[68],"novel":[71,83],"``cross-domain''":[72],"label":[73,88],"propagation":[74],"approach.":[75],"Our":[76,153],"proposed":[77],"method":[78],"consists":[79],"two":[81],"major":[82,157],"components,":[84],"Exemplar":[85],"SVM":[86],"based":[87,100],"propagation,":[89],"which":[90],"effectively":[91],"addresses":[92],"cross-domain":[94],"issue,":[95],"graphical":[98],"model":[99],"contextual":[101],"refinement":[102],"incorporating":[103],"constraints.":[105],"Most":[106],"importantly,":[107],"entire":[109],"process":[110],"does":[111],"not":[112],"require":[113],"any":[114,149],"data":[116,164],"target":[119,167],"scenes,":[120],"also":[121],"with":[122,174],"good":[123],"scalability":[124],"large":[126],"scale":[127],"We":[129],"evaluate":[130],"our":[131],"approach":[132,154],"on":[133],"well-known":[135],"Cornell":[136],"Point":[137],"Cloud":[138],"Dataset,":[139],"achieving":[140],"much":[141],"greater":[142],"efficiency":[143],"comparable":[145],"accuracy":[146,160],"even":[147],"without":[148],"data.":[152],"shows":[155],"further":[156],"gains":[158],"when":[161],"scenes":[168],"is":[169],"used,":[170],"outperforming":[171],"state-of-the-art":[172],"approaches":[173],"far":[175],"better":[176],"efficiency.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":3}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}