{"id":"https://openalex.org/W7161247360","doi":"https://doi.org/10.48550/arxiv.2605.15195","title":"VGGT-$\u03a9$","display_name":"VGGT-$\u03a9$","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161247360","doi":"https://doi.org/10.48550/arxiv.2605.15195"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.15195","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15195","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.2605.15195","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136225078","display_name":"Jianyuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136255386","display_name":"Minghao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Minghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041840752","display_name":"Shangzhan Zhang","orcid":"https://orcid.org/0009-0006-4693-6009"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shangzhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005136810","display_name":"Nikita Karaev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karaev, Nikita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136200746","display_name":"Johannes Sch\u00f6nberger","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sch\u00f6nberger, Johannes","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067188801","display_name":"Patrick Labatut","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Labatut, Patrick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035420035","display_name":"Piotr Bojanowski","orcid":"https://orcid.org/0000-0001-8098-5900"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bojanowski, Piotr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136209885","display_name":"David Novotny","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Novotny, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136266391","display_name":"Andrea Vedaldi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vedaldi, Andrea","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136271298","display_name":"Christian Rupprecht","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rupprecht, Christian","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/T10531","display_name":"Advanced Vision and Imaging","score":0.16349999606609344,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.16349999606609344,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.15459999442100525,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11590000241994858,"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":0.6383000016212463},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5920000076293945},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5389999747276306},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4235000014305115},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.3901999890804291},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.3617999851703644},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3479999899864197},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3377000093460083}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8421000242233276},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6383000016212463},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5389999747276306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5074999928474426},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4235000014305115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41589999198913574},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3479999899864197},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.326200008392334},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.29989999532699585},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2632000148296356},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.2558000087738037},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.15195","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15195","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.2605.15195","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15195","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":{"Recent":[0],"feed-forward":[1],"reconstruction":[2,48,183,224],"models,":[3],"such":[4],"as":[5],"VGGT,":[6],"have":[7],"proven":[8],"competitive":[9],"with":[10,34,101,161,220],"traditional":[11],"optimization-based":[12],"reconstructors":[13],"while":[14],"also":[15,112,207],"providing":[16],"geometry-aware":[17],"features":[18],"useful":[19],"for":[20,53,182,192,233],"other":[21],"tasks.":[22],"Here,":[23],"we":[24,68],"show":[25,208],"that":[26,72,81,209,223],"the":[27,106,151,196,210],"quality":[28],"of":[29,150,154,174,184],"these":[30,133],"models":[31,216],"scales":[32],"predictably":[33],"model":[35,63],"and":[36,51,56,85,104,123,169,186,217,229],"data":[37,78,165],"size.":[38],"We":[39,90,111,206],"do":[40],"so":[41],"by":[42,94,204],"introducing":[43],"VGGT-$\u03a9$,":[44],"which":[45,127],"substantially":[46],"improves":[47],"accuracy,":[49],"efficiency,":[50,75],"capabilities":[52],"both":[54],"static":[55,185],"dynamic":[57,83,187],"scenes.":[58],"To":[59],"enable":[60],"training":[61,74],"this":[62,141],"at":[64],"an":[65],"unprecedented":[66],"scale,":[67],"introduce":[69,124],"architectural":[70],"changes":[71],"improve":[73,214],"a":[76,86,96,120,227],"high-quality":[77],"annotation":[79],"pipeline":[80],"supports":[82],"scenes,":[84],"self-supervised":[87],"learning":[88],"protocol.":[89],"simplify":[91],"VGGT's":[92],"architecture":[93],"using":[95],"single":[97],"dense":[98],"prediction":[99],"head":[100],"multi-task":[102],"supervision":[103],"removing":[105],"expensive":[107],"high-resolution":[108],"convolutional":[109],"layers.":[110],"use":[113],"registers":[114,212],"to":[115,132,159,170],"aggregate":[116],"scene":[117],"information":[118,130],"into":[119],"compact":[121],"representation":[122],"register":[125],"attention,":[126],"restricts":[128],"inter-frame":[129],"exchange":[131],"registers,":[134],"in":[135],"part":[136],"replacing":[137],"global":[138],"attention.":[139],"In":[140],"way,":[142],"during":[143],"training,":[144],"VGGT-$\u03a9$":[145,178],"uses":[146],"only":[147],"about":[148],"30%":[149],"GPU":[152],"memory":[153],"its":[155],"predecessor,":[156],"allowing":[157],"us":[158],"train":[160],"15x":[162],"more":[163],"supervised":[164],"than":[166],"prior":[167],"work":[168],"leverage":[171],"vast":[172],"amounts":[173],"unlabeled":[175],"video":[176],"data.":[177],"achieves":[179],"strong":[180],"results":[181],"scenes":[188],"across":[189],"multiple":[190],"benchmarks,":[191],"example,":[193],"improving":[194],"over":[195],"previous":[197],"best":[198],"camera":[199],"estimation":[200],"accuracy":[201],"on":[202],"Sintel":[203],"77%.":[205],"learned":[211],"can":[213,225],"vision-language-action":[215],"support":[218],"alignment":[219],"language,":[221],"suggesting":[222],"be":[226],"powerful":[228],"scalable":[230],"proxy":[231],"task":[232],"spatial":[234],"understanding.":[235],"Project":[236],"Page:":[237],"http://vggt-omega.github.io/":[238]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}