{"id":"https://openalex.org/W7151445511","doi":"https://doi.org/10.48550/arxiv.2604.03878","title":"Learning 3D Reconstruction with Priors in Test Time","display_name":"Learning 3D Reconstruction with Priors in Test Time","publication_year":2026,"publication_date":"2026-04-04","ids":{"openalex":"https://openalex.org/W7151445511","doi":"https://doi.org/10.48550/arxiv.2604.03878"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03878","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03878","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.2604.03878","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133094723","display_name":"Lei Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133145119","display_name":"Haoyu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Haoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058219160","display_name":"Akshat Dave","orcid":"https://orcid.org/0000-0003-0560-632X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dave, Akshat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133107742","display_name":"Dimitris Samaras","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Samaras, Dimitris","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":0.6973000168800354,"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":0.6973000168800354,"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":0.13660000264644623,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.06369999796152115,"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/prior-probability","display_name":"Prior probability","score":0.928600013256073},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5636000037193298},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.4740999937057495},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.3603000044822693},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.35109999775886536},{"id":"https://openalex.org/keywords/3d-reconstruction","display_name":"3D reconstruction","score":0.3305000066757202},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.32179999351501465}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.928600013256073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6417999863624573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5807999968528748},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5636000037193298},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46000000834465027},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3434999883174896},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32600000500679016},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3190000057220459},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2606000006198883},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C14961307","wikidata":"https://www.wikidata.org/wiki/Q5377176","display_name":"Energy minimization","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03878","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03878","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.2604.03878","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03878","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":[{"id":"https://metadata.un.org/sdg/16","score":0.4053597152233124,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,56,103,126],"test-time":[3,166],"framework":[4,170],"for":[5,171],"multiview":[6],"Transformers":[7],"(MVTs)":[8],"that":[9],"incorporates":[10],"priors":[11,32,91,173],"(e.g.,":[12],"camera":[13,114],"poses,":[14],"intrinsics,":[15],"and":[16,44,59,72,85,113,133],"depth)":[17],"to":[18],"improve":[19],"3D":[20,106,175],"tasks":[21],"without":[22],"retraining":[23],"or":[24,77],"modifying":[25],"pre-trained":[26],"image-only":[27,151],"networks.":[28],"Rather":[29],"than":[30,145],"feeding":[31],"into":[33,94,174],"the":[34,42,46,67,98,130,139,149,162],"architecture,":[35],"we":[36],"cast":[37],"them":[38],"as":[39],"constraints":[40],"on":[41,97],"predictions":[43,71],"optimize":[45],"network":[47],"at":[48],"inference":[49],"time.":[50],"The":[51,63],"optimization":[52,168],"loss":[53,79],"consists":[54],"of":[55,105,164],"self-supervised":[57,64],"objective":[58,65],"prior":[60],"penalty":[61,95],"terms.":[62],"captures":[66],"compatibility":[68],"among":[69],"multi-view":[70],"is":[73],"implemented":[74],"using":[75],"photometric":[76],"geometric":[78],"between":[80],"renderings":[81],"from":[82],"other":[83],"views":[84],"each":[86],"view":[87],"itself.":[88],"Any":[89],"available":[90],"are":[92],"converted":[93],"terms":[96],"corresponding":[99],"output":[100],"modalities.":[101],"Across":[102],"series":[104],"vision":[107,176],"benchmarks,":[108],"including":[109],"point":[110],"map":[111],"estimation":[112],"pose":[115],"estimation,":[116],"our":[117,136,165],"method":[118,137,154],"consistently":[119],"improves":[120],"performance":[121],"over":[122],"base":[123,150],"MVTs":[124],"by":[125,143],"large":[127],"margin.":[128],"On":[129],"ETH3D,":[131],"7-Scenes,":[132],"NRGBD":[134],"datasets,":[135],"reduces":[138],"point-map":[140],"distance":[141],"error":[142],"more":[144],"half":[146],"compared":[147],"with":[148],"models.":[152],"Our":[153],"also":[155],"outperforms":[156],"retrained":[157],"prior-aware":[158],"feed-forward":[159],"methods,":[160],"demonstrating":[161],"effectiveness":[163],"constrained":[167],"(TCO)":[169],"incorporating":[172],"tasks.":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
