{"id":"https://openalex.org/W4408713927","doi":"https://doi.org/10.1109/itsc58415.2024.10919195","title":"CCDepth: A Lightweight Self-Supervised Depth Estimation Network with Enhanced Interpretability","display_name":"CCDepth: A Lightweight Self-Supervised Depth Estimation Network with Enhanced Interpretability","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4408713927","doi":"https://doi.org/10.1109/itsc58415.2024.10919195"},"language":"en","primary_location":{"id":"doi:10.1109/itsc58415.2024.10919195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10919195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-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/A5067234097","display_name":"Xi Zhang","orcid":"https://orcid.org/0000-0002-9247-0872"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xi Zhang","raw_affiliation_strings":["College of Information Science and Engineering, China University of Petroleum (Beijing),Beijing,China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, China University of Petroleum (Beijing),Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041778411","display_name":"Yaru Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaru Xue","raw_affiliation_strings":["College of Information Science and Engineering, China University of Petroleum (Beijing),Beijing,China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, China University of Petroleum (Beijing),Beijing,China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046933572","display_name":"Shaocheng Jia","orcid":"https://orcid.org/0000-0001-9586-5151"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Shaocheng Jia","raw_affiliation_strings":["The University of Hong Kong,Department of Civil Engineering,Hong Kong,China"],"affiliations":[{"raw_affiliation_string":"The University of Hong Kong,Department of Civil Engineering,Hong Kong,China","institution_ids":["https://openalex.org/I177725633","https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103152841","display_name":"Xin Pei","orcid":"https://orcid.org/0009-0006-2808-1799"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Pei","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology, Tsinghua University,Department of Automation,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology, Tsinghua University,Department of Automation,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067234097"],"corresponding_institution_ids":["https://openalex.org/I204553293"],"apc_list":null,"apc_paid":null,"fwci":0.7895,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.75216113,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"64","last_page":"69"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998000264167786,"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":0.9998000264167786,"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":0.998199999332428,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9736105799674988},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6820591688156128},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5359346270561218},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.533613920211792},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38276153802871704},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3343803584575653},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10152098536491394}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9736105799674988},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6820591688156128},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5359346270561218},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.533613920211792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38276153802871704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3343803584575653},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10152098536491394},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc58415.2024.10919195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10919195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1673923490","https://openalex.org/W1904727610","https://openalex.org/W1905829557","https://openalex.org/W2115579991","https://openalex.org/W2139905387","https://openalex.org/W2194775991","https://openalex.org/W2520707372","https://openalex.org/W2609883120","https://openalex.org/W2897802609","https://openalex.org/W2903233845","https://openalex.org/W2947583263","https://openalex.org/W2951234442","https://openalex.org/W2962258574","https://openalex.org/W2963351510","https://openalex.org/W2963412495","https://openalex.org/W2963583471","https://openalex.org/W2964052474","https://openalex.org/W2985775862","https://openalex.org/W3037743604","https://openalex.org/W3045086295","https://openalex.org/W3048524731","https://openalex.org/W3093198960","https://openalex.org/W3108179104","https://openalex.org/W3153847062","https://openalex.org/W4200495170","https://openalex.org/W4226455208","https://openalex.org/W4285803579","https://openalex.org/W4309630093","https://openalex.org/W4312619736","https://openalex.org/W4366765095","https://openalex.org/W4377286928","https://openalex.org/W4379469310","https://openalex.org/W4389459490","https://openalex.org/W4391768729","https://openalex.org/W4392528161","https://openalex.org/W6631190155","https://openalex.org/W6637162671","https://openalex.org/W6685261749","https://openalex.org/W6762970624","https://openalex.org/W6854203514"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W2033914206"],"abstract_inverted_index":{"Self-supervised":[0],"depth":[1,81],"estimation,":[2],"which":[3],"solely":[4],"requires":[5],"monocular":[6],"image":[7],"sequence":[8],"as":[9],"input,":[10],"has":[11,166],"become":[12],"increasingly":[13],"popular":[14],"and":[15,90,104,111,121,176],"promising":[16],"in":[17,63,114,138,183],"recent":[18],"years.":[19],"Current":[20],"research":[21],"primarily":[22],"focuses":[23],"on":[24,45,144,179],"enhancing":[25],"the":[26,30,33,39,43,49,65,91,105,127,131,145,150,163,180,184,189,192],"prediction":[27],"accuracy":[28],"of":[29,36,42,174,191],"models.":[31],"However,":[32],"excessive":[34],"number":[35],"parameters":[37],"impedes":[38],"universal":[40],"deployment":[41],"model":[44,123,164],"edge":[46],"devices.":[47],"Moreover,":[48],"emerging":[50],"neural":[51,87],"networks,":[52],"being":[53],"black-box":[54],"models,":[55],"are":[56],"difficult":[57],"to":[58,61,108],"analyze,":[59],"leading":[60],"challenges":[62],"understanding":[64],"rationales":[66],"for":[67],"performance":[68,156],"improvements.":[69],"To":[70],"mitigate":[71],"these":[72],"issues,":[73],"this":[74],"study":[75],"proposes":[76],"a":[77,134,172],"novel":[78,100],"hybrid":[79],"self-supervised":[80],"estimation":[82],"network,":[83],"CCDepth,":[84],"comprising":[85],"convolutional":[86],"networks":[88],"(CNNs)":[89],"white-box":[92],"CRATE":[93,106,128],"(Coding":[94],"RAte":[95],"reduction":[96],"TransformEr)":[97],"network.":[98],"This":[99],"network":[101,132,153,186],"uses":[102],"CNNs":[103],"modules":[107,129],"extract":[109],"local":[110],"global":[112,140],"information":[113],"images,":[115],"respectively,":[116],"thereby":[117],"boosting":[118],"learning":[119],"efficiency":[120],"reducing":[122],"size.":[124],"Furthermore,":[125],"incorporating":[126],"into":[130],"enables":[133],"mathematically":[135],"interpretable":[136],"process":[137],"capturing":[139],"features.":[141],"Extensive":[142],"experiments":[143],"KITTI":[146],"dataset":[147],"indicate":[148],"that":[149],"proposed":[151,193],"CCDepth":[152,185],"can":[154],"achieve":[155],"comparable":[157],"with":[158],"those":[159],"state-of-the-art":[160],"methods,":[161],"while":[162],"size":[165],"been":[167],"significantly":[168],"reduced.":[169],"In":[170],"addition,":[171],"series":[173],"quantitative":[175],"qualitative":[177],"analyses":[178],"inner":[181],"features":[182],"further":[187],"confirm":[188],"effectiveness":[190],"method.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}