{"id":"https://openalex.org/W7148949110","doi":"https://doi.org/10.48550/arxiv.2604.02331","title":"EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors","display_name":"EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensors","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7148949110","doi":"https://doi.org/10.48550/arxiv.2604.02331"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02331","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02331","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.2604.02331","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132831465","display_name":"Luca Bartolomei","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bartolomei, Luca","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132873554","display_name":"Fabio Tosi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tosi, Fabio","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132863932","display_name":"Matteo Poggi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Poggi, Matteo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072569849","display_name":"Stefano Mattoccia","orcid":"https://orcid.org/0000-0002-3681-7704"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mattoccia, Stefano","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124267364","display_name":"Guillermo Gallego","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gallego, Guillermo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5132831465"],"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9754999876022339,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9754999876022339,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10036","display_name":"Advanced Neural Network Applications","score":0.00419999985024333,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.00139999995008111,"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/event","display_name":"Event (particle physics)","score":0.6148999929428101},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5774999856948853},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5475999712944031},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.48969998955726624},{"id":"https://openalex.org/keywords/event-data","display_name":"Event data","score":0.44909998774528503},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44269999861717224},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4293999969959259},{"id":"https://openalex.org/keywords/stereo-cameras","display_name":"Stereo cameras","score":0.4196999967098236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7138000130653381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6608999967575073},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6148999929428101},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5774999856948853},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5475999712944031},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.48969998955726624},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.44909998774528503},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4456999897956848},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4293999969959259},{"id":"https://openalex.org/C35861506","wikidata":"https://www.wikidata.org/wiki/Q17141434","display_name":"Stereo cameras","level":3,"score":0.4196999967098236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4187000095844269},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3637000024318695},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2791999876499176},{"id":"https://openalex.org/C40149104","wikidata":"https://www.wikidata.org/wiki/Q5620977","display_name":"Factory (object-oriented programming)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C68537008","wikidata":"https://www.wikidata.org/wiki/Q247932","display_name":"Stereopsis","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02331","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02331","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.2604.02331","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02331","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":[{"score":0.4786922037601471,"id":"https://metadata.un.org/sdg/16","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],"propose":[1],"EventHub,":[2],"a":[3],"novel":[4,38],"framework":[5],"for":[6],"training":[7,56],"deep-event":[8],"stereo":[9,66,78,89,110],"networks":[10],"without":[11],"ground":[12],"truth":[13],"annotations":[14,32,45],"from":[15,68],"costly":[16],"active":[17],"sensors,":[18],"relying":[19],"instead":[20],"on":[21,85],"standard":[22],"color":[23],"images.":[24],"From":[25],"these":[26],"images,":[27],"we":[28,63],"derive":[29],"either":[30],"proxy":[31,34,44],"and":[33,96],"events":[35],"through":[36],"state-of-the-art":[37,65],"view":[39],"synthesis":[40],"techniques,":[41],"or":[42],"simply":[43],"when":[46],"images":[47],"are":[48],"already":[49],"paired":[50],"with":[51,80],"event":[52,73,77,88],"data.":[53],"Using":[54],"the":[55,92,99,106],"set":[57],"generated":[58],"by":[59],"our":[60],"data":[61,101],"factory,":[62],"repurpose":[64],"models":[67,79,112],"RGB":[69,109],"literature":[70],"to":[71],"process":[72],"data,":[74],"obtaining":[75],"new":[76],"unprecedented":[81],"generalization":[82],"capabilities.":[83],"Experiments":[84],"widely":[86],"used":[87],"datasets":[90],"support":[91],"effectiveness":[93],"of":[94,108],"EventHub":[95],"show":[97],"how":[98],"same":[100],"distillation":[102],"mechanism":[103],"can":[104],"improve":[105],"accuracy":[107],"foundation":[111],"in":[113],"challenging":[114],"conditions":[115],"such":[116],"as":[117],"nighttime":[118],"scenes.":[119]},"counts_by_year":[],"updated_date":"2026-04-04T06:15:33.020886","created_date":"2026-04-04T00:00:00"}