{"id":"https://openalex.org/W3195311297","doi":"https://doi.org/10.1109/tpami.2022.3216606","title":"ST3D++: Denoised Self-Training for Unsupervised Domain Adaptation on 3D Object Detection","display_name":"ST3D++: Denoised Self-Training for Unsupervised Domain Adaptation on 3D Object Detection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W3195311297","doi":"https://doi.org/10.1109/tpami.2022.3216606","mag":"3195311297","pmid":"https://pubmed.ncbi.nlm.nih.gov/36279352"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3216606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3216606","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5075014670","display_name":"Jihan Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jihan Yang","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083443210","display_name":"Shaoshuai Shi","orcid":"https://orcid.org/0000-0003-2558-181X"},"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":"Shaoshuai Shi","raw_affiliation_strings":["Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0003-2558-181X","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100407596","display_name":"Zhe Wang","orcid":"https://orcid.org/0000-0002-0597-4475"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Wang","raw_affiliation_strings":["SenseTime Research, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-0597-4475","affiliations":[{"raw_affiliation_string":"SenseTime Research, Hong Kong","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100732450","display_name":"Hongsheng Li","orcid":"https://orcid.org/0000-0002-2664-7975"},"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":"Hongsheng Li","raw_affiliation_strings":["Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-2664-7975","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102498323","display_name":"Xiaojuan Qi","orcid":"https://orcid.org/0000-0002-4285-1626"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiaojuan Qi","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-4285-1626","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9798,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.95322066,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"45","issue":"5","first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/computer-science","display_name":"Computer science","score":0.7976040840148926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7492780685424805},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6425761580467224},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6232714056968689},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6197724938392639},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5892517566680908},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5605486631393433},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5000617504119873},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4880160391330719},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47706711292266846},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.47504520416259766},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.473328173160553},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.46348077058792114},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26472538709640503},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.163773775100708},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07483607530593872}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7976040840148926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7492780685424805},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6425761580467224},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6232714056968689},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6197724938392639},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5892517566680908},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5605486631393433},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5000617504119873},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4880160391330719},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47706711292266846},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.47504520416259766},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.473328173160553},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.46348077058792114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26472538709640503},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.163773775100708},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07483607530593872},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2022.3216606","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3216606","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:36279352","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36279352","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 pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":101,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1731081199","https://openalex.org/W1836465849","https://openalex.org/W2045724293","https://openalex.org/W2062227835","https://openalex.org/W2099471712","https://openalex.org/W2150066425","https://openalex.org/W2156557681","https://openalex.org/W2159291411","https://openalex.org/W2296073425","https://openalex.org/W2555618208","https://openalex.org/W2562192638","https://openalex.org/W2594718649","https://openalex.org/W2792643794","https://openalex.org/W2793888044","https://openalex.org/W2795155917","https://openalex.org/W2798965597","https://openalex.org/W2895281799","https://openalex.org/W2897529137","https://openalex.org/W2900234507","https://openalex.org/W2919534447","https://openalex.org/W2945328857","https://openalex.org/W2949708697","https://openalex.org/W2949813473","https://openalex.org/W2962687275","https://openalex.org/W2962793481","https://openalex.org/W2962808524","https://openalex.org/W2962912109","https://openalex.org/W2963107255","https