{"id":"https://openalex.org/W3210197076","doi":"https://doi.org/10.1109/iv48863.2021.9576034","title":"LiDAR Data Noise Models and Methodology for Sim-to-Real Domain Generalization and Adaptation in Autonomous Driving Perception","display_name":"LiDAR Data Noise Models and Methodology for Sim-to-Real Domain Generalization and Adaptation in Autonomous Driving Perception","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3210197076","doi":"https://doi.org/10.1109/iv48863.2021.9576034","mag":"3210197076"},"language":"en","primary_location":{"id":"doi:10.1109/iv48863.2021.9576034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv48863.2021.9576034","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Intelligent Vehicles Symposium (IV)","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/A5090299828","display_name":"Jo\u00e3o Espadinha","orcid":null},"institutions":[{"id":"https://openalex.org/I4387152517","display_name":"Instituto Superior T\u00e9cnico","ror":"https://ror.org/03db2by73","country_code":null,"type":"education","lineage":["https://openalex.org/I141596103","https://openalex.org/I4387152517"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Joao Espadinha","raw_affiliation_strings":["Instituto Superior T\u00e9cnico, Lisbon, Portugal"],"affiliations":[{"raw_affiliation_string":"Instituto Superior T\u00e9cnico, Lisbon, Portugal","institution_ids":["https://openalex.org/I4387152517"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002364851","display_name":"Ivan Lebedev","orcid":"https://orcid.org/0009-0007-5306-3004"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ivan Lebedev","raw_affiliation_strings":["Hyundai Mobis Technical Center Europe (MTCE), Frankfurt, Germany"],"affiliations":[{"raw_affiliation_string":"Hyundai Mobis Technical Center Europe (MTCE), Frankfurt, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110811791","display_name":"Luka Lukic","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luka Lukic","raw_affiliation_strings":["Hyundai Mobis Technical Center Europe (MTCE), Frankfurt, Germany"],"affiliations":[{"raw_affiliation_string":"Hyundai Mobis Technical Center Europe (MTCE), Frankfurt, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028959217","display_name":"Alexandre Bernardino","orcid":"https://orcid.org/0000-0003-3991-1269"},"institutions":[{"id":"https://openalex.org/I4387152517","display_name":"Instituto Superior T\u00e9cnico","ror":"https://ror.org/03db2by73","country_code":null,"type":"education","lineage":["https://openalex.org/I141596103","https://openalex.org/I4387152517"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Alexandre Bernardino","raw_affiliation_strings":["Instituto Superior Tecnico, Lisbon, Portugal"],"affiliations":[{"raw_affiliation_string":"Instituto Superior Tecnico, Lisbon, Portugal","institution_ids":["https://openalex.org/I4387152517"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5090299828"],"corresponding_institution_ids":["https://openalex.org/I4387152517"],"apc_list":null,"apc_paid":null,"fwci":0.2882,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.56686275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"797","last_page":"803"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8071796894073486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.67568039894104},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6136655211448669},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5813834071159363},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5641315579414368},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5003352165222168},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.48468998074531555},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4822752773761749},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.46829620003700256},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4638500213623047},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4631679952144623},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.46028274297714233},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.4231828451156616},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4135550856590271},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4131878614425659},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37997967004776},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08009132742881775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8071796894073486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.67568039894104},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6136655211448669},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5813834071159363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5641315579414368},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5003352165222168},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.48468998074531555},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4822752773761749},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.46829620003700256},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4638500213623047},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4631679952144623},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.46028274297714233},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.4231828451156616},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4135550856590271},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4131878614425659},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37997967004776},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08009132742881775},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv48863.2021.9576034","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv48863.2021.9576034","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2115579991","https://openalex.org/W2150066425","https://openalex.org/W2605102758","https://openalex.org/W2895642264","https://openalex.org/W2936459520","https://openalex.org/W2962867954","https://openalex.org/W2964047820","https://openalex.org/W2968296999","https://openalex.org/W2970452316","https://openalex.org/W2991216808","https://openalex.org/W3003225285","https://openalex.org/W3003437478","https://openalex.org/W4295719664","https://openalex.org/W6745935785"],"related_works":["https://openalex.org/W3088831177","https://openalex.org/W2772397313","https://openalex.org/W2004370856","https://openalex.org/W2739874619","https://openalex.org/W4312857205","https://openalex.org/W2613186388","https://openalex.org/W2187221949","https://openalex.org/W4238992361","https://openalex.org/W4312559648","https://openalex.org/W2039154422"],"abstract_inverted_index":{"In":[0,51],"autonomous":[1,18,78],"driving,":[2],"object":[3,62],"detection":[4,63,119],"and":[5,14,48,64,81,90,100,113,120,171],"semantic":[6,65],"segmentation":[7,121],"are":[8,22],"critical":[9],"tasks":[10],"for":[11,40,61],"path":[12],"planning":[13],"control":[15],"of":[16,77,128,142,168,175],"an":[17],"vehicle.":[19],"Recent":[20],"approaches":[21],"based":[23],"on":[24,132],"supervised":[25,41],"learning":[26,42],"methods,":[27],"with":[28,137],"large":[29],"datasets":[30],"sampled":[31],"in":[32,109,117],"the":[33,69,86,98,101,118,125,143,173,176],"target":[34],"domain.":[35],"However,":[36],"annotating":[37],"training":[38],"data":[39,60,76,92,111,134,146,156],"methods":[43],"is":[44],"a":[45,140,159,165],"high":[46],"resource":[47],"time-consuming":[49],"task.":[50],"this":[52],"work,":[53],"we":[54],"propose":[55,82],"to":[56,73,84,157,163],"exploit":[57],"artificial":[58,75,89,133,155],"LiDAR":[59],"segmentation.":[66],"We":[67,95,123,149],"use":[68,164],"CARLA":[70],"simulator":[71],"[1]":[72],"generate":[74],"driving":[79],"scenarios":[80],"ways":[83],"mitigate":[85],"differences":[87],"between":[88],"real-world":[91,110,145],"(domain":[93,147],"generalization).":[94],"modeled":[96],"both":[97],"noise":[99],"missed":[102],"reflections":[103],"(denoted":[104],"point":[105],"dropout)":[106],"that":[107],"occur":[108],"collection,":[112],"show":[114],"their":[115],"effects":[116],"tasks.":[122],"assess":[124],"potential":[126],"benefits":[127],"using":[129,154],"pre-trained":[130],"models":[131],"when":[135,153],"fine-tuning":[136],"all,":[138],"or":[139],"fraction,":[141],"available":[144],"adaptation).":[148],"find":[150],"clear":[151],"improvements":[152],"pretrain":[158],"network,":[160],"which":[161],"allows":[162],"reduced":[166],"amount":[167],"realworld":[169],"data,":[170],"boost":[172],"performance":[174],"trained":[177],"models.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
