{"id":"https://openalex.org/W3117701455","doi":"https://doi.org/10.1109/itsc45102.2020.9294620","title":"A new Evaluation Approach for Deep Learning-based Monocular Depth Estimation Methods","display_name":"A new Evaluation Approach for Deep Learning-based Monocular Depth Estimation Methods","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3117701455","doi":"https://doi.org/10.1109/itsc45102.2020.9294620","mag":"3117701455"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"preprint","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/A5051543225","display_name":"Antoine Mauri","orcid":"https://orcid.org/0000-0002-0313-3859"},"institutions":[{"id":"https://openalex.org/I2802505300","display_name":"\u00c9cole Sup\u00e9rieure d'Ing\u00e9nieurs en G\u00e9nie \u00c9lectrique","ror":"https://ror.org/01apwkd48","country_code":"FR","type":"education","lineage":["https://openalex.org/I2802505300"]},{"id":"https://openalex.org/I62396329","display_name":"Universit\u00e9 de Rouen Normandie","ror":"https://ror.org/03nhjew95","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I62396329"]},{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Antoine Mauri","raw_affiliation_strings":["Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France","SEGULA Engineering, 19 rue d\u2019Arras, Nanterre, France","SEGULA Engineering, 19 rue d'Arras, Nanterre, France"],"affiliations":[{"raw_affiliation_string":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France","institution_ids":["https://openalex.org/I4210105918","https://openalex.org/I2802505300","https://openalex.org/I62396329"]},{"raw_affiliation_string":"SEGULA Engineering, 19 rue d\u2019Arras, Nanterre, France","institution_ids":[]},{"raw_affiliation_string":"SEGULA Engineering, 19 rue d'Arras, Nanterre, France","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006393121","display_name":"Redouane Khemmar","orcid":"https://orcid.org/0000-0002-6230-2966"},"institutions":[{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]},{"id":"https://openalex.org/I2802505300","display_name":"\u00c9cole Sup\u00e9rieure d'Ing\u00e9nieurs en G\u00e9nie \u00c9lectrique","ror":"https://ror.org/01apwkd48","country_code":"FR","type":"education","lineage":["https://openalex.org/I2802505300"]},{"id":"https://openalex.org/I62396329","display_name":"Universit\u00e9 de Rouen Normandie","ror":"https://ror.org/03nhjew95","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I62396329"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Redouane Khemmar","raw_affiliation_strings":["Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France"],"affiliations":[{"raw_affiliation_string":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France","institution_ids":["https://openalex.org/I4210105918","https://openalex.org/I2802505300","https://openalex.org/I62396329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013194782","display_name":"R\u00e9mi Boutteau","orcid":"https://orcid.org/0000-0003-1078-5043"},"institutions":[{"id":"https://openalex.org/I62396329","display_name":"Universit\u00e9 de Rouen Normandie","ror":"https://ror.org/03nhjew95","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I62396329"]},{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]},{"id":"https://openalex.org/I2802505300","display_name":"\u00c9cole Sup\u00e9rieure d'Ing\u00e9nieurs en G\u00e9nie \u00c9lectrique","ror":"https://ror.org/01apwkd48","country_code":"FR","type":"education","lineage":["https://openalex.org/I2802505300"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Remi Boutteau","raw_affiliation_strings":["Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France"],"affiliations":[{"raw_affiliation_string":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France","institution_ids":["https://openalex.org/I4210105918","https://openalex.org/I2802505300","https://openalex.org/I62396329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040745112","display_name":"Beno\u00eet Decoux","orcid":"https://orcid.org/0000-0003-4037-2880"},"institutions":[{"id":"https://openalex.org/I62396329","display_name":"Universit\u00e9 de Rouen Normandie","ror":"https://ror.org/03nhjew95","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I62396329"]},{"id":"https://openalex.org/I2802505300","display_name":"\u00c9cole Sup\u00e9rieure d'Ing\u00e9nieurs en G\u00e9nie \u00c9lectrique","ror":"https://ror.org/01apwkd48","country_code":"FR","type":"education","lineage":["https://openalex.org/I2802505300"]},{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Benoit Decoux","raw_affiliation_strings":["Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France"],"affiliations":[{"raw_affiliation_string":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France","institution_ids":["https://openalex.org/I4210105918","https://openalex.org/I2802505300","https://openalex.org/I62396329"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110801201","display_name":"Jean-Yves Ertaud","orcid":null},"institutions":[{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]},{"id":"https://openalex.org/I62396329","display_name":"Universit\u00e9 de Rouen Normandie","ror":"https://ror.org/03nhjew95","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I62396329"]},{"id":"https://openalex.org/I2802505300","display_name":"\u00c9cole Sup\u00e9rieure d'Ing\u00e9nieurs en G\u00e9nie \u00c9lectrique","ror":"https://ror.org/01apwkd48","country_code":"FR","type":"education","lineage":["https://openalex.org/I2802505300"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jean-Yves Ertaud","raw_affiliation_strings":["Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France"],"affiliations":[{"raw_affiliation_string":"Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, Rouen, France","institution_ids":["https://openalex.org/I4210105918","https://openalex.org/I2802505300","https://openalex.org/I62396329"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078486341","display_name":"Madjid Haddad","orcid":"https://orcid.org/0000-0002-8933-4714"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madjid Haddad","raw_affiliation_strings":["SEGULA Engineering, 19 rue d\u2019Arras, Nanterre, France","SEGULA