{"id":"https://openalex.org/W2970461331","doi":"https://doi.org/10.1109/ivs.2019.8813892","title":"Urban Localization with Street Views using a Convolutional Neural Network for End-to-End Camera Pose Regression","display_name":"Urban Localization with Street Views using a Convolutional Neural Network for End-to-End Camera Pose Regression","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2970461331","doi":"https://doi.org/10.1109/ivs.2019.8813892","mag":"2970461331"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2019.8813892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8813892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","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/A5066535821","display_name":"Guillaume Bresson","orcid":"https://orcid.org/0000-0003-4915-5065"},"institutions":[{"id":"https://openalex.org/I4210108314","display_name":"VeDeCoM Institute","ror":"https://ror.org/01ssrp339","country_code":"FR","type":"facility","lineage":["https://openalex.org/I4210108314"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Guillaume Bresson","raw_affiliation_strings":["Institut VEDECOM, Versailles, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institut VEDECOM, Versailles, France","institution_ids":["https://openalex.org/I4210108314"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102960613","display_name":"Yu Li","orcid":"https://orcid.org/0000-0001-6921-0776"},"institutions":[{"id":"https://openalex.org/I190752583","display_name":"ParisTech","ror":"https://ror.org/05c2qg481","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583"]},{"id":"https://openalex.org/I2746051580","display_name":"Universit\u00e9 Paris Sciences et Lettres","ror":"https://ror.org/013cjyk83","country_code":"FR","type":"education","lineage":["https://openalex.org/I2746051580"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Li Yu","raw_affiliation_strings":["MINES ParisTech, PSL Research University, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MINES ParisTech, PSL Research University, Paris, France","institution_ids":["https://openalex.org/I190752583","https://openalex.org/I2746051580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045448923","display_name":"Cyril Joly","orcid":"https://orcid.org/0000-0002-2899-0179"},"institutions":[{"id":"https://openalex.org/I190752583","display_name":"ParisTech","ror":"https://ror.org/05c2qg481","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583"]},{"id":"https://openalex.org/I2746051580","display_name":"Universit\u00e9 Paris Sciences et Lettres","ror":"https://ror.org/013cjyk83","country_code":"FR","type":"education","lineage":["https://openalex.org/I2746051580"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Cyril Joly","raw_affiliation_strings":["MINES ParisTech, PSL Research University, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MINES ParisTech, PSL Research University, Paris, France","institution_ids":["https://openalex.org/I190752583","https://openalex.org/I2746051580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008228948","display_name":"Fabien Moutarde","orcid":"https://orcid.org/0000-0003-4799-7285"},"institutions":[{"id":"https://openalex.org/I190752583","display_name":"ParisTech","ror":"https://ror.org/05c2qg481","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583"]},{"id":"https://openalex.org/I2746051580","display_name":"Universit\u00e9 Paris Sciences et Lettres","ror":"https://ror.org/013cjyk83","country_code":"FR","type":"education","lineage":["https://openalex.org/I2746051580"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Fabien Moutarde","raw_affiliation_strings":["MINES ParisTech, PSL Research University, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MINES ParisTech, PSL Research University, Paris, France","institution_ids":["https://openalex.org/I190752583","https://openalex.org/I2746051580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.8765,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.97424609,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1199","last_page":"1204"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9988999962806702,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9980999827384949,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.828148603439331},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8251675367355347},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7793937921524048},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6684657335281372},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.5632278323173523},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5390090346336365},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5357789993286133},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5351729393005371},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4649694263935089},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