{"id":"https://openalex.org/W7153352699","doi":"https://doi.org/10.48550/arxiv.2604.08542","title":"Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction","display_name":"Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7153352699","doi":"https://doi.org/10.48550/arxiv.2604.08542"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08542","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.08542","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133365964","display_name":"Tao Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xie, Tao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124010308","display_name":"Peishan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Peishan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Jin, Yudong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Yudong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133376638","display_name":"Yingfeng Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Yingfeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133358713","display_name":"Wei Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133383565","display_name":"Weiqiang Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Weiqiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133394460","display_name":"Qian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133394866","display_name":"Wei Hua","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hua, Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133329811","display_name":"Sida Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Sida","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133347443","display_name":"Xiaoyang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Xiaoyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133323685","display_name":"Xiaowei Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xiaowei","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":11,"corresponding_author_ids":["https://openalex.org/A5133365964"],"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.4781000018119812,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.4781000018119812,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.2280000001192093,"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":0.14970000088214874,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7321000099182129},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6394000053405762},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5608999729156494},{"id":"https://openalex.org/keywords/3d-reconstruction","display_name":"3D reconstruction","score":0.5436999797821045},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5146999955177307},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.46380001306533813},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4481000006198883},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4278999865055084},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.3749000132083893}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.760200023651123},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7321000099182129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6704999804496765},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6394000053405762},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5618000030517578},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5608999729156494},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5146999955177307},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.46380001306533813},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4278999865055084},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C2776863239","wikidata":"https://www.wikidata.org/wiki/Q7936601","display_name":"Visual hull","level":3,"score":0.3601999878883362},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.35679998993873596},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.33399999141693115},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3118000030517578},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29409998655319214},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2718999981880188}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08542","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.08542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08542","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5899571776390076,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,57,71,75,101,153,161],"task":[4],"of":[5,74,122,163],"large-scale":[6,150],"3D":[7,25,32,176],"scene":[8,76,98],"reconstruction":[9,16,44,110,177],"from":[10,27],"long":[11,49],"video":[12],"sequences.":[13],"Recent":[14],"feed-forward":[15],"models":[17],"have":[18],"shown":[19],"promising":[20],"results":[21],"by":[22,82],"directly":[23],"regressing":[24],"geometry":[26],"RGB":[28],"images":[29],"without":[30,141],"explicit":[31],"priors":[33],"or":[34],"geometric":[35],"constraints.":[36],"However,":[37],"these":[38],"methods":[39],"often":[40],"struggle":[41],"to":[42,52,59,77,103],"maintain":[43],"accuracy":[45,111,173,178],"and":[46,56,95,112,156,174],"consistency":[47],"over":[48],"sequences":[50],"due":[51],"limited":[53],"memory":[54,139],"capacity":[55,140],"inability":[58],"effectively":[60],"capture":[61],"global":[62,72,89],"contextual":[63,106],"cues.":[64],"In":[65],"contrast,":[66],"humans":[67],"can":[68],"naturally":[69],"exploit":[70],"understanding":[73],"inform":[78],"local":[79],"perception.":[80],"Motivated":[81],"this,":[83],"we":[84],"propose":[85],"a":[86,120],"novel":[87],"neural":[88,124],"context":[90,115],"representation":[91,116],"that":[92,126],"efficiently":[93],"compresses":[94],"retains":[96],"long-range":[97],"information,":[99],"enabling":[100],"model":[102],"leverage":[104],"extensive":[105],"cues":[107],"for":[108],"enhanced":[109],"consistency.":[113],"The":[114,146],"is":[117,183],"realized":[118],"through":[119],"set":[121],"lightweight":[123],"sub-networks":[125],"are":[127],"rapidly":[128],"adapted":[129],"during":[130],"test":[131],"time":[132],"via":[133],"self-supervised":[134],"objectives,":[135],"which":[136],"substantially":[137],"increases":[138],"incurring":[142],"significant":[143],"computational":[144],"overhead.":[145],"experiments":[147],"on":[148],"multiple":[149],"benchmarks,":[151],"including":[152],"KITTI":[154],"Odometry~\\cite{Geiger2012CVPR}":[155],"Oxford":[157],"Spires~\\cite{tao2025spires}":[158],"datasets,":[159],"demonstrate":[160],"effectiveness":[162],"our":[164],"approach":[165],"in":[166],"handling":[167],"ultra-large":[168],"scenes,":[169],"achieving":[170],"leading":[171],"pose":[172],"state-of-the-art":[175],"while":[179],"maintaining":[180],"efficiency.":[181],"Code":[182],"available":[184],"at":[185],"https://zju3dv.github.io/scal3r.":[186]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-04-11T00:00:00"}
