{"id":"https://openalex.org/W7165617294","doi":"https://doi.org/10.48550/arxiv.2606.22617","title":"OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs","display_name":"OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs","publication_year":2026,"publication_date":"2026-06-21","ids":{"openalex":"https://openalex.org/W7165617294","doi":"https://doi.org/10.48550/arxiv.2606.22617"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22617","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.2606.22617","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103160077","display_name":"Hao Vo","orcid":"https://orcid.org/0009-0001-3908-4606"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vo, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136932704","display_name":"Phu Loc Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Phu Loc","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139204180","display_name":"Khoa Vo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vo, Khoa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116696777","display_name":"Sieu Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Sieu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139205363","display_name":"Duc Minh Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Duc Minh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117217167","display_name":"Ngo Xuan Cuong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cuong, Ngo Xuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135869809","display_name":"Nghi D. Q. Bui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bui, Nghi D. Q.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139179562","display_name":"Anh Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Anh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139168891","display_name":"Duy Minh Ho Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Duy Minh Ho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139177861","display_name":"Ngan Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Ngan","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8661999702453613,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8661999702453613,"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":0.03790000081062317,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.02630000002682209,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/epipolar-geometry","display_name":"Epipolar geometry","score":0.7124000191688538},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6176000237464905},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5618000030517578},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5163000226020813},{"id":"https://openalex.org/keywords/spatial-intelligence","display_name":"Spatial intelligence","score":0.49300000071525574},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.41999998688697815},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.3310000002384186}],"concepts":[{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.7124000191688538},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6176000237464905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6116999983787537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5972999930381775},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5618000030517578},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5163000226020813},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4950000047683716},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.49300000071525574},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C93717769","wikidata":"https://www.wikidata.org/wiki/Q745105","display_name":"Odometer","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C2911011789","wikidata":"https://www.wikidata.org/wiki/Q130741","display_name":"Hallucinating","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2757999897003174},{"id":"https://openalex.org/C29123130","wikidata":"https://www.wikidata.org/wiki/Q874709","display_name":"Computational geometry","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.266400009393692},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22617","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.2606.22617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22617","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"Large":[1],"Language":[2],"Models":[3],"(MLLMs)":[4],"have":[5],"achieved":[6],"remarkable":[7],"performance":[8],"on":[9,35,125],"2D":[10,69],"visual":[11],"tasks,":[12],"yet":[13,59],"enhancing":[14],"their":[15],"spatial":[16,65],"intelligence":[17],"for":[18,63],"real-world":[19],"applications":[20],"such":[21],"as":[22],"Autonomous":[23],"Vehicles":[24],"(AV)":[25],"remains":[26],"an":[27],"open":[28],"challenge.":[29],"Existing":[30],"geometry-aware":[31,64],"MLLMs":[32,77],"typically":[33],"rely":[34],"auxiliary":[36],"3D":[37,100],"models":[38],"at":[39],"inference":[40],"time,":[41],"introducing":[42],"pipeline":[43],"complexity":[44],"and":[45,84,98,135],"the":[46,115],"risk":[47,130],"of":[48],"cascading":[49],"failures.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54],"present":[55],"OmniSpace,":[56],"a":[57,89,93,99],"simple":[58],"effective":[60],"plug-and-play":[61],"paradigm":[62],"reasoning":[66],"from":[67],"purely":[68],"observations.":[70],"Motivated":[71],"by":[72,80,110],"our":[73],"finding":[74],"that":[75,104,120],"current":[76],"are":[78],"bottlenecked":[79],"weak":[81],"cross-view":[82],"correspondence":[83],"depth":[85],"estimation,":[86],"OmniSpace":[87,121],"introduces":[88],"Camera":[90],"Pose":[91],"Injector,":[92],"Multi-view":[94],"Epipolar":[95],"Attention":[96],"module,":[97],"Geometric":[101],"Distillation":[102],"objective":[103],"jointly":[105],"address":[106],"these":[107],"two":[108],"limitations":[109],"transferring":[111],"geometric":[112],"knowledge":[113],"into":[114],"model.":[116],"Extensive":[117],"experiments":[118],"show":[119],"surpasses":[122],"existing":[123],"methods":[124],"planning":[126],"benchmarks":[127],"(nuScenes,":[128],"Bench2Drive),":[129],"detection":[131],"(nuInstruct),":[132],"language":[133],"(Omnidrive),":[134],"generalization":[136],"(DriveBench).":[137]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}