{"id":"https://openalex.org/W7131289660","doi":"https://doi.org/10.48550/arxiv.2602.18741","title":"Compact Hadamard Latent Codes for Efficient Spectral Rendering","display_name":"Compact Hadamard Latent Codes for Efficient Spectral Rendering","publication_year":2026,"publication_date":"2026-02-21","ids":{"openalex":"https://openalex.org/W7131289660","doi":"https://doi.org/10.48550/arxiv.2602.18741"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.18741","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126743873","display_name":"Jiaqi Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yu, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126732298","display_name":"Dar'ya Guarnera","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guarnera, Dar'ya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009336450","display_name":"Giuseppe Claudio Guarnera","orcid":"https://orcid.org/0000-0002-7703-5194"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guarnera, Giuseppe Claudio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5126743873"],"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":0.8220000267028809,"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":0.8220000267028809,"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/T11019","display_name":"Image Enhancement Techniques","score":0.053199999034404755,"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/T11666","display_name":"Color Science and Applications","score":0.03480000048875809,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.7125999927520752},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.6335999965667725},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.3693999946117401},{"id":"https://openalex.org/keywords/hadamard-transform","display_name":"Hadamard transform","score":0.35030001401901245},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.35010001063346863},{"id":"https://openalex.org/keywords/color-depth","display_name":"Color depth","score":0.3172000050544739}],"concepts":[{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.7125999927520752},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.6335999965667725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48249998688697815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4821000099182129},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4781000018119812},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4246000051498413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41909998655319214},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C60292330","wikidata":"https://www.wikidata.org/wiki/Q1014065","display_name":"Hadamard transform","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C91522604","wikidata":"https://www.wikidata.org/wiki/Q690110","display_name":"Color depth","level":5,"score":0.3172000050544739},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C2961294","wikidata":"https://www.wikidata.org/wiki/Q166863","display_name":"Color space","level":3,"score":0.29919999837875366},{"id":"https://openalex.org/C97991835","wikidata":"https://www.wikidata.org/wiki/Q910300","display_name":"Spectral color","level":5,"score":0.29120001196861267},{"id":"https://openalex.org/C2777021972","wikidata":"https://www.wikidata.org/wiki/Q22976830","display_name":"Uniqueness","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C4839761","wikidata":"https://www.wikidata.org/wiki/Q212111","display_name":"Spectral line","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.18741","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.18741","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.18741","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":"pmh:doi:10.48550/arxiv.2602.18741","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Spectral":[0,57],"rendering":[1,37,51,55,67],"accurately":[2],"reproduces":[3],"wavelength-dependent":[4],"appearance":[5],"but":[6],"is":[7,77,104,132],"computationally":[8],"expensive,":[9],"as":[10],"shading":[11],"must":[12],"be":[13],"evaluated":[14],"at":[15],"many":[16],"wavelength":[17],"samples":[18,139],"and":[19,33,81,89,93,149,155,188],"scales":[20],"roughly":[21],"linearly":[22],"with":[23,61],"the":[24,36,94,107,128,135,163,185,254],"number":[25,64,136],"of":[26,65,91,97,110,137,249],"samples.":[27],"It":[28],"also":[29],"requires":[30],"spectral":[31,42,50,84,138,221,255],"textures":[32],"lights":[34],"throughout":[35],"pipeline.":[38],"We":[39,114,141,231],"propose":[40],"Hadamard":[41,164],"codes,":[43,92,246],"a":[44,62,71,144,234],"compact":[45],"latent":[46,78,112,129,186,245],"representation":[47,121],"that":[48,116,152,202,239],"enables":[49],"using":[52,179],"standard":[53],"RGB":[54,66,175,182,212,241,251],"operations.":[56],"images":[58,176],"are":[59],"approximated":[60,105],"small":[63],"passes,":[68],"followed":[69],"by":[70,106],"decoding":[72],"step.":[73],"Our":[74],"key":[75],"requirement":[76],"linearity:":[79],"scaling":[80,88,154],"addition":[82,90,156],"in":[83,262],"space":[85],"correspond":[86],"to":[87,190,211,244],"element-wise":[95,108],"product":[96,109],"spectra":[98,126,192],"(for":[99],"example":[100],"reflectance":[101],"times":[102],"illumination)":[103],"their":[111],"codes.":[113],"show":[115],"an":[117,180],"exact":[118],"low-dimensional":[119],"algebra-preserving":[120],"cannot":[122],"exist":[123],"for":[124],"arbitrary":[125],"when":[127],"dimension":[130],"k":[131,167,203,224],"smaller":[133],"than":[134,218],"n.":[140],"therefore":[142],"introduce":[143,233],"learned":[145],"non-negative":[146],"linear":[147],"encoder":[148],"decoder":[150],"architecture":[151],"preserves":[153],"exactly":[157],"while":[158,214,257],"encouraging":[159],"approximate":[160],"multiplicativity":[161],"under":[162],"product.":[165],"With":[166],"=":[168,173,204,225],"6,":[169],"we":[170],"render":[171],"k/3":[172],"2":[174],"per":[177],"frame":[178],"unmodified":[181],"renderer,":[183],"reconstruct":[184],"image,":[187],"decode":[189],"high-resolution":[191],"or":[193,195],"XYZ":[194],"RGB.":[196],"Experiments":[197],"on":[198],"3D":[199],"scenes":[200],"demonstrate":[201],"6":[205],"significantly":[206],"reduces":[207],"color":[208],"error":[209],"compared":[210],"baselines":[213],"being":[215],"substantially":[216],"faster":[217],"naive":[219],"n-sample":[220],"rendering.":[222],"Using":[223],"9":[226],"provides":[227],"higher-quality":[228],"reference":[229],"results.":[230],"further":[232],"lightweight":[235],"neural":[236],"upsampling":[237],"network":[238],"maps":[240],"assets":[242],"directly":[243],"enabling":[247],"integration":[248],"legacy":[250],"content":[252],"into":[253],"pipeline":[256],"maintaining":[258],"perceptually":[259],"accurate":[260],"colors":[261],"rendered":[263],"images.":[264]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-02-25T00:00:00"}
