{"id":"https://openalex.org/W7154452686","doi":"https://doi.org/10.48550/arxiv.2604.12625","title":"Neural Dynamic GI: Random-Access Neural Compression for Temporal Lightmaps in Dynamic Lighting Environments","display_name":"Neural Dynamic GI: Random-Access Neural Compression for Temporal Lightmaps in Dynamic Lighting Environments","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154452686","doi":"https://doi.org/10.48550/arxiv.2604.12625"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12625","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.2604.12625","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133671574","display_name":"Jianhui Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Jianhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133725303","display_name":"Jian Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760218","display_name":"Zhi Zhou","orcid":"https://orcid.org/0000-0002-0987-9344"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Zhi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017524281","display_name":"Zhangjin Huang","orcid":"https://orcid.org/0000-0003-1475-8894"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Zhangjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100388742","display_name":"Chao Li","orcid":"https://orcid.org/0000-0003-2772-3244"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chao","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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9368000030517578,"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"}},"topics":[{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9368000030517578,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.032099999487400055,"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":0.009399999864399433,"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/data-compression","display_name":"Data compression","score":0.5419999957084656},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49889999628067017},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.49570000171661377},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.49380001425743103},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45750001072883606},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.45010000467300415},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.3610999882221222}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8282999992370605},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5419999957084656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5394999980926514},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49889999628067017},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.49570000171661377},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45750001072883606},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.45010000467300415},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4203000068664551},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.3610999882221222},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.34779998660087585},{"id":"https://openalex.org/C150178126","wikidata":"https://www.wikidata.org/wiki/Q18433212","display_name":"Dynamic range compression","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3125},{"id":"https://openalex.org/C118702147","wikidata":"https://www.wikidata.org/wiki/Q189396","display_name":"Dynamic random-access memory","level":3,"score":0.31049999594688416},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3012000024318695},{"id":"https://openalex.org/C194739806","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Computer data storage","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12625","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.2604.12625","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12625","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-quality":[0],"global":[1],"illumination":[2],"(GI)":[3],"in":[4,26,170,181],"real-time":[5,128,163],"rendering":[6],"is":[7],"commonly":[8],"achieved":[9],"using":[10],"precomputed":[11,180],"lighting":[12,28,34],"techniques,":[13],"with":[14,138,143,160],"lightmap":[15,65,178],"as":[16],"the":[17,79,91,106,114,122],"standard":[18],"choice.":[19],"To":[20,47,125,166],"support":[21],"GI":[22,55,151],"for":[23,63],"static":[24],"objects":[25],"dynamic":[27,150],"environments,":[29],"multiple":[30,85,182],"lightmaps":[31],"at":[32],"different":[33],"conditions":[35],"need":[36],"to":[37,77],"be":[38],"precomputed,":[39],"which":[40,88,109],"incurs":[41],"substantial":[42],"storage":[43,92,156],"and":[44,73,119,157],"memory":[45,158],"overhead.":[46,165],"overcome":[48],"this":[49,171],"limitation,":[50],"we":[51,97,130,173],"propose":[52],"Neural":[53],"Dynamic":[54],"(NDGI),":[56],"a":[57,99,133],"novel":[58],"compression":[59,101,112,123],"technique":[60],"specifically":[61],"designed":[62],"temporal":[64,80,177,186],"sets.":[66],"Our":[67],"method":[68],"utilizes":[69],"multi-dimensional":[70],"feature":[71,117],"maps":[72,118],"lightweight":[74],"neural":[75,140],"networks":[76],"integrate":[78,132],"information":[81],"instead":[82],"of":[83,94],"storing":[84],"sets":[86],"explicitly,":[87],"significantly":[89],"reduces":[90],"size":[93],"lightmaps.":[95],"Additionally,":[96],"introduce":[98],"block":[100],"(BC)":[102],"simulation":[103],"strategy":[104],"during":[105],"training":[107],"process,":[108],"enables":[110],"BC":[111],"on":[113],"final":[115],"generated":[116],"further":[120,168],"improves":[121],"ratio.":[124],"enable":[126],"efficient":[127],"decompression,":[129],"also":[131],"virtual":[134],"texturing":[135],"(VT)":[136],"system":[137],"our":[139,146,176],"representation.":[141],"Compared":[142],"prior":[144],"methods,":[145],"approach":[147],"achieves":[148],"high-quality":[149],"while":[152],"maintaining":[153],"remarkably":[154],"low":[155],"requirements,":[159],"only":[161],"modest":[162],"decompression":[164],"facilitate":[167],"research":[169],"direction,":[172],"will":[174],"release":[175],"dataset":[179],"scenes":[183],"featuring":[184],"diverse":[185],"variations.":[187]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}
