{"id":"https://openalex.org/W2978788458","doi":"https://doi.org/10.1109/tip.2019.2944343","title":"Unsupervised Monocular Depth Estimation From Light Field Image","display_name":"Unsupervised Monocular Depth Estimation From Light Field Image","publication_year":2019,"publication_date":"2019-10-03","ids":{"openalex":"https://openalex.org/W2978788458","doi":"https://doi.org/10.1109/tip.2019.2944343","mag":"2978788458","pmid":"https://pubmed.ncbi.nlm.nih.gov/31603783"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2019.2944343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2944343","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5020050758","display_name":"Wenhui Zhou","orcid":"https://orcid.org/0000-0003-2216-8878"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenhui Zhou","raw_affiliation_strings":["School of Computer Science and Technology, Hangzhou Dianzi Uniersity, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hangzhou Dianzi Uniersity, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064059062","display_name":"Enci Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Enci Zhou","raw_affiliation_strings":["School of Computer Science and Technology, Hangzhou Dianzi Uniersity, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hangzhou Dianzi Uniersity, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067114207","display_name":"Gaomin Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaomin Liu","raw_affiliation_strings":["School of Computer Science and Technology, Hangzhou Dianzi Uniersity, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Hangzhou Dianzi Uniersity, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067452827","display_name":"Lili Lin","orcid":"https://orcid.org/0000-0001-8196-3244"},"institutions":[{"id":"https://openalex.org/I75059550","display_name":"Zhejiang Gongshang University","ror":"https://ror.org/0569mkk41","country_code":"CN","type":"education","lineage":["https://openalex.org/I75059550"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Lin","raw_affiliation_strings":["School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China","institution_ids":["https://openalex.org/I75059550"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074260102","display_name":"Andrew Lumsdaine","orcid":"https://orcid.org/0000-0002-9153-6622"},"institutions":[{"id":"https://openalex.org/I142606810","display_name":"Pacific Northwest National Laboratory","ror":"https://ror.org/05h992307","country_code":"US","type":"facility","lineage":["https://openalex.org/I1325736334","https://openalex.org/I1330989302","https://openalex.org/I142606810","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Lumsdaine","raw_affiliation_strings":["Pacific Northwest Laboratory, Richland, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pacific Northwest Laboratory, Richland, USA","institution_ids":["https://openalex.org/I142606810"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020050758"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":null,"apc_paid":null,"fwci":2.8577,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.92957192,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"29","issue":null,"first_page":"1606","last_page":"1617"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9940000176429749,"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/epipolar-geometry","display_name":"Epipolar geometry","score":0.8292836546897888},{"id":"https://openalex.org/keywords/light-field","display_name":"Light field","score":0.8111887574195862},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7311044931411743},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6980112791061401},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6794105768203735},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.600890040397644},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5838844776153564},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5792200565338135},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5155296325683594},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5026514530181885},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.414958119392395},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.365706205368042},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30509325861930847},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2748217284679413},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10651177167892456}],"concepts":[{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.8292836546897888},{"id":"https://openalex.org/C48983235","wikidata":"https://www.wikidata.org/wiki/Q593161","display_name":"Light field","level":2,"score":0.8111887574195862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7311044931411743},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6980112791061401},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6794105768203735},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.600890040397644},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5838844776153564},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5792200565338135},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5155296325683594},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5026514530181885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.414958119392395},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.365706205368042},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30509325861930847},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2748217284679413},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10651177