{"id":"https://openalex.org/W4417130737","doi":"https://doi.org/10.1109/lsp.2025.3641506","title":"LVMF3D: Large Vision Model Boosting Multimodal Fusion for Indoor 3D Object Detection","display_name":"LVMF3D: Large Vision Model Boosting Multimodal Fusion for Indoor 3D Object Detection","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W4417130737","doi":"https://doi.org/10.1109/lsp.2025.3641506"},"language":null,"primary_location":{"id":"doi:10.1109/lsp.2025.3641506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3641506","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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":"Yichen Shi","orcid":"https://orcid.org/0009-0002-3208-1960"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yichen Shi","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0002-3208-1960","affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenming Yang","orcid":"https://orcid.org/0000-0002-2506-1286"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenming Yang","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-2506-1286","affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101812290","display_name":"Nan Su","orcid":"https://orcid.org/0009-0008-5211-9472"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Su","raw_affiliation_strings":["Department of Electronics Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-5211-9472","affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045183950","display_name":"Guijin Wang","orcid":"https://orcid.org/0000-0002-2131-3044"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guijin Wang","raw_affiliation_strings":["Department of Electronics Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2131-3044","affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3595692,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"356","last_page":"360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9404000043869019,"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":0.9404000043869019,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.009999999776482582,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.004100000020116568,"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/object-detection","display_name":"Object detection","score":0.8068000078201294},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6823999881744385},{"id":"https://openalex.org/keywords/viola\u2013jones-object-detection-framework","display_name":"Viola\u2013Jones object detection framework","score":0.6176000237464905},{"id":"https://openalex.org/keywords/object-class-detection","display_name":"Object-class detection","score":0.5985999703407288},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5512999892234802},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5407999753952026},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4970000088214874},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4878999888896942},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4607999920845032}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8282999992370605},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.8068000078201294},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7767999768257141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7527999877929688},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6823999881744385},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.6176000237464905},{"id":"https://openalex.org/C71681937","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object-class detection","level":5,"score":0.5985999703407288},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5512999892234802},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5407999753952026},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4970000088214874},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4878999888896942},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4607999920845032},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.45980000495910645},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4334000051021576},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4066999852657318},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40470001101493835},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.28940001130104065},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.2833999991416931},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2603999972343445},{"id":"https://openalex.org/C123134398","wikidata":"https://www.wikidata.org/wiki/Q2493819","display_name":"Haar-like features","level":5,"score":0.2590999901294708},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3641506","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3641506","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1923184257","https://openalex.org/W2988715931","https://openalex.org/W3034428269","https://openalex.org/W3034429258","https://openalex.org/W3096387236","https://openalex.org/W3096754345","https://openalex.org/W3215207332","https://openalex.org/W4200629389","https://openalex.org/W4214526701","https://openalex.org/W4214624153","https://openalex.org/W4312349930","https://openalex.org/W4312709167","https://openalex.org/W4313142416","https://openalex.org/W4372283849","https://openalex.org/W4386076222","https://openalex.org/W4386232615","https://openalex.org/W4386596856","https://openalex.org/W4388983859","https://openalex.org/W4393147877","https://openalex.org/W4400858699","https://openalex.org/W4402262711","https://openalex.org/W4402727494","https://openalex.org/W4402830327","https://openalex.org/W4403780761","https://openalex.org/W4404612908","https://openalex.org/W4409365848"],"related_works":[],"abstract_inverted_index":{"3D":[0,47,100,121,134],"object":[1,48,122,135],"detection":[2,24,49,123,136],"plays":[3],"an":[4],"important":[5],"role":[6],"in":[7,96],"intelligent":[8],"systems":[9],"perceiving":[10],"the":[11,23,30,61,67,78,97,112,119],"world.":[12],"Although":[13],"many":[14],"studies":[15],"have":[16],"been":[17],"conducted":[18],"to":[19,64,76,103,132],"address":[20],"this":[21],"task,":[22],"accuracy":[25],"is":[26,58,74],"still":[27],"limited":[28],"by":[29],"network's":[31],"learning":[32],"capability.":[33],"Therefore,":[34],"we":[35],"propose":[36],"LVMF3D,":[37],"a":[38],"Large":[39],"Vision":[40],"Model":[41],"(LVM)":[42],"boosted":[43],"multimodal":[44],"fusion":[45,110],"indoor":[46,120],"framework,":[50],"consisting":[51],"of":[52],"two":[53,113],"branches.":[54,114],"The":[55,71],"pre-trained":[56],"LVM":[57],"used":[59,75],"as":[60],"RGB":[62],"branch":[63,73],"better":[65],"extract":[66],"image":[68],"texture":[69],"feature.":[70,81],"point":[72],"encode":[77],"spatial":[79],"geometric":[80],"Furthermore,":[82],"Point":[83],"Fusion":[84,90],"Module":[85,91],"(PFM)":[86],"and":[87,99,107,127],"Multi-Scale":[88],"Attention":[89],"(MS-AFM)":[92],"are":[93],"specially":[94],"designed":[95],"2D":[98],"spaces,":[101],"respectively,":[102],"realize":[104],"more":[105],"comprehensive":[106],"effective":[108],"information":[109],"between":[111],"We":[115],"conduct":[116],"experiments":[117],"on":[118],"dataset":[124],"SUN":[125],"RGB-D":[126],"achieve":[128],"state-of-the-art":[129],"results":[130],"compared":[131],"other":[133],"methods.":[137]},"counts_by_year":[],"updated_date":"2026-01-02T23:11:23.791532","created_date":"2025-12-08T00:00:00"}