{"id":"https://openalex.org/W3211538029","doi":"https://doi.org/10.23919/icac50006.2021.9594153","title":"Edge-preserving Smoothing Regularization for Monocular Depth Estimation","display_name":"Edge-preserving Smoothing Regularization for Monocular Depth Estimation","publication_year":2021,"publication_date":"2021-09-02","ids":{"openalex":"https://openalex.org/W3211538029","doi":"https://doi.org/10.23919/icac50006.2021.9594153","mag":"3211538029"},"language":"en","primary_location":{"id":"doi:10.23919/icac50006.2021.9594153","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icac50006.2021.9594153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 26th International Conference on Automation and Computing (ICAC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.23919/ICAC50006.2021.9594153","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071110323","display_name":"Saqib Nazir","orcid":"https://orcid.org/0000-0003-0098-1868"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Saqib Nazir","raw_affiliation_strings":["Research Center for Spatial Information (CEOSpaceTech), University POLITEHNICA of Bucharest (UPB), Bucharest, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Spatial Information (CEOSpaceTech), University POLITEHNICA of Bucharest (UPB), Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019825441","display_name":"Daniela Col\u0163uc","orcid":"https://orcid.org/0000-0002-0237-7878"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Daniela Coltuc","raw_affiliation_strings":["Research Center for Spatial Information (CEOSpaceTech), University POLITEHNICA of Bucharest (UPB), Bucharest, Romania"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Center for Spatial Information (CEOSpaceTech), University POLITEHNICA of Bucharest (UPB), Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071110323"],"corresponding_institution_ids":["https://openalex.org/I61641377"],"apc_list":null,"apc_paid":null,"fwci":0.3879,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62062009,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9991000294685364,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9976999759674072,"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/smoothing","display_name":"Smoothing","score":0.7364470362663269},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.714167058467865},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7128863334655762},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6399674415588379},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6076385974884033},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.55450838804245},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4691397547721863},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4494768977165222},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3846662938594818}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.7364470362663269},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.714167058467865},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7128863334655762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6399674415588379},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6076385974884033},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.55450838804245},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4691397547721863},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4494768977165222},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3846662938594818}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/icac50006.2021.9594153","is_oa":false,"landing_page_url":"https://doi.org/10.23919/icac50006.2021.9594153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 26th International Conference on Automation and Computing (ICAC)","raw_type":"proceedings-article"},{"id":"pmh:oai:zenodo.org:6198816","is_oa":true,"landing_page_url":"https://doi.org/10.23919/ICAC50006.2021.9594153","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:6198816","is_oa":true,"landing_page_url":"https://doi.org/10.23919/ICAC50006.2021.9594153","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1905829557","https://openalex.org/W2124907686","https://openalex.org/W2153582625","https://openalex.org/W2520707372","https://openalex.org/W2561074213","https://openalex.org/W2961343177","https://openalex.org/W2962741876","https://openalex.org/W2963654727","https://openalex.org/W2963911235","https://openalex.org/W2985775862","https://openalex.org/W3011395398","https://openalex.org/W3017059628","https://openalex.org/W3018814261","https://openalex.org/W3034275131","https://openalex.org/W3034901835","https://openalex.org/W3034953156","https://openalex.org/W3035289617","https://openalex.org/W3035563424","https://openalex.org/W3089762353","https://openalex.org/W3101424466","https://openalex.org/W3189195585","https://openalex.org/W6605121731","https://openalex.org/W6678569853"],"related_works":["https://openalex.org/W2383807498","https://openalex.org/W1978572805","https://openalex.org/W1997992934","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W4213275102","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Monocular":[0],"depth":[1,40,48],"estimation":[2,41],"is":[3,89,145,186],"a":[4,43,51,62,78,92,124],"fundamental":[5],"challenge":[6],"since":[7],"the":[8,18,67,70,73,82,112,136,143,146,149,154,172,193],"foundation":[9],"of":[10,20,57,72,81,86,142,182,192],"computer":[11],"vision":[12],"with":[13,42,77],"many":[14,36],"real-world":[15],"applications.":[16],"Recently,":[17],"introduction":[19],"Deep":[21],"Convolutional":[22],"Neural":[23],"Networks":[24],"(CNN)":[25],"has":[26,65],"brought":[27],"significant":[28],"improvements":[29],"to":[30,90,108,121,189],"this":[31,87],"particular":[32],"problem.":[33],"There":[34],"are":[35],"solutions":[37],"for":[38,179],"scene":[39],"focus":[44],"on":[45,130,139,160,171],"obtaining":[46],"high-quality":[47],"maps":[49],"from":[50],"given":[52],"RGB":[53],"image.":[54],"The":[55,84,127],"insertion":[56],"prior":[58],"information":[59],"by":[60],"adding":[61],"smoothing":[63,71],"regularization":[64,96,113,137],"improved":[66],"results.":[68],"However,":[69],"surfaces":[74],"comes":[75],"together":[76],"certain":[79],"degradation":[80],"edges.":[83,194],"goal":[85],"paper":[88],"make":[91],"comparison":[93],"between":[94],"various":[95],"terms":[97],"used":[98,115,165],"either":[99],"in":[100,117,123],"supervised":[101,125],"or":[102],"self-supervised":[103,118,150],"learning":[104],"methods.":[105],"In":[106],"addition":[107],"this,":[109],"we":[110,164],"modified":[111,151],"term":[114],"currently":[116],"methods":[119],"such":[120],"work":[122],"manner.":[126],"experimental":[128],"results":[129],"NYU-Depth":[131],"v2":[132],"have":[133],"shown":[134],"that":[135,185],"based":[138,170],"L1":[140],"norm":[141],"gradient":[144],"best":[147],"and":[148,175],"one":[152],"outperforms":[153],"rest.":[155],"Finally,":[156],"rather":[157],"than":[158],"relying":[159],"common":[161],"evaluation":[162],"metrics,":[163],"an":[166],"additional":[167],"accuracy":[168,181],"measure":[169],"Steerable":[173],"Pyramid":[174],"Kullback-Leibler":[176],"divergence":[177],"(KLD)":[178],"edge":[180],"estimated":[183],"depths":[184],"more":[187],"sensitive":[188],"positional":[190],"errors":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}