.4518895745277405},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.4129071831703186},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3617324233055115}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.828148603439331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8251675367355347},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7793937921524048},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6684657335281372},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.5632278323173523},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5390090346336365},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5357789993286133},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5351729393005371},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4649694263935089},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.4518895745277405},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.4129071831703186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3617324233055115},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2019.8813892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8813892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8500000238418579,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1512698229","https://openalex.org/W1522301498","https://openalex.org/W1556546299","https://openalex.org/W1563625711","https://openalex.org/W1883248133","https://openalex.org/W1978338723","https://openalex.org/W2014001040","https://openalex.org/W2021063678","https://openalex.org/W2031597059","https://openalex.org/W2043578134","https://openalex.org/W2043732461","https://openalex.org/W2054969198","https://openalex.org/W2058220625","https://openalex.org/W2076482495","https://openalex.org/W2089618650","https://openalex.org/W2100408571","https://openalex.org/W2134446283","https://openalex.org/W2194775991","https://openalex.org/W2200124539","https://openalex.org/W2461937780","https://openalex.org/W2749379418","https://openalex.org/W2750632489","https://openalex.org/W2963456126","https://openalex.org/W2963946945","https://openalex.org/W2964121744","https://openalex.org/W3124420883","https://openalex.org/W6631190155","https://openalex.org/W6750106230"],"related_works":["https://openalex.org/W2151749779","https://openalex.org/W3179968364","https://openalex.org/W1999612375","https://openalex.org/W2123263858","https://openalex.org/W2938107654","https://openalex.org/W3127959533","https://openalex.org/W4387967917","https://openalex.org/W200819717","https://openalex.org/W4307623796","https://openalex.org/W4394784820"],"abstract_inverted_index":{"This":[0],"paper":[1,139],"presents":[2],"an":[3,31,81,105],"end-to-end":[4],"real-time":[5],"monocular":[6],"absolute":[7],"localization":[8,118],"approach":[9,112],"that":[10,46,72,130],"uses":[11,114],"Google":[12],"Street":[13],"View":[14],"panoramas":[15],"as":[16,135],"a":[17,24,39,77,86,123,152],"prior":[18],"source":[19],"of":[20,33,42,61,68,80,104,144,175,197],"information":[21],"to":[22,38,84,151,160,165],"train":[23],"Convolutional":[25],"Neural":[26],"Network":[27],"(CNN).":[28],"We":[29,44],"propose":[30],"adaptation":[32],"the":[34,50,59,62,89,102,108,111,132,142,145,173,176,179,186,201],"PoseNet":[35],"architecture":[36],"[8]":[37],"sparse":[40],"database":[41],"panoramas.":[43],"show":[45,129],"we":[47],"can":[48],"expand":[49],"latter":[51],"by":[52],"synthesizing":[53],"new":[54],"images":[55],"and":[56,93,98,119],"consequently":[57],"improve":[58],"accuracy":[60,143],"pose":[63,187],"regressor.":[64],"The":[65,126,147],"main":[66],"advantage":[67],"our":[69,182],"method":[70,184],"is":[71],"it":[73],"does":[74,99],"not":[75,100],"require":[76,101],"first":[78],"passage":[79],"equipped":[82],"vehicle":[83],"build":[85],"map.":[87],"Moreover,":[88],"offline":[90],"data":[91],"generation":[92],"CNN":[94],"training":[95,133],"are":[96,155],"automatic":[97],"input":[103],"operator.":[106],"In":[107],"online":[109],"phase,":[110],"only":[113],"one":[115],"camera":[116,177],"for":[117],"regresses":[120],"poses":[121],"in":[122,137],"global":[124],"frame.":[125],"conducted":[127],"experiments":[128],"augmenting":[131],"set":[134],"presented":[136],"this":[138],"drastically":[140],"improves":[141],"CNN.":[146],"results,":[148],"when":[149],"compared":[150],"handcrafted-feature-based":[153],"approach,":[154],"less":[156,170],"accurate":[157],"(around":[158],"7.5":[159],"8":[161],"m":[162],"against":[163],"2.5":[164],"3":[166,198],"m)":[167],"but":[168],"also":[169],"dependent":[171],"on":[172],"position":[174],"inside":[178],"vehicle.":[180],"Furthermore,":[181],"CNN-based":[183],"computes":[185],"approximately":[188],"40":[189],"times":[190],"faster":[191],"(75":[192],"ms":[193],"per":[194],"image":[195],"instead":[196],"s)":[199],"than":[200],"handcrafted":[202],"approach.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
