{"id":"https://openalex.org/W2805635917","doi":"https://doi.org/10.48550/arxiv.1802.10328","title":"Neural Inverse Rendering for General Reflectance Photometric Stereo","display_name":"Neural Inverse Rendering for General Reflectance Photometric Stereo","publication_year":2018,"publication_date":"2018-02-28","ids":{"openalex":"https://openalex.org/W2805635917","doi":"https://doi.org/10.48550/arxiv.1802.10328","mag":"2805635917"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1802.10328","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.10328","pdf_url":"https://arxiv.org/pdf/1802.10328","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1802.10328","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037684597","display_name":"Tatsunori Taniai","orcid":"https://orcid.org/0000-0003-3361-4861","observed_orcids":["https://orcid.org/0000-0003-3361-4861"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taniai, Tatsunori","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009867352","display_name":"Takanori Maehara","orcid":"https://orcid.org/0000-0002-2101-1484","observed_orcids":["https://orcid.org/0000-0002-2101-1484"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maehara, Takanori","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":true,"cited_by_count":81,"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":0.5512999892234802,"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":0.5512999892234802,"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":0.2345999926328659,"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":0.17499999701976776,"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/photometric-stereo","display_name":"photometric stereo","score":0.9509999752044678},{"id":"https://openalex.org/keywords/reflectance-modeling","display_name":"reflectance modeling","score":0.5170000195503235},{"id":"https://openalex.org/keywords/brdf-estimation","display_name":"BRDF estimation","score":0.8050000071525574},{"id":"https://openalex.org/keywords/normal-estimation","display_name":"surface normal estimation","score":0.765999972820282},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.7680000066757202},{"id":"https://openalex.org/keywords/rendering-equation","display_name":"rendering equation","score":0.8569999933242798},{"id":"https://openalex.org/keywords/permutation-invariance","display_name":"permutation invariance","score":0.7300000190734863},{"id":"https://openalex.org/keywords/3d-shape-reconstruction","display_name":"3D shape reconstruction","score":0.6669999957084656},{"id":"https://openalex.org/keywords/image-generation","display_name":"image generation","score":0.35600000619888306}],"concepts":[{"id":"https://openalex.org/C44365914","wikidata":"https://www.wikidata.org/wiki/Q17120636","display_name":"Photometric stereo","level":3,"score":0.7739623785018921},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.74790358543396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7478606104850769},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.724261999130249},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.59637850522995},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5563755631446838},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.54219651222229},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5346314907073975},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5089287757873535},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5066350102424622},{"id":"https://openalex.org/C108597893","wikidata":"https://www.wikidata.org/wiki/Q663650","display_name":"Reflectivity","level":2,"score":0.4824962615966797},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4300435781478882},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.41773927211761475},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27070873975753784},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14359372854232788},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0746341347694397},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1802.10328","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.10328","pdf_url":"https://arxiv.org/pdf/1802.10328","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2952425628","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1802.10328","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1802.10328","doi":"https://doi.org/10.48550/arxiv.1802.10328","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1802.10328","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1802.10328","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1802.10328","pdf_url":"https://arxiv.org/pdf/1802.10328","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2805635917.pdf","grobid_xml":"https://content.openalex.org/works/W2805635917.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W182144941","https://openalex.org/W1487520337","https://openalex.org/W1520242730","https://openalex.org/W1539790409","https://openalex.org/W1564531641","https://openalex.org/W1600439205","https://openalex.org/W1677182931","https://openalex.org/W1975089519","https://openalex.org/W1975931458","https://openalex.org/W1978356057","https://openalex.org/W2028401795","https://openalex.org/W2085608034","https://openalex.org/W2097839898","https://openalex.org/W2104136923","https://openalex.org/W2121649761","https://openalex.org/W2123460334","https://openalex.org/W2129404737","https://openalex.org/W2134019950","https://openalex.org/W2138334472","https://openalex.org/W2149015394","https://openalex.org/W2158373453","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2461158874","https://openalex.org/W2519267527","https://openalex.org/W2560609797","https://openalex.org/W2604231069","https://openalex.org/W2622263826","https://openalex.org/W2624699774","https://openalex.org/W2728277730","https://openalex.org/W2766067961","https://openalex.org/W2769856846","https://openalex.org/W2787460109","https://openalex.org/W2949117887","https://openalex.org/W2963150697","https://openalex.org/W2963371269","https://openalex.org/W2963959597","https://openalex.org/W2964013315","https://openalex.org/W2964121744","https://openalex.org/W3100388886","https://openalex.org/W3151597212"],"related_works":["https://openalex.org/W2788719962","https://openalex.org/W3112404496","https://openalex.org/W4391129939","https://openalex.org/W2766067961","https://openalex.org/W4392529959","https://openalex.org/W3040624984","https://openalex.org/W3134419943","https://openalex.org/W4405981620","https://openalex.org/W2889383273","https://openalex.org/W3141317008"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,13,97,165],"novel":[3],"convolutional":[4],"neural":[5,51],"network":[6,112,124],"architecture":[7],"for":[8,41,76],"photometric":[9],"stereo":[10],"(Woodham,":[11],"1980),":[12],"problem":[14,36],"of":[15,86],"recovering":[16],"3D":[17],"object":[18],"surface":[19,104],"normals":[20,105,148],"from":[21],"multiple":[22],"images":[23],"observed":[24,121,135],"under":[25],"varying":[26],"illuminations.":[27],"Despite":[28],"its":[29],"long":[30],"history":[31],"in":[32,62,71,80],"computer":[33],"vision,":[34],"the":[35,111,116,161],"still":[37],"shows":[38],"fundamental":[39],"challenges":[40],"surfaces":[42],"with":[43],"unknown":[44],"general":[45],"reflectance":[46,56],"properties":[47],"(BRDFs).":[48],"Leveraging":[49],"deep":[50],"networks":[52,82],"to":[53,69,84,91,119,159],"learn":[54],"complicated":[55],"models":[57],"is":[58,157],"promising,":[59],"but":[60],"studies":[61],"this":[63],"direction":[64],"are":[65,108,126],"very":[66],"limited":[67],"due":[68],"difficulties":[70],"acquiring":[72],"accurate":[73],"ground":[74,146],"truth":[75,147],"training":[77],"and":[78,106,113,136],"also":[79],"designing":[81],"invariant":[83],"permutation":[85],"input":[87],"images.":[88,122,138,154],"In":[89],"order":[90],"address":[92],"these":[93],"challenges,":[94],"we":[95],"propose":[96],"physics":[98],"based":[99],"unsupervised":[100],"learning":[101,141],"framework":[102],"where":[103],"BRDFs":[107],"predicted":[109],"by":[110,130],"fed":[114],"into":[115],"rendering":[117],"equation":[118],"synthesize":[120],"The":[123],"weights":[125],"optimized":[127],"during":[128],"testing":[129],"minimizing":[131],"reconstruction":[132],"loss":[133],"between":[134],"synthesized":[137],"Thus,":[139],"our":[140],"process":[142],"does":[143],"not":[144],"require":[145],"or":[149],"even":[150],"pre-training":[151],"on":[152,164],"external":[153],"Our":[155],"method":[156],"shown":[158],"achieve":[160],"state-of-the-art":[162],"performance":[163],"challenging":[166],"real-world":[167],"scene":[168],"benchmark.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":3}],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2018-06-13T00:00:00"}
