{"id":"https://openalex.org/W3031015638","doi":"https://doi.org/10.1109/icip40778.2020.9190808","title":"False Positive Removal for 3D Vehicle Detection With Penetrated Point Classifier","display_name":"False Positive Removal for 3D Vehicle Detection With Penetrated Point Classifier","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3031015638","doi":"https://doi.org/10.1109/icip40778.2020.9190808","mag":"3031015638"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9190808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190808","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":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2005.13153","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040180422","display_name":"Sungmin Woo","orcid":"https://orcid.org/0000-0002-1378-6722"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungmin Woo","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088185086","display_name":"Sangwon Hwang","orcid":"https://orcid.org/0000-0001-5549-5846"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sangwon Hwang","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386547","display_name":"Woojin Kim","orcid":"https://orcid.org/0000-0001-5520-4228"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Woojin Kim","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063366355","display_name":"Junhyeop Lee","orcid":"https://orcid.org/0000-0001-8325-4097"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junhyeop Lee","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078241289","display_name":"Dogyoon Lee","orcid":"https://orcid.org/0000-0002-0361-4417"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dogyoon Lee","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015739530","display_name":"Sangyoun Lee","orcid":"https://orcid.org/0000-0003-0394-6777"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sangyoun Lee","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]},{"raw_affiliation_string":"Yonsei University, School of Electrical and Electronic Engineering, Seoul, Korea#TAB#","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04656628,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"2721","last_page":"2725"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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":0.9994000196456909,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9891999959945679,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9793999791145325,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/point-cloud","display_name":"Point cloud","score":0.7822259664535522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6721860766410828},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6317420601844788},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5768406391143799},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5723920464515686},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5623738765716553},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5215492248535156},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5058717131614685},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4684465229511261},{"id":"https://openalex.org/keywords/percentage-point","display_name":"Percentage point","score":0.45140090584754944},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4470521807670593},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4127582907676697},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3321758508682251},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2640671133995056},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1439497470855713},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13142558932304382},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.12544956803321838},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08070763945579529},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07822978496551514}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7822259664535522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6721860766410828},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6317420601844788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5768406391143799},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5723920464515686},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5623738765716553},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5215492248535156},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5058717131614685},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4684465229511261},{"id":"https://openalex.org/C44648626","wikidata":"https://www.wikidata.org/wiki/Q1049848","display_name":"Percentage point","level":2,"score":0.45140090584754944},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4470521807670593},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4127582907676697},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3321758508682251},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2640671133995056},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1439497470855713},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13142558932304382},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.12544956803321838},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08070763945579529},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07822978496551514},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","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},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/icip40778.2020.9190808","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190808","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"},{"id":"pmh:oai:arXiv.org:2005.13153","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.13153","pdf_url":"https://arxiv.org/pdf/2005.13153","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3031015638","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2005.13153","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2005.13153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2005.13153","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"},{"id":"doi:10.17023/g26w-zd12","is_oa":true,"landing_page_url":"https://doi.org/10.17023/g26w-zd12","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2005.13153","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.13153","pdf_url":"https://arxiv.org/pdf/2005.13153","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322030","display_name":"Ministry of Science, ICT and Future Planning","ror":"https://ror.org/032e49973"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3031015638.pdf","grobid_xml":"https://content.openalex.org/works/W3031015638.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W2150066425","https://openalex.org/W2229637417","https://openalex.org/W2468368736","https://openalex.org/W2476752140","https://openalex.org/W2555618208","https://openalex.org/W2560544142","https://openalex.org/W2560609797","https://openalex.org/W2798965597","https://openalex.org/W2894705404","https://openalex.org/W2897529137","https://openalex.org/W2949708697","https://openalex.org/W2952394248","https://openalex.org/W2954174912","https://openalex.org/W2962807143","https://openalex.org/W2963087201","https://openalex.org/W2963121255","https://openalex.org/W2963400571","https://openalex.org/W2963727135","https://openalex.org/W2971011093","https://openalex.org/W2974437408","https://openalex.org/W2999556989","https://openalex.org/W3034407526","https://openalex.org/W3117804044","https://openalex.org/W6739778489","https://openalex.org/W6763422710","https://openalex.org/W6764514022"],"related_works":["https://openalex.org/W2155413166","https://openalex.org/W2911652069","https://openalex.org/W2405227744","https://openalex.org/W2979815249","https://openalex.org/W2897847603","https://openalex.org/W2251216540","https://openalex.org/W3046847942","https://openalex.org/W2033724900","https://openalex.org/W3169854625","https://openalex.org/W2398260609","https://openalex.org/W1890300368","https://openalex.org/W3209161601","https://openalex.org/W2964351809","https://openalex.org/W2353924061","https://openalex.org/W2182746667","https://openalex.org/W3158141244","https://openalex.org/W3038151511","https://openalex.org/W2804957041","https://openalex.org/W1576973951","https://openalex.org/W3138360141"],"abstract_inverted_index":{"Recently,":[0],"researchers":[1],"have":[2],"been":[3],"leveraging":[4],"LiDAR":[5,71],"point":[6,83],"cloud":[7],"for":[8],"higher":[9],"accuracy":[10],"in":[11],"3D":[12],"vehicle":[13,87],"detection.":[14],"Most":[15],"state-of-the-art":[16,121],"methods":[17],"are":[18,23,49],"deep":[19],"learning":[20],"based,":[21],"but":[22],"easily":[24],"affected":[25],"by":[26,137],"the":[27,33,57,67,86,89,95,120,130,146],"number":[28],"of":[29,70,88,116,119,151],"points":[30,73,140,144],"generated":[31,76],"on":[32,66,109,145],"object.":[34],"This":[35],"vulnerability":[36],"leads":[37],"to":[38],"numerous":[39],"false":[40,100],"positive":[41],"boxes":[42],"at":[43,129],"high":[44],"recall":[45,132],"positions,":[46],"where":[47],"objects":[48],"occasionally":[50],"predicted":[51,90],"with":[52],"few":[53],"points.":[54],"To":[55],"address":[56],"issue,":[58],"we":[59],"introduce":[60],"Penetrated":[61],"Point":[62],"Classifier":[63],"(PPC)":[64],"based":[65],"underlying":[68],"property":[69],"that":[72,127],"cannot":[74],"be":[75],"behind":[77,85],"vehicles.":[78],"It":[79],"determines":[80],"whether":[81],"a":[82],"exists":[84],"box,":[91],"and":[92,112,141,148],"if":[93],"does,":[94],"box":[96],"is":[97,107,134],"distinguished":[98],"as":[99],"positive.":[101],"Our":[102],"straightforward":[103],"yet":[104],"unprecedented":[105],"approach":[106],"evaluated":[108],"KITTI":[110],"dataset":[111],"achieved":[113],"performance":[114],"improvement":[115],"PointRCNN,":[117],"one":[118],"methods.":[122],"The":[123],"experiment":[124],"results":[125],"show":[126],"precision":[128],"highest":[131],"position":[133],"dramatically":[135],"increased":[136],"15.46":[138],"percentage":[139,143],"14.63":[142],"moderate":[147],"hard":[149],"difficulty":[150],"car":[152],"class,":[153],"respectively.":[154]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
