{"id":"https://openalex.org/W4415708498","doi":"https://doi.org/10.1109/icme59968.2025.11208964","title":"VoxelDet: Towards Accurate 3D Object Detection with Voxel Pruning and Fine Geometric Shape","display_name":"VoxelDet: Towards Accurate 3D Object Detection with Voxel Pruning and Fine Geometric Shape","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708498","doi":"https://doi.org/10.1109/icme59968.2025.11208964"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11208964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11208964","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5101615912","display_name":"Jia Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Wen","raw_affiliation_strings":["YanShan University,School of Information Science and Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"YanShan University,School of Information Science and Engineering,China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108050441","display_name":"Jialin Li","orcid":"https://orcid.org/0000-0003-3786-004X"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jialin Li","raw_affiliation_strings":["YanShan University,School of Information Science and Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"YanShan University,School of Information Science and Engineering,China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100458342","display_name":"Ting Zhang","orcid":"https://orcid.org/0000-0002-9569-9357"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ting Zhang","raw_affiliation_strings":["YanShan University,School of Information Science and Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"YanShan University,School of Information Science and Engineering,China","institution_ids":["https://openalex.org/I39333907"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39333907"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.8924000263214111,"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":0.8924000263214111,"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":0.025100000202655792,"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"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.02019999921321869,"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/pruning","display_name":"Pruning","score":0.6884999871253967},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6252999901771545},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.6089000105857849},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.5426999926567078},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5374000072479248},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5360000133514404},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5238000154495239},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5184000134468079},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5099999904632568}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7509999871253967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7425000071525574},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6884999871253967},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6252999901771545},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.6089000105857849},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5649999976158142},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.5426999926567078},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5374000072479248},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5360000133514404},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5238000154495239},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5184000134468079},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5099999904632568},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C56435381","wikidata":"https://www.wikidata.org/wiki/Q1196371","display_name":"Geometric transformation","level":3,"score":0.4514000117778778},{"id":"https://openalex.org/C43173174","wikidata":"https://www.wikidata.org/wiki/Q2646942","display_name":"Regular grid","level":3,"score":0.3402999937534332},{"id":"https://openalex.org/C7305733","wikidata":"https://www.wikidata.org/wiki/Q207961","display_name":"Geometric shape","level":2,"score":0.3125},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3077999949455261},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.3046000003814697},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.30329999327659607},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C32990609","wikidata":"https://www.wikidata.org/wiki/Q306542","display_name":"Transformation geometry","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.27129998803138733},{"id":"https://openalex.org/C104065381","wikidata":"https://www.wikidata.org/wiki/Q1002535","display_name":"Geometric modeling","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C136520226","wikidata":"https://www.wikidata.org/wiki/Q302814","display_name":"Geometric data analysis","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11208964","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11208964","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2897529137","https://openalex.org/W3034314779","https://openalex.org/W3118341329","https://openalex.org/W4214777292","https://openalex.org/W4226439325","https://openalex.org/W4312437143","https://openalex.org/W4312546175","https://openalex.org/W4320002812","https://openalex.org/W4382240183","https://openalex.org/W4386075636","https://openalex.org/W4386076066","https://openalex.org/W4386076253","https://openalex.org/W4390872929"],"related_works":[],"abstract_inverted_index":{"Recently,":[0],"the":[1,18,30,89,93,117],"voxel-based":[2],"two-stage":[3,26],"model":[4],"shows":[5],"impressive":[6],"performance":[7,132],"in":[8],"3D":[9,14],"object":[10,36],"detection.":[11],"However,":[12],"commonly-used":[13],"convolutional":[15,63],"backbone":[16],"encounter":[17],"challenge":[19],"of":[20],"limited":[21],"receptive":[22,74],"fields.":[23,75],"And":[24,76],"existing":[25],"models":[27],"that":[28,128],"generate":[29],"RoI":[31,94,112],"features":[32],"suffer":[33],"from":[34],"missing":[35,118],"geometric":[37,119],"shapes.":[38],"Motivated":[39],"by":[40],"these,":[41],"we":[42],"propose":[43],"a":[44,67,100],"high-performance":[45],"network":[46],"named":[47],"VoxelDet.":[48],"We":[49],"introduce":[50],"additional":[51],"down-sampling":[52],"convolutions":[53],"to":[54,87,115,134],"capture":[55],"large":[56],"context":[57],"information.":[58],"By":[59],"combining":[60],"these":[61],"added":[62,86],"features,":[64],"VoxelDet":[65,98,129],"achieves":[66,130],"better":[68],"trade-off":[69],"between":[70,111],"spatial":[71],"resolutions":[72],"and":[73],"an":[77],"auxiliary":[78],"strategy":[79],"with":[80],"Hierarchical":[81],"Voxel":[82],"Pruning":[83],"(HVP)":[84],"is":[85],"alleviate":[88],"computation":[90],"problem.":[91,121],"In":[92],"grid":[95,113],"pooling":[96],"step,":[97],"incorporates":[99],"fine":[101],"Grid":[102],"Geometric":[103],"Attention":[104],"(GGA)":[105],"module.":[106],"GGA":[107],"captures":[108],"long-range":[109],"dependencies":[110],"points":[114],"tackle":[116],"shapes":[120],"Extensive":[122],"experiments":[123],"on":[124],"KITTI":[125],"dataset":[126],"demonstrate":[127],"competitive":[131],"comparable":[133],"state-of-the-art":[135],"models.":[136]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-30T00:00:00"}