{"id":"https://openalex.org/W7160958414","doi":"https://doi.org/10.48550/arxiv.2605.10026","title":"MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving","display_name":"MUSDA: Multi-source Multi-modality Unsupervised Domain Adaptive 3D Object Detection for Autonomous Driving","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160958414","doi":"https://doi.org/10.48550/arxiv.2605.10026"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10026","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.10026","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135983061","display_name":"Xiaohu Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Xiaohu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107454689","display_name":"Hamed Khatounabadi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khatounabadi, Hamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135345059","display_name":"Hayder Radha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Radha, Hayder","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.46779999136924744,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.46779999136924744,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.4611000120639801,"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/T11448","display_name":"Face recognition and analysis","score":0.012000000104308128,"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/leverage","display_name":"Leverage (statistics)","score":0.7434999942779541},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6564000248908997},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6137999892234802},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5547999739646912},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48809999227523804},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.454800009727478},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41929998993873596},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3993000090122223}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.814300000667572},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7434999942779541},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.682200014591217},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6564000248908997},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6137999892234802},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5547999739646912},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48069998621940613},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.454800009727478},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41929998993873596},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3224000036716461},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2976999878883362},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2676999866962433},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10026","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.10026","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10026","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.7209309339523315,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1],"advancement":[2],"of":[3,130],"autonomous":[4,74],"driving,":[5],"numerous":[6],"annotated":[7],"multi-modality":[8,53,65],"datasets":[9,162],"have":[10],"become":[11],"available.":[12],"This":[13],"presents":[14],"an":[15],"opportunity":[16],"to":[17,145],"develop":[18,136],"domain-adaptive":[19],"3D":[20,70,159],"object":[21,71,160],"detectors":[22],"for":[23,63,73,109],"new":[24],"environments":[25],"without":[26],"relying":[27],"on":[28,39,133,155],"labor-intensive":[29],"manual":[30],"annotations.":[31],"However,":[32],"traditional":[33],"domain":[34,43,67,93,112],"adaptation":[35,68],"methods":[36],"typically":[37],"focus":[38],"a":[40,45,60,124,137],"single":[41,46],"source":[42,79,120,150,181],"or":[44],"modality,":[47],"limiting":[48],"their":[49],"effectiveness":[50],"in":[51,69],"multi-source,":[52,64],"scenarios.":[54],"In":[55],"this":[56],"paper,":[57],"we":[58,122,135],"propose":[59],"novel":[61],"framework":[62,87,173],"unsupervised":[66],"detection":[72,151,161],"driving.":[75],"Given":[76],"multiple":[77,119,149],"labeled":[78],"domains":[80],"and":[81,102,166,180],"one":[82],"unlabeled":[83],"target":[84],"domain,":[85],"our":[86,171],"first":[88],"introduces":[89],"hierarchical":[90],"spatially-conditioned":[91],"(HSC)":[92],"classifiers,":[94],"which":[95],"jointly":[96],"align":[97],"features":[98],"from":[99,118,148],"both":[100,178],"camera":[101],"LiDAR":[103],"modalities":[104,179],"at":[105],"two":[106],"distinct":[107],"levels":[108],"each":[110,128],"source-target":[111],"pair.":[113],"To":[114],"effectively":[115,174],"leverage":[116],"information":[117,176],"domains,":[121,182],"construct":[123],"prototype":[125,138],"graph":[126,139],"between":[127],"pair":[129],"domains.":[131],"Based":[132],"this,":[134],"weighted":[140],"(PGW)":[141],"multi-source":[142],"fusion":[143],"strategy":[144],"aggregate":[146],"predictions":[147],"heads.":[152],"Experimental":[153],"results":[154],"three":[156],"widely":[157],"used":[158],"-":[163,168],"Waymo,":[164],"nuScenes,":[165],"Lyft":[167],"demonstrate":[169],"that":[170],"proposed":[172],"integrates":[175],"across":[177],"consistently":[183],"outperforming":[184],"state-of-the-art":[185],"methods.":[186]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}