{"id":"https://openalex.org/W4416251616","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228004","title":"PUDet: Advancing 3D Object Detection with Generative Upsampling Networks","display_name":"PUDet: Advancing 3D Object Detection with Generative Upsampling Networks","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251616","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228004"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5029030838","display_name":"Limei Xu","orcid":"https://orcid.org/0000-0002-2557-0240"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Limei Xu","raw_affiliation_strings":["Beijing Institute of Technology,School of Integrated Circuits and Electronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Integrated Circuits and Electronics,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037682721","display_name":"Zhiguo Zhou","orcid":"https://orcid.org/0009-0005-6145-6268"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiguo Zhou","raw_affiliation_strings":["Beijing Institute of Technology,School of Integrated Circuits and Electronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Integrated Circuits and Electronics,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112529458","display_name":"Xuehua Zhou","orcid":"https://orcid.org/0000-0003-0933-3568"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuehua Zhou","raw_affiliation_strings":["Beijing Institute of Technology,School of Integrated Circuits and Electronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Technology,School of Integrated Circuits and Electronics,Beijing,China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029030838"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37489003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5205000042915344,"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":0.5205000042915344,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.2475000023841858,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.09260000288486481,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.8450000286102295},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7574999928474426},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6603000164031982},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6011000275611877},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5055000185966492},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4357999861240387},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.43220001459121704},{"id":"https://openalex.org/keywords/point-distribution-model","display_name":"Point distribution model","score":0.4189000129699707},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.39399999380111694}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8450000286102295},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7574999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.696399986743927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6805999875068665},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6603000164031982},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6388000249862671},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6011000275611877},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5055000185966492},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.43220001459121704},{"id":"https://openalex.org/C118317068","wikidata":"https://www.wikidata.org/wiki/Q2100760","display_name":"Point distribution model","level":2,"score":0.4189000129699707},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.38190001249313354},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.35740000009536743},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.34200000762939453},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.32820001244544983},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C38785706","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Interest point detection","level":5,"score":0.25529998540878296},{"id":"https://openalex.org/C181095308","wikidata":"https://www.wikidata.org/wiki/Q1541599","display_name":"Geometric primitive","level":2,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":55,"referenced_works":["https://openalex.org/W2150066425","https://openalex.org/W2555618208","https://openalex.org/W2798965597","https://openalex.org/W2897529137","https://openalex.org/W2949708697","https://openalex.org/W2951517617","https://openalex.org/W2963390820","https://openalex.org/W2963680153","https://openalex.org/W2963727135","https://openalex.org/W2964062501","https://openalex.org/W2968296999","https://openalex.org/W2988715931","https://openalex.org/W2997337685","https://openalex.org/W2997814983","https://openalex.org/W2998254148","https://openalex.org/W3034314779","https://openalex.org/W3035346742","https://openalex.org/W3035461736","https://openalex.org/W3035574168","https://openalex.org/W3108426750","https://openalex.org/W3113028524","https://openalex.org/W3118341329","https://openalex.org/W3130463448","https://openalex.org/W3166089996","https://openalex.org/W3167095230","https://openalex.org/W3170030651","https://openalex.org/W3175676582","https://openalex.org/W3184736166","https://openalex.org/W3202229469","https://openalex.org/W3203631022","https://openalex.org/W3204971388","https://openalex.org/W3205005447","https://openalex.org/W3209639308","https://openalex.org/W3210543564","https://openalex.org/W3217335336","https://openalex.org/W4200632008","https://openalex.org/W4214777292","https://openalex.org/W4226098442","https://openalex.org/W4250431399","https://openalex.org/W4256209052","https://openalex.org/W4310596280","https://openalex.org/W4312294656","https://openalex.org/W4312322669","https://openalex.org/W4319301075","https://openalex.org/W4382240183","https://openalex.org/W4386075590","https://openalex.org/W4386075817","https://openalex.org/W4386075854","https://openalex.org/W4386076242","https://openalex.org/W4386076253","https://openalex.org/W4386076603","https://openalex.org/W4386076702","https://openalex.org/W4386083121","https://openalex.org/W4390872929","https://openalex.org/W4402715759"],"related_works":[],"abstract_inverted_index":{"Lidar-based":[0],"3D":[1,46],"object":[2,117,156],"detection":[3,32],"achieves":[4],"superior":[5],"performance.":[6,33],"However,":[7],"the":[8,73,121,143,152,160,167,176,183],"unevenly":[9],"distributed":[10],"point":[11,58,95,112,157],"clouds":[12],"on":[13,155,166],"foreground":[14,70],"objects":[15,23],"can":[16],"weaken":[17],"their":[18],"geometric":[19,67,124],"representation.":[20],"Moreover,":[21],"far-away":[22],"typically":[24],"have":[25],"very":[26],"few":[27],"points,":[28],"which":[29,48,93,110],"further":[30],"impairs":[31],"In":[34],"this":[35],"paper,":[36],"we":[37,126],"present":[38],"a":[39,57],"novel":[40],"framework":[41,174],"PUDet":[42,80],"(Point":[43],"Cloud":[44],"Upsampling":[45],"Detector),":[47],"integrates":[49],"generative":[50],"models":[51],"into":[52],"discriminative":[53],"detectors.":[54],"We":[55,149],"leverage":[56],"cloud":[59],"upsampling":[60],"network":[61],"with":[62],"prior":[63],"knowledge":[64],"to":[65,114],"enhance":[66],"details":[68],"of":[69,123,145,185],"objects,":[71,92,109,141],"aiding":[72],"detector":[74],"in":[75],"achieving":[76],"more":[77],"accurate":[78],"prediction.":[79],"incorporates":[81],"two":[82],"key":[83],"modules:":[84],"LDEM":[85,146],"(Local":[86],"Distribution":[87],"Enhancement":[88],"Module)":[89,106],"for":[90,107,136],"nearby":[91,138],"optimizes":[94],"distribution":[96],"while":[97],"minimizing":[98],"computational":[99],"costs,":[100],"and":[101,133,139,147],"DDAM":[102],"(Distant":[103],"Density":[104],"Augmentation":[105],"distant":[108,140],"increases":[111],"density":[113],"better":[115],"delineate":[116],"contours.":[118],"To":[119],"validate":[120],"optimization":[122],"contours,":[125],"conducted":[127],"experiments":[128],"comparing":[129],"uniform":[130],"loss":[131],"before":[132],"after":[134],"enhancement":[135],"both":[137],"demonstrating":[142],"efficacy":[144],"DDAM.":[148],"also":[150],"display":[151],"attention":[153],"maps":[154],"clouds,":[158],"explaining":[159],"observed":[161],"accuracy":[162],"gains.":[163],"Experimental":[164],"results":[165],"KITTI":[168],"testing":[169],"set":[170],"show":[171],"that":[172],"our":[173],"improves":[175],"baseline":[177],"CT3D":[178],"by":[179],"1.84":[180],"mAP,":[181],"confirming":[182],"effectiveness":[184],"PUDet.":[186],"Code":[187],"will":[188],"be":[189],"available":[190],"at":[191],"https://github.com/bellamyhsu/PUDet/tree/main.":[192]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