://openalex.org/W2963121255","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963400571","https://openalex.org/W2963481481","https://openalex.org/W2963727135","https://openalex.org/W2963789515","https://openalex.org/W2963826681","https://openalex.org/W2963870446","https://openalex.org/W2964062501","https://openalex.org/W2964115968","https://openalex.org/W2966743431","https://openalex.org/W2968296999","https://openalex.org/W2968634921","https://openalex.org/W2970987681","https://openalex.org/W2979548969","https://openalex.org/W2981705647","https://openalex.org/W2981949127","https://openalex.org/W2982204955","https://openalex.org/W2985406498","https://openalex.org/W2995852213","https://openalex.org/W2997643725","https://openalex.org/W2998115938","https://openalex.org/W2998607115","https://openalex.org/W3004237909","https://openalex.org/W3008105217","https://openalex.org/W3034140121","https://openalex.org/W3034314779","https://openalex.org/W3034602892","https://openalex.org/W3034975685","https://openalex.org/W3035160371","https://openalex.org/W3035172746","https://openalex.org/W3035574168","https://openalex.org/W3035743198","https://openalex.org/W3035750252","https://openalex.org/W3043274762","https://openalex.org/W3095253761","https://openalex.org/W3096609285","https://openalex.org/W3126960890","https://openalex.org/W3127310617","https://openalex.org/W3131626050","https://openalex.org/W3134438579","https://openalex.org/W3174496366","https://openalex.org/W3175269419","https://openalex.org/W3175294391","https://openalex.org/W3176888779","https://openalex.org/W3177015573","https://openalex.org/W3183991776","https://openalex.org/W3206405742","https://openalex.org/W4230769173","https://openalex.org/W4310078553","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639480849","https://openalex.org/W6683341053","https://openalex.org/W6683633756","https://openalex.org/W6730623217","https://openalex.org/W6733814495","https://openalex.org/W6735050833","https://openalex.org/W6739778489","https://openalex.org/W6746282794","https://openalex.org/W6750109254","https://openalex.org/W6756787073","https://openalex.org/W6762753787","https://openalex.org/W6766648584","https://openalex.org/W6767325187","https://openalex.org/W6767486985","https://openalex.org/W6771395016","https://openalex.org/W6779510507","https://openalex.org/W6784697580","https://openalex.org/W6790641280","https://openalex.org/W6803241046"],"related_works":["https://openalex.org/W2745033168","https://openalex.org/W4287991909","https://openalex.org/W4390721878","https://openalex.org/W2945311944","https://openalex.org/W4327500857","https://openalex.org/W2965994363","https://openalex.org/W4311223090","https://openalex.org/W1689909837","https://openalex.org/W4205729548","https://openalex.org/W4301521271"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,107,136],"present":[4],"a":[5,11,115,138,145,194,238],"self-training":[6],"method,":[7],"named":[8],"ST3D++,":[9],"with":[10,60,101,114,181,270],"holistic":[12],"pseudo":[13,31,43,72,94,110,128,162,177],"label":[14,32,73,111],"denoising":[15],"pipeline":[16],"for":[17,216],"unsupervised":[18],"domain":[19,59,71,104,196],"adaptation":[20],"on":[21,45,55,174,205,229,245,263],"3D":[22,52,207,266],"object":[23,53,62,77,99,191,267],"detection.":[24],"ST3D++":[25,49,225],"aims":[26],"at":[27,276],"reducing":[28],"noise":[29,74],"in":[30,131,249],"generation":[33,112],"as":[34,36],"well":[35],"alleviating":[37],"the":[38,51,56,81,85,98,109,122,132,169,234,258,264],"negative":[39],"impacts":[40],"of":[41,80,126,251],"noisy":[42,153,161],"labels":[44,95],"model":[46,133,158],"training.":[47],"First,":[48],"pre-trains":[50],"detector":[54,86,100,170,192],"labeled":[57,163,178],"source":[58,82,139],"random":[61],"scaling":[63],"(ROS)":[64],"which":[65],"is":[66,87,203,274],"designed":[67],"to":[68,120,150,160,171,193],"reduce":[69],"target":[70,103,179,195,271],"arising":[75],"from":[76],"scale":[78],"bias":[79],"domain.":[83],"Then,":[84],"progressively":[88],"improved":[89],"through":[90],"alternating":[91],"between":[92],"generating":[93],"and":[96,124,144,156,185,214,223,255],"training":[97,134,142,183],"pseudo-labeled":[102],"data.":[105,164],"Here,":[106],"equip":[108],"process":[113],"hybrid":[116],"quality-aware":[117],"triplet":[118],"memory":[119],"improve":[121],"quality":[123],"stability":[125],"generated":[127],"labels.":[129],"Meanwhile,":[130],"stage,":[135],"propose":[137],"data":[140,147,180],"assisted":[141],"strategy":[143],"curriculum":[146],"augmentation":[148],"policy":[149],"effectively":[151,187],"rectify":[152],"gradient":[154],"directions":[155],"avoid":[157],"over-fitting":[159],"These":[165],"specific":[166],"designs":[167],"enable":[168],"be":[172],"trained":[173],"meticulously":[175],"refined":[176],"denoised":[182],"signals,":[184],"thus":[186],"facilitate":[188],"adapting":[189],"an":[190],"without":[197],"requiring":[198],"annotations.":[199],"Finally,":[200],"our":[201],"method":[202],"assessed":[204],"four":[206],"benchmark":[208,269],"datasets":[209],"(i.e.,":[210,220],"Waymo,":[211],"KITTI,":[212],"Lyft,":[213],"nuScenes)":[215],"three":[217],"common":[218],"categories":[219],"car,":[221],"pedestrian":[222],"bicycle).":[224],"achieves":[226],"state-of-the-art":[227],"performance":[228],"all":[230],"evaluated":[231],"settings,":[232],"outperforming":[233],"corresponding":[235],"baseline":[236],"by":[237],"large":[239],"margin":[240],"(e.g.,":[241],"9.6%":[242],"\u223c":[243],"38.16%":[244],"Waymo":[246],"\u2192":[247],"KITTI":[248,265],"terms":[250],"AP[Formula:":[252],"see":[253],"text]),":[254],"even":[256],"surpasses":[257],"fully":[259],"supervised":[260],"oracle":[261],"results":[262],"detection":[268],"prior.":[272],"Code":[273],"available":[275],"https://github.com/CVMI-Lab/ST3D.":[277]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":29},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2021-08-30T00:00:00"}