{"id":"https://openalex.org/W4312121297","doi":"https://doi.org/10.1109/cisp-bmei56279.2022.9979987","title":"A Comparative Study on RepVGG and ResNet for Monocular 3D Object Detection","display_name":"A Comparative Study on RepVGG and ResNet for Monocular 3D Object Detection","publication_year":2022,"publication_date":"2022-11-05","ids":{"openalex":"https://openalex.org/W4312121297","doi":"https://doi.org/10.1109/cisp-bmei56279.2022.9979987"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei56279.2022.9979987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei56279.2022.9979987","pdf_url":null,"source":{"id":"https://openalex.org/S4363605502","display_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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":null,"display_name":"Peng Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Yan","raw_affiliation_strings":["College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications,Nanjing,China","College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101950655","display_name":"Wenze Shao","orcid":"https://orcid.org/0000-0001-6869-7789"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenze Shao","raw_affiliation_strings":["College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications,Nanjing,China","College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]},{"raw_affiliation_string":"College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I41198531"],"apc_list":null,"apc_paid":null,"fwci":0.2387,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.5917122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"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/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.986299991607666,"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/residual-neural-network","display_name":"Residual neural network","score":0.8403666019439697},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.7487725019454956},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.708592414855957},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.702122688293457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.668505847454071},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6160945892333984},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4943270683288574},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47711876034736633},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4402086138725281},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39975056052207947},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32946085929870605},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0759861171245575}],"concepts":[{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.8403666019439697},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.7487725019454956},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.708592414855957},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.702122688293457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.668505847454071},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6160945892333984},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4943270683288574},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47711876034736633},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4402086138725281},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39975056052207947},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32946085929870605},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0759861171245575}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei56279.2022.9979987","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei56279.2022.9979987","pdf_url":null,"source":{"id":"https://openalex.org/S4363605502","display_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2150066425","https://openalex.org/W2194775991","https://openalex.org/W2560544142","https://openalex.org/W2565639579","https://openalex.org/W2605189827","https://openalex.org/W2953106684","https://openalex.org/W2954174912","https://openalex.org/W2963037989","https://openalex.org/W2963323244","https://openalex.org/W2963351448","https://openalex.org/W2963794551","https://openalex.org/W2981857055","https://openalex.org/W2982770724","https://openalex.org/W2998633559","https://openalex.org/W2999947750","https://openalex.org/W3034407526","https://openalex.org/W3034479628","https://openalex.org/W3035180028","https://openalex.org/W3035254347","https://openalex.org/W3035749845","https://openalex.org/W3043237494","https://openalex.org/W3096245940","https://openalex.org/W3106834807","https://openalex.org/W3110007378","https://openalex.org/W3114509423","https://openalex.org/W3129282545","https://openalex.org/W3167976421","https://openalex.org/W4288320659","https://openalex.org/W6620707391","https://openalex.org/W6628973269","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6760424586","https://openalex.org/W6764290155","https://openalex.org/W6781679151"],"related_works":["https://openalex.org/W2599472179","https://openalex.org/W4312121297","https://openalex.org/W4310471641","https://openalex.org/W2894651257","https://openalex.org/W3200590620","https://openalex.org/W4200172193","https://openalex.org/W4303926741","https://openalex.org/W2963002925","https://openalex.org/W4313560195","https://openalex.org/W4387829094"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"a":[3,28,37,51],"comparative":[4],"analysis":[5],"is":[6,34],"made":[7],"on":[8,50,81,108],"two":[9],"cutting-edge":[10],"deep":[11],"learning":[12],"backbones,":[13],"i.e.,":[14],"ResNet":[15,69],"and":[16,70,77],"RepVGG,":[17,26],"in":[18,72],"the":[19,60,65,82,90,97],"scenario":[20],"of":[21,32,68,74],"monocular":[22],"3D":[23,54,84],"object":[24,55],"detection.":[25],"as":[27,59],"more":[29,109],"recent":[30,53],"variant":[31],"VGG,":[33],"claimed":[35],"enjoy":[36],"favorable":[38],"accuracy-speed":[39],"balance":[40],"compared":[41],"to":[42,63],"other":[43],"state-of-the-art":[44],"ones,":[45],"especially":[46],"ResNet.":[47,99],"We":[48],"build":[49],"very":[52],"detection":[56,75,85],"model":[57],"KM3D":[58],"test":[61],"bed,":[62],"investigate":[64],"performance":[66],"differences":[67],"RepVGG":[71,92],"terms":[73],"speed":[76],"accuracy.":[78],"Experiment":[79],"results":[80],"popular":[83],"dataset":[86],"KITTI":[87],"demonstrate":[88],"that":[89],"backbone":[91,98],"has":[93],"not":[94],"convincingly":[95],"surpassed":[96],"Undoubtedly,":[100],"such":[101],"an":[102],"empirical":[103],"finding":[104],"inspires":[105],"one":[106],"research":[107],"robust":[110],"candidate":[111],"backbones":[112],"than":[113],"RepVGG.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
