{"id":"https://openalex.org/W2810300686","doi":"https://doi.org/10.1145/3197517.3201279","title":"Efficient reflectance capture using an autoencoder","display_name":"Efficient reflectance capture using an autoencoder","publication_year":2018,"publication_date":"2018-07-30","ids":{"openalex":"https://openalex.org/W2810300686","doi":"https://doi.org/10.1145/3197517.3201279","mag":"2810300686"},"language":"en","primary_location":{"id":"doi:10.1145/3197517.3201279","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3197517.3201279","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005722947","display_name":"Kaizhang Kang","orcid":"https://orcid.org/0009-0004-4381-0510"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaizhang Kang","raw_affiliation_strings":["Zhejiang University, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103218695","display_name":"Zimin Chen","orcid":"https://orcid.org/0000-0002-6673-6438"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zimin Chen","raw_affiliation_strings":["Zhejiang University, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103500807","display_name":"Jiaping Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaping Wang","raw_affiliation_strings":["Sinovation Ventures"],"affiliations":[{"raw_affiliation_string":"Sinovation Ventures","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027671723","display_name":"Kun Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Zhou","raw_affiliation_strings":["Zhejiang University, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025339003","display_name":"Hongzhi Wu","orcid":"https://orcid.org/0000-0002-4404-2275"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongzhi Wu","raw_affiliation_strings":["Zhejiang University, China"],"affiliations":[{"raw_affiliation_string":"Zhejiang University, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5005722947"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":20.1481,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.99484189,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"37","issue":"4","first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9997000098228455,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9987000226974487,"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/autoencoder","display_name":"Autoencoder","score":0.9132989645004272},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7944536209106445},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6981178522109985},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6616197228431702},{"id":"https://openalex.org/keywords/bidirectional-reflectance-distribution-function","display_name":"Bidirectional reflectance distribution function","score":0.6187194585800171},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.578738808631897},{"id":"https://openalex.org/keywords/reflectivity","display_name":"Reflectivity","score":0.5575441718101501},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5126213431358337},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5012874603271484},{"id":"https://openalex.org/keywords/data-acquisition","display_name":"Data acquisition","score":0.4151567816734314},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3713688254356384},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3322877883911133},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.11681857705116272},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06841570138931274},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06743291020393372}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9132989645004272},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7944536209106445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6981178522109985},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6616197228431702},{"id":"https://openalex.org/C151596937","wikidata":"https://www.wikidata.org/wiki/Q856980","display_name":"Bidirectional reflectance distribution function","level":3,"score":0.6187194585800171},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.578738808631897},{"id":"https://openalex.org/C108597893","wikidata":"https://www.wikidata.org/wiki/Q663650","display_name":"Reflectivity","level":2,"score":0.5575441718101501},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5126213431358337},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5012874603271484},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.4151567816734314},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3713688254356384},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3322877883911133},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.11681857705116272},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06841570138931274},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06743291020393372},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3197517.3201279","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3197517.3201279","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G813091519","display_name":null,"funder_award_id":"61772457,U1609215","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1486787228","https://openalex.org/W1966385659","https://openalex.org/W1977213188","https://openalex.org/W1994352258","https://openalex.org/W2000123870","https://openalex.org/W2008966759","https://openalex.org/W2024688693","https://openalex.org/W2037242534","https://openalex.org/W2055465094","https://openalex.org/W2069053895","https://openalex.org/W2094673986","https://openalex.org/W2097376033","https://openalex.org/W2100495367","https://openalex.org/W2102862737","https://openalex.org/W2103788848","https://openalex.org/W2105096195","https://openalex.org/W2106726792","https://openalex.org/W2120978234","https://openalex.org/W2159004935","https://openalex.org/W2242654827","https://openalex.org/W2296661961","https://openalex.org/W2475362300","https://openalex.org/W2549156558","https://openalex.org/W2554498528","https://openalex.org/W2557283755","https://openalex.org/W2736596523","https://openalex.org/W3000666251","https://openalex.org/W3005895305","https://openalex.org/W4245596045"],"related_works":["https://openalex.org/W1996671438","https://openalex.org/W4382897192","https://openalex.org/W2611841783","https://openalex.org/W2063805703","https://openalex.org/W2158803563","https://openalex.org/W2000112597","https://openalex.org/W2774256758","https://openalex.org/W2026859948","https://openalex.org/W2538601840","https://openalex.org/W2765253738"],"abstract_inverted_index":{"We":[0,104,137],"propose":[1],"a":[2,46,62,80,112],"novel":[3,160],"framework":[4,38,110,151],"that":[5],"automatically":[6],"learns":[7],"the":[8,54,69,94,97,100,106,143,156],"lighting":[9,55,125],"patterns":[10,56],"for":[11,154],"efficient":[12],"reflectance":[13,85],"acquisition,":[14,60],"as":[15,17,119,121],"well":[16],"how":[18],"to":[19,53,90,129],"faithfully":[20],"reconstruct":[21],"spatially":[22],"varying":[23],"anisotropic":[24],"BRDFs":[25],"and":[26,61,87,99,147,161],"local":[27],"frames":[28],"from":[29,72],"measurements":[30],"under":[31],"such":[32],"patterns.":[33],"The":[34,75],"core":[35],"of":[36,45,83,96,102,108,115,134],"our":[37,109,140],"is":[39,77,152],"an":[40],"asymmetric":[41],"deep":[42],"autoencoder,":[43],"consisting":[44],"nonnegative,":[47],"linear":[48],"encoder":[49],"which":[50,66,127],"directly":[51],"corresponds":[52],"used":[57],"in":[58,158],"physical":[59,116],"stacked,":[63],"nonlinear":[64],"decoder":[65],"computationally":[67],"recovers":[68],"BRDF":[70],"information":[71],"captured":[73,148],"photographs.":[74,149],"autoencoder":[76],"trained":[78],"with":[79,142],"large":[81],"amount":[82],"synthetic":[84],"data,":[86],"can":[88],"adapt":[89],"various":[91],"factors,":[92],"including":[93],"geometry":[95],"setup":[98],"properties":[101],"appearance.":[103],"demonstrate":[105],"effectiveness":[107],"on":[111],"wide":[113],"range":[114],"materials,":[117],"using":[118],"few":[120],"16":[122],"~":[123,131],"32":[124],"patterns,":[126],"correspond":[128],"12":[130],"25":[132],"seconds":[133],"acquisition":[135,163],"time.":[136],"also":[138],"validate":[139],"results":[141],"ground":[144],"truth":[145],"data":[146],"Our":[150],"useful":[153],"increasing":[155],"efficiency":[157],"both":[159],"existing":[162],"setups.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}