{"id":"https://openalex.org/W7160553288","doi":"https://doi.org/10.48550/arxiv.2605.04506","title":"Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting","display_name":"Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splatting","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160553288","doi":"https://doi.org/10.48550/arxiv.2605.04506"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04506","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.2605.04506","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135540507","display_name":"Binh Long Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Binh Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135556637","display_name":"Kien Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Kien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135553722","display_name":"Sridha Sridharan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sridharan, Sridha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135549095","display_name":"Clinton Fookes","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fookes, Clinton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135577959","display_name":"Peyman Moghadam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moghadam, Peyman","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6455000042915344,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6455000042915344,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.042399998754262924,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.03779999911785126,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/leverage","display_name":"Leverage (statistics)","score":0.5710999965667725},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5295000076293945},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5236999988555908},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5006999969482422},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4864000082015991},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4616999924182892},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.444599986076355},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.42239999771118164},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.40709999203681946},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.40709999203681946}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8119000196456909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6324999928474426},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5710999965667725},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5295000076293945},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5236999988555908},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5185999870300293},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5006999969482422},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.40709999203681946},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3953999876976013},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.35120001435279846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.26440000534057617},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04506","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.2605.04506","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04506","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7481876015663147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,87],"introduce":[1],"Ilov3Splat,":[2],"a":[3,170],"novel":[4],"framework":[5,130],"for":[6,175],"instance-level":[7,36],"open-vocabulary":[8,159],"3D":[9,14,43,85,120,136,177],"scene":[10,53,178],"understanding":[11],"built":[12],"on":[13,22,139,151],"Gaussian":[15,60,125],"Splatting":[16],"(3D-GS).":[17],"Most":[18],"prior":[19,158],"work":[20],"depends":[21],"2D":[23],"rendering-based":[24],"matching":[25],"or":[26,147],"point-level":[27],"semantic":[28,56],"association,":[29],"which":[30],"undermines":[31],"cross-view":[32],"consistency,":[33],"lacks":[34],"coherent":[35,81],"reasoning,":[37],"and":[38,55,80,166,172],"limits":[39],"precision":[40],"in":[41,84,135,162],"downstream":[42],"tasks.":[44],"To":[45],"address":[46],"these":[47],"limitations,":[48],"our":[49,129],"method":[50],"jointly":[51],"optimizes":[52],"geometry":[54],"representations":[57],"by":[58,118],"augmenting":[59],"splats":[61],"with":[62],"view-consistent":[63],"feature":[64,92],"fields.":[65],"Specifically,":[66],"we":[67],"leverage":[68],"multi-resolution":[69],"hash":[70],"embedding":[71],"to":[72,122,131],"efficiently":[73],"encode":[74],"language-aligned":[75],"CLIP":[76],"features,":[77,116],"enabling":[78],"dense":[79],"language":[82,141],"grounding":[83],"space.":[86],"further":[88],"train":[89],"an":[90],"instance":[91,167],"field":[93],"using":[94],"contrastive":[95],"loss":[96],"over":[97],"SAM":[98],"masks,":[99],"supporting":[100],"fine-grained":[101],"object":[102,164],"distinction":[103],"across":[104],"views.":[105],"At":[106],"inference":[107],"time,":[108],"CLIP-encoded":[109],"queries":[110],"are":[111],"matched":[112],"against":[113],"the":[114],"learned":[115],"followed":[117],"two-stage":[119],"clustering":[121],"retrieve":[123],"relevant":[124],"groups.":[126],"This":[127],"enables":[128],"identify":[132],"arbitrary":[133],"objects":[134],"scenes":[137],"based":[138],"natural":[140],"descriptions,":[142],"without":[143],"requiring":[144],"category":[145],"supervision":[146],"manual":[148],"annotations.":[149],"Experiments":[150],"standard":[152],"benchmarks":[153],"demonstrate":[154],"that":[155],"Ilov3Splat":[156],"outperforms":[157],"3D-GS":[160],"methods":[161],"both":[163],"selection":[165],"segmentation,":[168],"offering":[169],"flexible":[171],"accurate":[173],"solution":[174],"language-driven":[176],"understanding.":[179],"Project":[180],"page:":[181],"https://csiro-robotics.github.io/Ilov3Splat.":[182]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-08T00:00:00"}