{"id":"https://openalex.org/W4372346414","doi":"https://doi.org/10.1109/icassp49357.2023.10097144","title":"Deep Triple-Supervision Learning Unannotated Surgical Endoscopic Video Data for Monocular Dense Depth Estimation","display_name":"Deep Triple-Supervision Learning Unannotated Surgical Endoscopic Video Data for Monocular Dense Depth Estimation","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372346414","doi":"https://doi.org/10.1109/icassp49357.2023.10097144"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10097144","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10097144","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5044224149","display_name":"Wenkang Fan","orcid":"https://orcid.org/0000-0002-8364-5159"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenkang Fan","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110919489","display_name":"Kaiyun Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiyun Zhang","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018058464","display_name":"Hong Shi","orcid":"https://orcid.org/0000-0002-0182-8729"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Shi","raw_affiliation_strings":["Xiamen University,Department of Computer Science,Xiamen,China,361005"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,Department of Computer Science,Xiamen,China,361005","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100385444","display_name":"Jianhua Chen","orcid":"https://orcid.org/0000-0002-4068-3956"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Chen","raw_affiliation_strings":["Xiamen University,Department of Computer Science,Xiamen,China,361005"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,Department of Computer Science,Xiamen,China,361005","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082244615","display_name":"Yinran Chen","orcid":"https://orcid.org/0000-0001-8062-1859"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinran Chen","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100426934","display_name":"Xi\u00f3ngbi\u0101o Lu\u00f3","orcid":"https://orcid.org/0000-0002-5238-489X"},"institutions":[{"id":"https://openalex.org/I129708740","display_name":"Fujian Medical University","ror":"https://ror.org/050s6ns64","country_code":"CN","type":"education","lineage":["https://openalex.org/I129708740"]},{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]},{"id":"https://openalex.org/I4210148548","display_name":"Fujian Provincial Cancer Hospital","ror":"https://ror.org/058ms9w43","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210148548"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiongbiao Luo","raw_affiliation_strings":["Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102","Department of Computer Science, Xiamen University, Xiamen, China","Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University,National Institute for Data Science in Health and Medicine,Xiamen,China,361102","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Department of Computer Science, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, China","institution_ids":["https://openalex.org/I129708740","https://openalex.org/I4210148548"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3641,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64163696,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9995999932289124,"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":0.9995999932289124,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.992900013923645,"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/computer-science","display_name":"Computer science","score":0.7164338827133179},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6648572087287903},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6447429656982422},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6380130052566528},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5345671772956848},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.45255908370018005},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44126856327056885},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3933340609073639},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3449481725692749}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7164338827133179},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6648572087287903},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6447429656982422},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6380130052566528},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5345671772956848},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.45255908370018005},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44126856327056885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3933340609073639},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3449481725692749}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10097144","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10097144","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null},{"id":"https://openalex.org/F4320322170","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1612997784","https://openalex.org/W2064076387","https://openalex.org/W2152671441","https://openalex.org/W2187968763","https://openalex.org/W2471962767","https://openalex.org/W2474281075","https://openalex.org/W2559597482","https://openalex.org/W2609883120","https://openalex.org/W2798548676","https://openalex.org/W2951524694","https://openalex.org/W2963583471","https://openalex.org/W2963906250","https://openalex.org/W2985775862","https://openalex.org/W2989184872","https://openalex.org/W3090011935","https://openalex.org/W3096678291","https://openalex.org/W3107156787","https://openalex.org/W3152803807","https://openalex.org/W3161896016","https://openalex.org/W6618372016","https://openalex.org/W6760958310"],"related_works":["https://openalex.org/W200819717","https://openalex.org/W2032269556","https://openalex.org/W2944448661","https://openalex.org/W2064421702","https://openalex.org/W1991834176","https://openalex.org/W4253756925","https://openalex.org/W2131956013","https://openalex.org/W2109107145","https://openalex.org/W2805523177","https://openalex.org/W4381188157"],"abstract_inverted_index":{"Surface":[0],"reconstruction":[1],"is":[2],"an":[3,69],"essential":[4],"way":[5],"to":[6,58,73,81,95],"expand":[7],"surgical":[8],"field":[9],"of":[10,22],"view":[11],"during":[12],"endoscopic":[13,23,55,61],"surgery,":[14],"but":[15],"it":[16],"certainly":[17],"requires":[18],"dense":[19,29,62,97],"depth":[20,30,63,79,98],"estimation":[21],"video":[24,56],"sequences.":[25],"Unfortunately,":[26],"such":[27],"a":[28,47,83],"recovery":[31],"suffers":[32],"from":[33],"illumination":[34],"variation,":[35],"weak":[36],"texture,":[37],"and":[38,77,99,123,129],"occlusion.":[39],"To":[40],"address":[41],"these":[42],"problems,":[43],"this":[44],"work":[45],"proposes":[46],"new":[48],"triple-supervision":[49,117],"self-learning":[50],"strategy":[51,66,89],"that":[52,115],"uses":[53],"unannotated":[54],"data":[57,85],"predict":[59],"monocular":[60],"information.":[64,101],"This":[65],"first":[67],"employs":[68],"effective":[70,122],"conventional":[71],"method":[72,105],"estimate":[74],"camera":[75],"poses":[76],"sparse":[78,84],"maps":[80],"establishing":[82],"self-supervision.":[86],"Furthermore,":[87],"our":[88,104,116],"still":[90],"combines":[91],"two":[92],"consistency":[93],"measures":[94],"supervise":[96],"photometric":[100],"We":[102],"evaluated":[103],"on":[106],"collected":[107],"colonoscopic":[108],"videos,":[109],"with":[110],"the":[111],"experimental":[112],"results":[113],"showing":[114],"learning":[118,131],"framework":[119],"works":[120],"more":[121],"accurate":[124],"than":[125],"some":[126],"current":[127],"self-supervised":[128],"unsupervised":[130],"methods.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}