{"id":"https://openalex.org/W7167206113","doi":"https://doi.org/10.48550/arxiv.2607.01827","title":"C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation","display_name":"C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation","publication_year":2026,"publication_date":"2026-07-02","ids":{"openalex":"https://openalex.org/W7167206113","doi":"https://doi.org/10.48550/arxiv.2607.01827"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.01827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.01827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.01827","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139975920","display_name":"Jinlong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jinlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074455169","display_name":"XJ Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Xun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139962304","display_name":"Qiming Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Qiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140018538","display_name":"Shijia Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Shijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139993209","display_name":"Chenglu Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Chenglu","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":0.8986999988555908,"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":0.8986999988555908,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.01640000008046627,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11448","display_name":"Face recognition and analysis","score":0.009700000286102295,"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/boosting","display_name":"Boosting (machine learning)","score":0.6671000123023987},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6516000032424927},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5315999984741211},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4350000023841858},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.43459999561309814},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4309999942779541},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.40790000557899475},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4025999903678894}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6671000123023987},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6647999882698059},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6516000032424927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.614300012588501},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5315999984741211},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.43459999561309814},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4025999903678894},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3952000141143799},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3479999899864197},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3147999942302704},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.26109999418258667}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.01827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.01827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2607.01827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.01827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6395741105079651}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LiDAR-based":[0],"3D":[1,165,178],"object":[2],"detection":[3],"is":[4],"essential":[5],"for":[6],"autonomous":[7],"driving":[8],"systems.":[9],"However,":[10],"traditional":[11],"Ego-only":[12],"Perception":[13,35],"(Eo-Perception)":[14],"suffers":[15],"from":[16,113],"limited":[17],"perspective":[18],"and":[19,45,87,93,105,134,143,149,162],"occlusions":[20],"in":[21,102,177],"a":[22,57,174],"complex":[23],"outdoor":[24],"environment,":[25],"leading":[26],"to":[27,61,82,98,173],"performance":[28,121,180],"bottlenecks.":[29],"Recently,":[30],"research":[31],"on":[32,139],"multi-agent":[33],"Collaborative":[34],"(Co-Perception)":[36],"has":[37],"demonstrated":[38],"excellent":[39,120,164],"performance,":[40],"but":[41],"high":[42],"communication":[43,132,184],"costs":[44],"accumulated":[46],"pose":[47],"error":[48],"hinder":[49],"its":[50],"application.":[51],"To":[52],"address":[53],"this,":[54,114],"we":[55],"explore":[56],"novel":[58],"C2E":[59,110],"(Co-Perception":[60],"Eo-Perception)":[62],"paradigm":[63],"through":[64],"the":[65,109,119,128,140,147,158],"Multi-to-Single":[66],"(M2S)":[67],"agent":[68],"contrastive":[69],"knowledge":[70],"distillation":[71],"framework.":[72],"Our":[73,167],"M2S":[74,116,153,168],"framework":[75,154,169],"first":[76],"designs":[77],"Multi-Level":[78],"Feature":[79],"Enhancement":[80],"module":[81],"provide":[83],"more":[84],"stable":[85],"features,":[86],"introduces":[88],"Auxiliary":[89],"Point":[90],"Cloud":[91],"Reconstruction":[92],"Multi-Teacher":[94],"Contrastive":[95],"Distillation":[96],"mechanisms":[97],"mitigate":[99],"domain":[100],"gaps":[101],"point":[103],"cloud":[104],"feature":[106],"distributions":[107],"within":[108],"paradigm.":[111],"Benefiting":[112],"our":[115,152],"can":[117,170],"retain":[118],"of":[122,151],"collaborative":[123],"perception":[124],"while":[125],"effectively":[126],"avoiding":[127],"drawbacks,":[129],"such":[130],"as":[131],"delays":[133],"positioning":[135],"errors.":[136],"Extensive":[137],"experiments":[138],"V2XSet,":[141],"V2V4Real":[142],"DAIR-V2X":[144],"datasets":[145],"show":[146],"effectiveness":[148],"generalizability":[150],"when":[155],"combined":[156],"with":[157],"state-of-the-art":[159],"CoSDH":[160],"model":[161],"other":[163],"detectors.":[166],"deliver":[171],"up":[172],"8.64%":[175],"improvement":[176],"mAP":[179],"without":[181],"introducing":[182],"any":[183],"costs.":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-04T00:00:00"}