{"id":"https://openalex.org/W4416749643","doi":"https://doi.org/10.1109/iros60139.2025.11246096","title":"Anyview: General Indoor 3D Object Detection with Variable Frames","display_name":"Anyview: General Indoor 3D Object Detection with Variable Frames","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749643","doi":"https://doi.org/10.1109/iros60139.2025.11246096"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5064729482","display_name":"Zhenyu Wu","orcid":"https://orcid.org/0000-0002-7183-6943"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenyu Wu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Intelligent Engineering and Automation"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Intelligent Engineering and Automation","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070271175","display_name":"Xiuwei Xu","orcid":"https://orcid.org/0009-0003-2871-5008"},"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":"Xiuwei Xu","raw_affiliation_strings":["Tsinghua University,Department of Automation"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100389366","display_name":"Ziwei Wang","orcid":"https://orcid.org/0000-0001-9225-8495"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ziwei Wang","raw_affiliation_strings":["Nanyang Technological University,School of Electrical and Electronic Engineering"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University,School of Electrical and Electronic Engineering","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068686492","display_name":"Chong Xia","orcid":null},"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":"Chong Xia","raw_affiliation_strings":["Tsinghua University,Department of Automation"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077094563","display_name":"Linqing Zhao","orcid":"https://orcid.org/0000-0001-9635-0715"},"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":"Linqing Zhao","raw_affiliation_strings":["Tsinghua University,Department of Automation"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100460385","display_name":"Jiwen Lu","orcid":"https://orcid.org/0000-0002-6121-5529"},"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":"Jiwen Lu","raw_affiliation_strings":["Tsinghua University,Department of Automation"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103689774","display_name":"Haibin Yan","orcid":"https://orcid.org/0009-0008-2979-9895"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibin Yan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Intelligent Engineering and Automation"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Intelligent Engineering and Automation","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5064729482"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37022781,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"13333","last_page":"13340"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8967000246047974,"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":0.8967000246047974,"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":0.03819999843835831,"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":0.009600000455975533,"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":0.6216999888420105},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.599399983882904},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5252000093460083},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5130000114440918},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.47189998626708984},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4415999948978424},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4415000081062317},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4406000077724457},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.3806000053882599}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7371000051498413},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7134000062942505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6992999911308289},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6216999888420105},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5252000093460083},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4415999948978424},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C204641915","wikidata":"https://www.wikidata.org/wiki/Q7315509","display_name":"Residual frame","level":4,"score":0.3569999933242798},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34450000524520874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246096","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W116751493","https://openalex.org/W1923184257","https://openalex.org/W2594519801","https://openalex.org/W2799123546","https://openalex.org/W2904332125","https://openalex.org/W2962888833","https://openalex.org/W2963827913","https://openalex.org/W2988715931","https://openalex.org/W3034429258","https://openalex.org/W3096609285","https://openalex.org/W3171032126","https://openalex.org/W4214624153","https://openalex.org/W4319878982","https://openalex.org/W4321482282","https://openalex.org/W4403922463","https://openalex.org/W4404769919","https://openalex.org/W4407900973","https://openalex.org/W4409368280","https://openalex.org/W4413147465"],"related_works":[],"abstract_inverted_index":{"In":[0,102],"this":[1],"paper,":[2],"we":[3,114,143,172],"propose":[4,144],"a":[5,33,116,137,145,166,175,221,227],"novel":[6,117],"network":[7],"framework":[8,120],"for":[9,32,111,123,187],"indoor":[10],"3D":[11,66,106,118],"object":[12,107],"detection":[13,108,119,218],"to":[14,64,104,148,179],"handle":[15,89],"variable":[16,73,99],"input":[17,30,63,92,134,156],"frame":[18,74,91,100,159],"numbers":[19,75,132],"in":[20,51],"practical":[21,52,112,125],"scenarios.":[22],"Existing":[23],"methods":[24,109],"only":[25,88],"consider":[26],"fixed":[27,90],"frames":[28,135],"of":[29,77,133,154,184,230],"data":[31,93],"single":[34,138],"detector,":[35],"such":[36,55],"as":[37,56],"monocular":[38],"RGB-D":[39,48,70,157],"images":[40,71],"or":[41],"point":[42,81],"clouds":[43],"reconstructed":[44,79],"from":[45],"dense":[46],"multi-view":[47],"images.":[49],"While":[50],"application":[53],"scenes":[54],"robot":[57],"navigation":[58],"and":[59,94,160,196,216,223],"manipulation,":[60],"the":[61,65,69,78,84,150,182,199,206,233],"raw":[62],"detectors":[67],"is":[68],"with":[72,98,136,220,232],"instead":[76],"scene":[80],"cloud.":[82],"However,":[83],"previous":[85],"approaches":[86],"can":[87],"have":[95],"poor":[96],"performance":[97],"input.":[101],"order":[103],"facilitate":[105],"suitable":[110],"tasks,":[113],"present":[115],"named":[121],"AnyView":[122],"our":[124,210],"applications,":[126],"which":[127,190],"generalizes":[128],"well":[129],"across":[130],"different":[131],"model.":[139],"To":[140],"be":[141],"specific,":[142],"geometric":[146,152],"learner":[147],"mine":[149],"local":[151],"features":[153,186],"each":[155,188],"image":[158],"implement":[161],"local-global":[162],"feature":[163,194],"interaction":[164],"through":[165],"designed":[167],"spatial":[168],"mixture":[169],"module.":[170],"Meanwhile,":[171],"further":[173,197],"utilize":[174],"dynamic":[176],"token":[177],"strategy":[178],"adaptively":[180],"adjust":[181],"number":[183],"extracted":[185],"frame,":[189],"ensures":[191],"consistent":[192],"global":[193],"density":[195],"enhances":[198],"generalization":[200],"after":[201],"fusion.":[202],"Extensive":[203],"experiments":[204],"on":[205],"ScanNet":[207],"dataset":[208],"show":[209],"method":[211],"achieves":[212],"both":[213],"great":[214],"generalizability":[215],"high":[217],"accuracy":[219],"simple":[222],"clean":[224],"architecture":[225],"containing":[226],"similar":[228],"amount":[229],"parameters":[231],"baselines.":[234]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-28T00:00:00"}