{"id":"https://openalex.org/W3085308862","doi":"https://doi.org/10.1109/icip40778.2020.9190809","title":"Boosting Image-Based Localization Via Randomly Geometric Data Augmentation","display_name":"Boosting Image-Based Localization Via Randomly Geometric Data Augmentation","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3085308862","doi":"https://doi.org/10.1109/icip40778.2020.9190809","mag":"3085308862"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9190809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5011765460","display_name":"Yiming Wan","orcid":"https://orcid.org/0000-0002-1315-9120"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Wan","raw_affiliation_strings":["NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010846765","display_name":"Wei Gao","orcid":"https://orcid.org/0000-0003-2257-5684"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Gao","raw_affiliation_strings":["NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101935099","display_name":"Sheng Han","orcid":"https://orcid.org/0000-0002-1212-2676"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Han","raw_affiliation_strings":["NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100645165","display_name":"Yihong Wu","orcid":"https://orcid.org/0000-0003-3340-2535"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihong Wu","raw_affiliation_strings":["NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.179,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.9188641,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"688","last_page":"692"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9997000098228455,"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/T10638","display_name":"Optical measurement and interference techniques","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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8594891428947449},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.6959748268127441},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6945315599441528},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6774646043777466},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6113835573196411},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6009110808372498},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.5608159899711609},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5429508686065674},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5012907981872559},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4606054425239563},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44955405592918396},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4472872316837311},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.438618928194046},{"id":"https://openalex.org/keywords/monocular-vision","display_name":"Monocular vision","score":0.4231816828250885},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3580605983734131},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.243047833442688}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8594891428947449},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.6959748268127441},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6945315599441528},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6774646043777466},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6113835573196411},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6009110808372498},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5608159899711609},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5429508686065674},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5012907981872559},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4606054425239563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44955405592918396},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4472872316837311},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.438618928194046},{"id":"https://openalex.org/C158829959","wikidata":"https://www.wikidata.org/wiki/Q1640606","display_name":"Monocular vision","level":2,"score":0.4231816828250885},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3580605983734131},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.243047833442688}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip40778.2020.9190809","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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":16,"referenced_works":["https://openalex.org/W1989476314","https://openalex.org/W2085261163","https://openalex.org/W2108598243","https://openalex.org/W2117228865","https://openalex.org/W2151103935","https://openalex.org/W2194775991","https://openalex.org/W2200124539","https://openalex.org/W2584731199","https://openalex.org/W2602709638","https://openalex.org/W2605111497","https://openalex.org/W2609883120","https://openalex.org/W2737630486","https://openalex.org/W2795645133","https://openalex.org/W2892865870","https://openalex.org/W2963024893","https://openalex.org/W2964175348"],"related_works":["https://openalex.org/W3213997683","https://openalex.org/W4307623796","https://openalex.org/W2995270189","https://openalex.org/W2084124712","https://openalex.org/W2435467664","https://openalex.org/W2091635186","https://openalex.org/W4381188157","https://openalex.org/W2011626633","https://openalex.org/W2037866696","https://openalex.org/W4251947321"],"abstract_inverted_index":{"Visual":[0],"localization":[1,53,135],"is":[2],"a":[3,83],"fundamental":[4],"problem":[5],"in":[6],"computer":[7],"vision":[8],"and":[9,51,98,113],"robotics.":[10],"Recently,":[11],"deep":[12,24],"learning":[13],"has":[14],"shown":[15],"to":[16,32,47,63,118],"be":[17],"effective":[18],"for":[19,87],"robust":[20],"monocular":[21],"localization.":[22],"Most":[23],"learning-based":[25],"methods":[26,41],"utilize":[27],"convolution":[28],"neural":[29],"network":[30,86],"(CNN)":[31],"regress":[33],"global":[34],"6":[35],"degree-of-freedom":[36],"(Dof)":[37],"pose.":[38],"However,":[39],"these":[40],"suffer":[42],"from":[43],"pose":[44,121],"sparsity,":[45],"leading":[46],"over-fitting":[48],"during":[49,73],"training":[50,90],"poor":[52],"performance":[54],"on":[55],"unseen":[56],"data.":[57],"In":[58],"this":[59,65],"paper,":[60],"we":[61,76,102],"try":[62],"alleviate":[64],"issue":[66],"by":[67],"implementing":[68],"randomly":[69,104],"geometric":[70,128],"augmentation":[71,129],"(RGA)":[72],"training.":[74],"Specifically,":[75],"firstly":[77],"estimate":[78],"the":[79,88,93,120,134],"depth":[80,84],"map":[81],"using":[82],"estimation":[85],"initial":[89,114],"image.":[91],"Combing":[92],"estimated":[94],"depth,":[95],"RGB":[96],"image":[97],"its":[99],"corresponding":[100],"pose,":[101],"can":[103,131],"synthesize":[105],"new":[106],"images":[107,115],"of":[108],"different":[109],"views.":[110],"The":[111],"synthesized":[112],"are":[116],"used":[117],"train":[119],"regression":[122],"network.":[123],"Experiment":[124],"results":[125],"show":[126],"our":[127],"strategy":[130],"significantly":[132],"improve":[133],"accuracy.":[136]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}