{"id":"https://openalex.org/W7147515006","doi":"https://doi.org/10.48550/arxiv.2603.27995","title":"UniDA3D: A Unified Domain-Adaptive Framework for Multi-View 3D Object Detection","display_name":"UniDA3D: A Unified Domain-Adaptive Framework for Multi-View 3D Object Detection","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147515006","doi":"https://doi.org/10.48550/arxiv.2603.27995"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27995","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27995","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":null,"license_id":null,"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.2603.27995","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132624678","display_name":"Hongjing Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Hongjing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132578597","display_name":"Cheng Chi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chi, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132624362","display_name":"Jinlin Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jinlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113968850","display_name":"Yanzhao Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Yanzhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132625617","display_name":"Zhen Lei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132603695","display_name":"Wenqi Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Wenqi","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/T10036","display_name":"Advanced Neural Network Applications","score":0.9341999888420105,"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":0.9341999888420105,"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":0.032600000500679016,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.003599999938160181,"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/object-detection","display_name":"Object detection","score":0.7215999960899353},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6351000070571899},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6144000291824341},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6108999848365784},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5403000116348267},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4900999963283539},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.47290000319480896},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4717999994754791},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46389999985694885},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.41830000281333923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7750999927520752},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7215999960899353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6525999903678894},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6351000070571899},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6144000291824341},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6108999848365784},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4943999946117401},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4717999994754791},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46389999985694885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45239999890327454},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.41830000281333923},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.3822999894618988},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2612999975681305},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27995","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27995","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.27995","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27995","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Camera-only":[0],"3D":[1,20,80,194,200,223],"object":[2,21,81,119],"detection":[3,22],"is":[4],"critical":[5],"for":[6,84,178],"autonomous":[7],"driving,":[8],"offering":[9],"a":[10,26,76,99,108,141,183,197],"cost-effective":[11],"alternative":[12],"to":[13,30,58,117,157,171],"LiDAR":[14],"based":[15],"methods.":[16],"In":[17,169],"particular,":[18],"multi-view":[19,79,199,222],"has":[23],"emerged":[24],"as":[25,51,98],"promising":[27],"direction":[28],"due":[29,57],"its":[31],"balanced":[32],"trade-off":[33],"between":[34,121],"performance":[35,44],"and":[36,54,95,106,123,129,135,151,161,207,214,233],"cost.":[37],"However,":[38],"existing":[39],"methods":[40],"often":[41],"suffer":[42],"significant":[43],"degradation":[45],"under":[46,87,225],"complex":[47],"environmental":[48],"conditions":[49],"such":[50],"nighttime,":[52,93,205],"fog,":[53],"rain,":[55],"primarily":[56],"their":[59],"reliance":[60],"on":[61],"training":[62,145,177,186],"data":[63],"collected":[64],"mostly":[65],"in":[66,165,231],"ideal":[67],"conditions.":[68,90],"To":[69],"address":[70],"this":[71],"challenge,":[72],"we":[73,139],"propose":[74],"UniDA3D,":[75],"unified":[77,100,185],"domain-adaptive":[78,142],"detector":[82],"designed":[83],"robust":[85,192],"perception":[86],"diverse":[88],"adverse":[89],"UniDA3D":[91,181,216],"formulates":[92],"rainy,":[94,206],"foggy":[96,208],"scenes":[97],"multi":[101],"target":[102,124,167],"domain":[103,112],"adaptation":[104],"problem":[105],"leverages":[107],"novel":[109],"query":[110],"guided":[111],"discrepancy":[113],"mitigation":[114],"(QDDM)":[115],"module":[116],"align":[118],"features":[120],"source":[122],"domains":[125],"at":[126],"both":[127],"batch":[128],"global":[130],"levels":[131],"via":[132],"query-centric":[133],"adversarial":[134],"contrastive":[136],"learning.":[137],"Furthermore,":[138],"introduce":[140],"teacher":[143,150],"student":[144],"pipeline":[146],"with":[147],"an":[148],"exponential-moving-average":[149],"dynamically":[152],"updated":[153],"high-quality":[154],"pseudo":[155],"labels":[156],"enhance":[158],"consistency":[159],"learning":[160],"suppress":[162],"background":[163],"noise":[164],"unlabeled":[166],"domains.":[168],"contrast":[170],"prior":[172],"approaches":[173],"that":[174],"require":[175],"separate":[176],"each":[179],"condition,":[180],"performs":[182],"single":[184],"process":[187],"across":[188],"multiple":[189],"domains,":[190],"enabling":[191],"all-weather":[193],"perception.":[195],"On":[196],"synthesized":[198],"benchmark":[201],"constructed":[202],"by":[203],"generating":[204],"counterparts":[209],"from":[210],"nuScenes":[211],"(nuScenes-Night,":[212],"nuScenes-Rain,":[213],"nuScenes-Haze),":[215],"consistently":[217],"outperforms":[218],"state":[219],"of-the-art":[220],"camera-only":[221],"detectors":[224],"extreme":[226],"conditions,":[227],"achieving":[228],"substantial":[229],"gains":[230],"mAP":[232],"NDS":[234],"while":[235],"maintaining":[236],"real-time":[237],"inference":[238],"efficiency.":[239]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2026-04-02T00:00:00"}
