{"id":"https://openalex.org/W7151265010","doi":"https://doi.org/10.48550/arxiv.2604.04402","title":"UENR-600K: A Large-Scale Physically Grounded Dataset for Nighttime Video Deraining","display_name":"UENR-600K: A Large-Scale Physically Grounded Dataset for Nighttime Video Deraining","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7151265010","doi":"https://doi.org/10.48550/arxiv.2604.04402"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04402","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04402","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.2604.04402","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133107643","display_name":"Pei Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Pei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133129507","display_name":"Hai Ci","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ci, Hai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133117067","display_name":"Beibei Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Beibei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133123922","display_name":"Yiren Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Yiren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133077312","display_name":"Mike Zheng Shou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shou, Mike Zheng","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/T11019","display_name":"Image Enhancement Techniques","score":0.991599977016449,"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/T11019","display_name":"Image Enhancement Techniques","score":0.991599977016449,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/baseline","display_name":"Baseline (sea)","score":0.5486999750137329},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.5038999915122986},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4584999978542328},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.41019999980926514},{"id":"https://openalex.org/keywords/overlay","display_name":"Overlay","score":0.39559999108314514},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.359499990940094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7314000129699707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5756000280380249},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.5038999915122986},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4584999978542328},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C136085584","wikidata":"https://www.wikidata.org/wiki/Q910289","display_name":"Overlay","level":2,"score":0.39559999108314514},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.374099999666214},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.359499990940094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.337799996137619},{"id":"https://openalex.org/C2776196576","wikidata":"https://www.wikidata.org/wiki/Q196113","display_name":"Camouflage","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3019999861717224},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.27140000462532043}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04402","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04402","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.2604.04402","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04402","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","id":"https://metadata.un.org/sdg/11","score":0.7821240425109863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Nighttime":[0],"video":[1,134],"deraining":[2,140],"is":[3],"uniquely":[4],"challenging":[5],"because":[6,59],"raindrops":[7],"interact":[8],"with":[9],"artificial":[10],"lighting.":[11],"Unlike":[12],"daytime":[13],"white":[14],"rain,":[15],"nighttime":[16,55],"rain":[17,33,60,94,117],"takes":[18],"on":[19,31,163],"various":[20],"colors":[21],"and":[22,35,105],"appears":[23],"locally":[24],"illuminated.":[25],"Existing":[26],"small-scale":[27],"synthetic":[28],"datasets":[29],"rely":[30],"2D":[32],"overlays":[34],"fail":[36],"to":[37,44,47,92,150,169],"capture":[38],"these":[39],"physical":[40],"properties,":[41],"causing":[42],"models":[43,161],"generalize":[45,166],"poorly":[46],"real-world":[48,170],"night":[49],"rain.":[50],"Meanwhile,":[51],"capturing":[52,109],"real":[53,107],"paired":[54],"videos":[56],"remains":[57],"impractical":[58],"effects":[61],"cannot":[62],"be":[63],"isolated":[64],"from":[65],"other":[66],"degradations":[67],"like":[68,112],"sensor":[69],"noise.":[70],"To":[71],"bridge":[72,153],"this":[73,120],"gap,":[74],"we":[75,123],"introduce":[76],"UENR-600K,":[77],"a":[78,125,142],"large-scale,":[79],"physically":[80,106],"grounded":[81],"dataset":[82,165],"containing":[83],"600,000":[84],"1080p":[85],"frame":[86],"pairs.":[87],"We":[88],"utilize":[89],"Unreal":[90],"Engine":[91],"simulate":[93],"as":[95,141],"3D":[96],"particles":[97],"within":[98],"virtual":[99],"environments.":[100],"This":[101],"approach":[102],"guarantees":[103],"photorealism":[104],"raindrops,":[108],"correct":[110],"details":[111],"color":[113],"refractions,":[114],"scene":[115],"occlusions,":[116],"curtains.":[118],"Leveraging":[119],"high-quality":[121],"data,":[122],"establish":[124],"new":[126],"state-of-the-art":[127],"baseline":[128,138],"by":[129],"adapting":[130],"the":[131,154],"Wan":[132],"2.2":[133],"generation":[135,144],"model.":[136],"Our":[137],"treat":[139],"video-to-video":[143],"task,":[145],"exploiting":[146],"strong":[147],"generative":[148],"priors":[149],"almost":[151],"entirely":[152],"sim-to-real":[155],"gap.":[156],"Extensive":[157],"benchmarking":[158],"demonstrates":[159],"that":[160],"trained":[162],"our":[164],"significantly":[167],"better":[168],"videos.":[171],"Project":[172],"page:":[173],"https://showlab.github.io/UENR-600K/.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}