{"id":"https://openalex.org/W4312902387","doi":"https://doi.org/10.1109/lra.2022.3221871","title":"Self-Supervised Monocular Depth Estimation With Isometric-Self-Sample-Based Learning","display_name":"Self-Supervised Monocular Depth Estimation With Isometric-Self-Sample-Based Learning","publication_year":2022,"publication_date":"2022-11-14","ids":{"openalex":"https://openalex.org/W4312902387","doi":"https://doi.org/10.1109/lra.2022.3221871"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2022.3221871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2022.3221871","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"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 Robotics and Automation Letters","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/A5051789113","display_name":"Geonho Cha","orcid":"https://orcid.org/0000-0002-3008-4642"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Geonho Cha","raw_affiliation_strings":["Clova AI, NAVER Corp, Seongnam, Korea"],"raw_orcid":"https://orcid.org/0000-0002-3008-4642","affiliations":[{"raw_affiliation_string":"Clova AI, NAVER Corp, Seongnam, Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061520736","display_name":"Ho-Deok Jang","orcid":"https://orcid.org/0000-0001-6386-3813"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ho-Deok Jang","raw_affiliation_strings":["Clova AI, NAVER Corp, Seongnam, Korea"],"raw_orcid":"https://orcid.org/0000-0001-6386-3813","affiliations":[{"raw_affiliation_string":"Clova AI, NAVER Corp, Seongnam, Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028451951","display_name":"Dongyoon Wee","orcid":"https://orcid.org/0000-0003-0359-146X"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dongyoon Wee","raw_affiliation_strings":["Clova AI, NAVER Corp, Seongnam, Korea"],"raw_orcid":"https://orcid.org/0000-0003-0359-146X","affiliations":[{"raw_affiliation_string":"Clova AI, NAVER Corp, Seongnam, Korea","institution_ids":["https://openalex.org/I60922564"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60922564"],"apc_list":null,"apc_paid":null,"fwci":0.243,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.48073506,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"8","issue":"4","first_page":"2173","last_page":"2180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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":0.9998999834060669,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9957000017166138,"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":0.9955000281333923,"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":0.7608509659767151},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.6867326498031616},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6063910126686096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5918565988540649},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5478718280792236},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5430487990379333},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45443975925445557},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44841283559799194},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.4254641830921173},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37372180819511414}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7608509659767151},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.6867326498031616},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6063910126686096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5918565988540649},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5478718280792236},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5430487990379333},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45443975925445557},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44841283559799194},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.4254641830921173},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37372180819511414},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2022.3221871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2022.3221871","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766","2377-3774"],"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 Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1522301498","https://openalex.org/W2074254947","https://openalex.org/W2083047701","https://openalex.org/W2117539524","https://openalex.org/W2132947399","https://openalex.org/W2133665775","https://openalex.org/W2150066425","https://openalex.org/W2194775991","https://openalex.org/W2300779272","https://openalex.org/W2340897893","https://openalex.org/W2520707372","https://openalex.org/W2609883120","https://openalex.org/W2887848798","https://openalex.org/W2890949887","https://openalex.org/W2896047481","https://openalex.org/W2963150697","https://openalex.org/W2963412495","https://openalex.org/W2963591054","https://openalex.org/W2963654727","https://openalex.org/W2963906250","https://openalex.org/W2964968086","https://openalex.org/W2967550825","https://openalex.org/W2981732213","https://openalex.org/W2982102242","https://openalex.org/W2985775862","https://openalex.org/W2995687155","https://openalex.org/W3034403088","https://openalex.org/W3034604951","https://openalex.org/W3097932847","https://openalex.org/W3107156787","https://openalex.org/W3107389224","https://openalex.org/W3108829545","https://openalex.org/W3174211490","https://openalex.org/W3176210901","https://openalex.org/W3192489863","https://openalex.org/W4206545998","https://openalex.org/W4281563989","https://openalex.org/W4313022741","https://openalex.org/W4321512633","https://openalex.org/W6631190155","https://openalex.org/W6685261749","https://openalex.org/W6766978945","https://openalex.org/W6767088534","https://openalex.org/W6785345353"],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2788191623","https://openalex.org/W3151908889","https://openalex.org/W2946952476","https://openalex.org/W4200417262","https://openalex.org/W4308217387","https://openalex.org/W2904616728","https://openalex.org/W4224217118","https://openalex.org/W2974859913"],"abstract_inverted_index":{"Managing":[0],"the":[1,5,16,30,34,40,52,73,101,117,122,126,132,198,209],"dynamic":[2,31],"regions":[3,32],"in":[4,33,76],"photometric":[6,35],"loss":[7,36],"formulation":[8,37],"has":[9],"been":[10],"a":[11,77,141,201],"main":[12],"issue":[13,27],"for":[14,107,144],"handling":[15],"self-supervised":[17],"depth":[18,210],"estimation":[19],"problem.":[20],"Most":[21],"previous":[22],"methods":[23],"have":[24],"alleviated":[25],"this":[26,56,60],"by":[28,111,200],"removing":[29],"based":[38],"on":[39,116,166],"masks":[41],"estimated":[42,118],"from":[43],"another":[44],"module,":[45],"making":[46],"it":[47,206],"difficult":[48],"to":[49,58,70,182],"fully":[50,71],"utilize":[51,72],"training":[53,74,89,109,128,157],"images.":[54],"In":[55,204],"letter,":[57],"handle":[59],"problem,":[61],"we":[62],"propose":[63],"an":[64],"isometric":[65,102],"self-sample-based":[66],"learning":[67],"(ISSL)":[68],"method":[69,84,137],"images":[75,92],"simple":[78],"yet":[79],"effective":[80],"way.":[81],"The":[82],"proposed":[83],"provides":[85,153],"additional":[86,154,164],"supervision":[87,155],"during":[88,156,172],"using":[90],"self-generated":[91],"that":[93,187],"comply":[94],"with":[95,179],"pure":[96],"static":[97,133],"scene":[98,134,225],"assumption.":[99],"Specifically,":[100],"self-sample":[103],"generator":[104],"synthesizes":[105],"self-samples":[106,124],"each":[108],"image":[110,129],"applying":[112],"random":[113],"rigid":[114],"transformations":[115],"depth.":[119],"Thus":[120],"both":[121],"generated":[123],"and":[125,170,221,223],"corresponding":[127],"always":[130],"follow":[131],"relation.":[135],"Our":[136],"can":[138],"serve":[139],"as":[140],"plug-and-play":[142],"module":[143,191],"two":[145,193],"existing":[146,194],"models":[147,180,195],"without":[148],"any":[149],"architectural":[150],"modifications.":[151],"It":[152],"phase":[158],"only.":[159],"Thus,":[160],"there":[161],"is":[162],"no":[163],"overhead":[165],"base":[167],"model":[168],"parameters":[169],"computation":[171],"inference":[173],"phase.":[174],"These":[175],"properties":[176],"fit":[177],"well":[178],"oriented":[181],"real-time":[183],"applications.":[184],"We":[185],"show":[186],"plugging":[188],"our":[189],"ISSL":[190],"into":[192],"consistently":[196],"improves":[197],"performance":[199],"large":[202],"margin.":[203],"addition,":[205],"also":[207],"boosts":[208],"accuracy":[211],"over":[212],"different":[213],"types":[214],"of":[215],"scene,":[216],"i.e.,":[217],"outdoor":[218],"scenes":[219],"(KITTI":[220],"Make3D)":[222],"indoor":[224],"(NYUv2),":[226],"validating":[227],"its":[228],"high":[229],"effectiveness.":[230]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}