{"id":"https://openalex.org/W7152972139","doi":"https://doi.org/10.48550/arxiv.2604.07997","title":"Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environments","display_name":"Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environments","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152972139","doi":"https://doi.org/10.48550/arxiv.2604.07997"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.07997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07997","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.07997","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133375450","display_name":"Yun Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133331364","display_name":"Jianjun Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Jianjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133371547","display_name":"Jian Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133348297","display_name":"Jin Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Jin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133373315","display_name":"Na Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Na","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6899999976158142,"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":0.6899999976158142,"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/T11448","display_name":"Face recognition and analysis","score":0.05000000074505806,"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":0.03889999911189079,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6255999803543091},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5065000057220459},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.44449999928474426},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4375999867916107},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4350000023841858},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.429500013589859},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4242999851703644},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.4083999991416931},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3968999981880188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7444999814033508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6669999957084656},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6255999803543091},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5065000057220459},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47450000047683716},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4375999867916107},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.429500013589859},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.4083999991416931},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3968999981880188},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.385699987411499},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3650999963283539},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.34459999203681946},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30410000681877136},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.26989999413490295},{"id":"https://openalex.org/C20894473","wikidata":"https://www.wikidata.org/wiki/Q1116105","display_name":"Object model","level":3,"score":0.2696000039577484}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.07997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07997","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.07997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07997","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Incremental":[0,40],"3D":[1,18,41,47,102,156,183],"object":[2,71,174,184],"perception":[3,48,80],"is":[4,114,213],"a":[5,38,51,68,111,140,168],"critical":[6],"step":[7],"toward":[8],"embodied":[9],"intelligence":[10],"in":[11,74,108],"dynamic":[12],"indoor":[13],"environments.":[14],"However,":[15],"existing":[16],"incremental":[17,182],"detection":[19],"methods":[20],"rely":[21],"on":[22,127,194],"extensive":[23],"annotations":[24],"of":[25,63,81,121],"novel":[26,53,173],"classes":[27],"for":[28,172,180],"satisfactory":[29],"performance.":[30],"To":[31,105],"address":[32],"this":[33],"limitation,":[34],"we":[35,186],"propose":[36],"FI3Det,":[37],"Few-shot":[39],"Detection":[42],"framework":[43,179],"that":[44],"enables":[45],"efficient":[46],"with":[49],"only":[50],"few":[52],"samples":[54],"by":[55],"leveraging":[56],"vision-language":[57],"models":[58],"(VLMs)":[59],"to":[60,78,88,117,159],"learn":[61],"knowledge":[62],"unseen":[64,82],"categories.":[65,83],"FI3Det":[66,138,203],"introduces":[67],"VLM-guided":[69],"unknown":[70,90],"learning":[72],"module":[73],"the":[75,119,177],"base":[76],"stage":[77],"enhance":[79],"Specifically,":[84],"it":[85],"employs":[86],"VLMs":[87],"mine":[89],"objects":[91],"and":[92,100,123,131,155,190,199,206],"extract":[93],"comprehensive":[94],"representations,":[95,110],"including":[96],"2D":[97,153],"semantic":[98,154],"features":[99,125,158],"class-agnostic":[101],"bounding":[103],"boxes.":[104],"mitigate":[106],"noise":[107],"these":[109],"weighting":[112],"mechanism":[113,171],"further":[115],"designed":[116],"re-weight":[118],"contributions":[120],"point-":[122],"box-level":[124],"based":[126],"their":[128],"spatial":[129],"locations":[130],"feature":[132],"consistency":[133],"within":[134],"each":[135],"box.":[136],"Moreover,":[137],"proposes":[139],"gated":[141],"multimodal":[142,169],"prototype":[143],"imprinting":[144],"module,":[145],"where":[146,202],"category":[147],"prototypes":[148],"are":[149,164],"constructed":[150],"from":[151],"aligned":[152],"geometric":[157],"compute":[160],"classification":[161],"scores,":[162],"which":[163],"then":[165],"fused":[166],"via":[167],"gating":[170],"detection.":[175],"As":[176],"first":[178],"few-shot":[181],"detection,":[185],"establish":[187],"both":[188],"batch":[189],"sequential":[191],"evaluation":[192],"settings":[193],"two":[195],"datasets,":[196],"ScanNet":[197],"V2":[198],"SUN":[200],"RGB-D,":[201],"achieves":[204],"strong":[205],"consistent":[207],"improvements":[208],"over":[209],"baseline":[210],"methods.":[211],"Code":[212],"available":[214],"at":[215],"https://github.com/zyrant/FI3Det.":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-11T00:00:00"}