Engineering, 19 rue d'Arras, Nanterre, France"],"affiliations":[{"raw_affiliation_string":"SEGULA Engineering, 19 rue d\u2019Arras, Nanterre, France","institution_ids":[]},{"raw_affiliation_string":"SEGULA Engineering, 19 rue d'Arras, Nanterre, France","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5051543225"],"corresponding_institution_ids":["https://openalex.org/I2802505300","https://openalex.org/I62396329","https://openalex.org/I4210105918"],"apc_list":null,"apc_paid":null,"fwci":0.20993464,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.51990192,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"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":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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9979000091552734,"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":0.996999979019165,"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":0.8106293082237244},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.7183135151863098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6923521757125854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6066807508468628},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5066057443618774},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4496062099933624},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.4433024525642395},{"id":"https://openalex.org/keywords/depth-perception","display_name":"Depth perception","score":0.4281822741031647},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.42569079995155334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34506499767303467},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.331417441368103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2782399654388428},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2050400674343109},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10415375232696533}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8106293082237244},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.7183135151863098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6923521757125854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6066807508468628},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5066057443618774},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4496062099933624},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.4433024525642395},{"id":"https://openalex.org/C52672216","wikidata":"https://www.wikidata.org/wiki/Q1749840","display_name":"Depth perception","level":3,"score":0.4281822741031647},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.42569079995155334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34506499767303467},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.331417441368103},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2782399654388428},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2050400674343109},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10415375232696533},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc45102.2020.9294620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1903029394","https://openalex.org/W1905829557","https://openalex.org/W2066142518","https://openalex.org/W2115579991","https://openalex.org/W2120556640","https://openalex.org/W2512305642","https://openalex.org/W2520707372","https://openalex.org/W2599765304","https://openalex.org/W2609883120","https://openalex.org/W2907670226","https://openalex.org/W2913133337","https://openalex.org/W2934279571","https://openalex.org/W2962816904","https://openalex.org/W2962960377","https://openalex.org/W2963150697","https://openalex.org/W2963264757","https://openalex.org/W2963412495","https://openalex.org/W2963446712","https://openalex.org/W2963881378","https://openalex.org/W2964020152","https://openalex.org/W2969566464","https://openalex.org/W2979594848","https://openalex.org/W2985775862","https://openalex.org/W3000518373","https://openalex.org/W3035574168","https://openalex.org/W4289082871","https://openalex.org/W6750474155","https://openalex.org/W6752497527","https://openalex.org/W6757246177","https://openalex.org/W6760782946","https://openalex.org/W6766261854"],"related_works":["https://openalex.org/W2074216034","https://openalex.org/W2073100926","https://openalex.org/W2949096641","https://openalex.org/W2970686063","https://openalex.org/W1976277412","https://openalex.org/W2785517206","https://openalex.org/W4213085014","https://openalex.org/W2066222094","https://openalex.org/W4320729701","https://openalex.org/W4225386930"],"abstract_inverted_index":{"In":[0,23,46],"smart":[1],"mobility":[2],"based":[3,31,58],"road":[4,138],"navigation,":[5],"object":[6,101],"detection,":[7],"depth":[8,32,55,98,102,128],"estimation":[9,33,56],"and":[10,68,85,104,116,139],"tracking":[11],"are":[12],"very":[13],"important":[14],"tasks":[15],"for":[16,35,127,131,136],"improvement":[17],"of":[18,29,53,96],"the":[19,24,65,69,94],"environment":[20],"perception":[21],"quality.":[22],"recent":[25],"years,":[26],"a":[27,86,123],"surge":[28],"deep-learning":[30],"methods":[34],"monocular":[36],"cameras":[37],"has":[38],"lead":[39],"to":[40,121],"significant":[41],"progress":[42],"in":[43,77,93,133],"this":[44,47,78],"field.":[45],"paper,":[48],"we":[49],"propose":[50],"an":[51,81],"evaluation":[52,99,103,126],"state-of-the-art":[54],"algorithms":[57],"on":[59,63,113],"single":[60,61],"input":[62],"both":[64,114,137],"KITTI":[66,115],"dataset":[67],"recently":[70],"published":[71],"NUScenes":[72,117],"dataset.":[73],"The":[74],"models":[75],"evaluated":[76],"paper":[79],"include":[80],"unsupervised":[82],"method":[83,88],"(Monodepth2)":[84],"supervised":[87],"(BTS).":[89],"Our":[90],"work":[91],"lies":[92],"elaboration":[95],"novel":[97],"protocols,":[100],"distance":[105],"ranges":[106],"evaluation.":[107],"We":[108],"validated":[109],"our":[110],"new":[111],"protocols":[112],"datasets,":[118],"allowing":[119],"us":[120],"get":[122],"more":[124],"comprehensive":[125],"estimation,":[129],"especially":[130],"applications":[132],"scene":[134],"understanding":[135],"rail":[140],"environment.":[141]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}