{"id":"https://openalex.org/W7162778655","doi":"https://doi.org/10.48550/arxiv.2605.30060","title":"Towards Consistent Video Geometry Estimation","display_name":"Towards Consistent Video Geometry Estimation","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162778655","doi":"https://doi.org/10.48550/arxiv.2605.30060"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30060","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.2605.30060","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137349005","display_name":"Zhu Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137322185","display_name":"Jingnan Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jingnan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137343047","display_name":"Runmin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Runmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036054001","display_name":"Lingteng Qiu","orcid":"https://orcid.org/0000-0002-3250-0486"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Lingteng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137319707","display_name":"Zhengyi Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Zhengyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137327040","display_name":"Rui Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137314250","display_name":"Yichao Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Yichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006499051","display_name":"Kejie Qiu","orcid":"https://orcid.org/0000-0002-2516-3020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Kejie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137330585","display_name":"Siyu Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Siyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058796919","display_name":"Si-Yuan Cao","orcid":"https://orcid.org/0000-0001-5143-4501"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Zilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020795553","display_name":"Hui\u2010Liang Shen","orcid":"https://orcid.org/0000-0001-8469-019X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Si-Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Shen, Hui-Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Hui-Liang","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":0.6705999970436096,"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":0.6705999970436096,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.10440000146627426,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.03350000083446503,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5552999973297119},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4837999939918518},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4300000071525574},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4196999967098236},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.4162999987602234},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4058000147342682},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.32580000162124634},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.325300008058548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6955000162124634},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5552999973297119},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5465999841690063},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5432999730110168},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4300000071525574},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4196999967098236},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4058000147342682},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.325300008058548},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2689000070095062},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.25519999861717224},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30060","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.2605.30060","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30060","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":[{"display_name":"Sustainable cities and communities","score":0.5410379767417908,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,85],"work":[1],"presents":[2],"ViGeo,":[3],"a":[4,21,37,80,88],"feed-forward":[5],"foundation":[6],"model":[7,50],"for":[8],"recovering":[9],"spatially":[10],"dense":[11],"and":[12,33,54,60,97,100,109,116,140,147],"temporally":[13,107],"consistent":[14],"geometry":[15],"from":[16],"video":[17,89,148],"sequences.":[18],"Built":[19],"upon":[20],"plain":[22],"transformer":[23],"architecture":[24],"without":[25,71],"task-specific":[26],"architectural":[27],"modifications,":[28],"ViGeo":[29,119,133],"supports":[30],"streaming,":[31],"full-sequence,":[32],"long-video":[34,141],"inference":[35],"within":[36,124],"unified":[38],"model.":[39],"The":[40],"key":[41],"design":[42],"is":[43],"dynamic":[44],"chunking":[45],"attention,":[46],"which":[47],"exposes":[48],"the":[49,125],"to":[51,63,104],"both":[52],"bidirectional":[53],"causal":[55],"temporal":[56],"contexts":[57],"during":[58],"training":[59,112],"allows":[61],"it":[62],"adapt":[64],"its":[65],"attention":[66],"pattern":[67],"at":[68],"test":[69],"time":[70],"retraining.":[72],"To":[73],"improve":[74],"supervision":[75],"quality,":[76],"we":[77],"further":[78],"introduce":[79],"completion-based":[81],"data":[82],"refinement":[83],"framework.":[84,127],"framework":[86],"trains":[87],"depth":[90,115,142],"completion":[91],"teacher":[92],"that":[93],"conditions":[94],"on":[95,130],"sparse":[96],"noisy":[98],"annotations":[99],"exploits":[101],"video/multi-view":[102],"context":[103],"produce":[105],"dense,":[106],"coherent,":[108],"geometrically":[110],"reliable":[111],"targets.":[113],"Beyond":[114],"point":[117,149],"maps,":[118],"also":[120],"predicts":[121],"surface":[122,144],"normals":[123],"same":[126],"Trained":[128],"solely":[129],"public":[131],"datasets,":[132],"achieves":[134],"state-of-the-art":[135],"performance":[136],"across":[137],"online,":[138],"offline,":[139],"estimation,":[143,146],"normal":[145],"map":[150],"estimation.":[151]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}