167892456},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2019.2944343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2944343","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:31603783","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31603783","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6694301442","display_name":null,"funder_award_id":"LZ14F020003","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"}],"funders":[{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":75,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1522301498","https://openalex.org/W1585668122","https://openalex.org/W1803059841","https://openalex.org/W1914596733","https://openalex.org/W2007165614","https://openalex.org/W2039298799","https://openalex.org/W2043408784","https://openalex.org/W2079176966","https://openalex.org/W2119967924","https://openalex.org/W2123042594","https://openalex.org/W2128359381","https://openalex.org/W2133515844","https://openalex.org/W2133665775","https://openalex.org/W2137313943","https://openalex.org/W2149694199","https://openalex.org/W2155763342","https://openalex.org/W2171740948","https://openalex.org/W2194775991","https://openalex.org/W2200825767","https://openalex.org/W2211024058","https://openalex.org/W2259424905","https://openalex.org/W2289524987","https://openalex.org/W2296677960","https://openalex.org/W2337243791","https://openalex.org/W2341651707","https://openalex.org/W2342420672","https://openalex.org/W2345834824","https://openalex.org/W2440384215","https://openalex.org/W2464308330","https://openalex.org/W2471061117","https://openalex.org/W2520707372","https://openalex.org/W2548527721","https://openalex.org/W2549094858","https://openalex.org/W2551052086","https://openalex.org/W2591697814","https://openalex.org/W2593564043","https://openalex.org/W2737842117","https://openalex.org/W2741694910","https://openalex.org/W2751225115","https://openalex.org/W2752279250","https://openalex.org/W2753230437","https://openalex.org/W2766499215","https://openalex.org/W2776033207","https://openalex.org/W2778641202","https://openalex.org/W2779124836","https://openalex.org/W2779696137","https://openalex.org/W2783120174","https://openalex.org/W2783516823","https://openalex.org/W2790316230","https://openalex.org/W2798270832","https://openalex.org/W2798487245","https://openalex.org/W2897264375","https://openalex.org/W2902024778","https://openalex.org/W2902536616","https://openalex.org/W2951234442","https://openalex.org/W2962864875","https://openalex.org/W2963583471","https://openalex.org/W2963872754","https://openalex.org/W2963955021","https://openalex.org/W2964121744","https://openalex.org/W2964178790","https://openalex.org/W3100388886","https://openalex.org/W6631190155","https://openalex.org/W6634891648","https://openalex.org/W6660417602","https://openalex.org/W6678146716","https://openalex.org/W6680152570","https://openalex.org/W6685261749","https://openalex.org/W6704184719","https://openalex.org/W6729480787","https://openalex.org/W6734178593","https://openalex.org/W6734447043","https://openalex.org/W6745651466","https://openalex.org/W6748117778"],"related_works":["https://openalex.org/W132764016","https://openalex.org/W2148054235","https://openalex.org/W2132043085","https://openalex.org/W4251504644","https://openalex.org/W2369285629","https://openalex.org/W4385805064","https://openalex.org/W2929681741","https://openalex.org/W2594336302","https://openalex.org/W2787003719","https://openalex.org/W3209881651"],"abstract_inverted_index":{"Learning":[0],"based":[1],"depth":[2,29,35,66,71,87],"estimation":[3,67],"from":[4,72],"light":[5,38,59,77,93,119],"field":[6,39,60,78,120],"has":[7],"made":[8],"significant":[9],"progresses":[10],"in":[11,129,142],"recent":[12],"years.":[13],"However,":[14],"most":[15,143],"existing":[16],"approaches":[17],"are":[18,40],"under":[19],"the":[20,54,73,85,124,130,154,162],"supervised":[21],"framework,":[22],"which":[23],"requires":[24],"vast":[25],"quantities":[26],"of":[27,37,58,76,92,156,166],"ground-truth":[28,81],"data":[30],"for":[31,44],"training.":[32],"Furthermore,":[33],"accurate":[34],"maps":[36],"hardly":[41],"available":[42],"except":[43],"a":[45,116],"few":[46],"synthetic":[47,121],"datasets.":[48],"In":[49],"this":[50],"paper,":[51],"we":[52,95],"exploit":[53],"multi-orientation":[55],"epipolar":[56],"geometry":[57,90],"and":[61,89,107,164],"propose":[62],"an":[63],"unsupervised":[64,100,126,151],"monocular":[65],"network.":[68],"It":[69],"predicts":[70],"central":[74],"view":[75],"without":[79],"any":[80],"information.":[82],"Inspired":[83],"by":[84],"inherent":[86],"cues":[88],"constraints":[91],"field,":[94],"then":[96],"introduce":[97],"three":[98],"novel":[99],"loss":[101,106],"functions:":[102],"photometric":[103],"loss,":[104],"defocus":[105],"symmetry":[108],"loss.":[109],"We":[110,159],"have":[111],"evaluated":[112],"our":[113,136,157,167],"method":[114,127,137,168],"on":[115,169],"public":[117],"4D":[118,131],"dataset.":[122],"As":[123],"first":[125],"published":[128],"Light":[132],"Field":[133],"Benchmark":[134],"website,":[135],"can":[138],"achieve":[139],"satisfactory":[140],"performance":[141],"error":[144],"metrics.":[145],"Comparison":[146],"experiments":[147],"with":[148],"two":[149],"state-of-the-art":[150],"methods":[152],"demonstrate":[153],"superiority":[155],"method.":[158],"also":[160],"prove":[161],"effectiveness":[163],"generality":[165],"real-world":[170],"light-field":[171],"images.":[172]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":4}],"updated_date":"2026-05-26T13:28:51.108037","created_date":"2025-10-10T00:00:00"}