{"id":"https://openalex.org/W7155391267","doi":"https://doi.org/10.48550/arxiv.2604.20395","title":"SpaCeFormer: Fast Proposal-Free Open-Vocabulary 3D Instance Segmentation","display_name":"SpaCeFormer: Fast Proposal-Free Open-Vocabulary 3D Instance Segmentation","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155391267","doi":"https://doi.org/10.48550/arxiv.2604.20395"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20395","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20395","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.20395","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5104159361","display_name":"Chris Choy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choy, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134420837","display_name":"Junha Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Junha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134399227","display_name":"Chunghyun Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Chunghyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134413054","display_name":"Minsu Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cho, Minsu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134427976","display_name":"Jan Kautz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kautz, Jan","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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6376000046730042,"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"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.6376000046730042,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.09679999947547913,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.07270000129938126,"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/bottleneck","display_name":"Bottleneck","score":0.6753000020980835},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6503000259399414},{"id":"https://openalex.org/keywords/serialization","display_name":"Serialization","score":0.6183000206947327},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6028000116348267},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.430400013923645},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4271000027656555},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.40689998865127563},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.4050999879837036}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.732699990272522},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6753000020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6646999716758728},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6503000259399414},{"id":"https://openalex.org/C52723943","wikidata":"https://www.wikidata.org/wiki/Q1127410","display_name":"Serialization","level":2,"score":0.6183000206947327},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6028000116348267},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43059998750686646},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.430400013923645},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4271000027656555},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.4050999879837036},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.35199999809265137},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.33629998564720154},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.3192000091075897},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3183000087738037},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.31700000166893005},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C2776717989","wikidata":"https://www.wikidata.org/wiki/Q19410276","display_name":"FREAK","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20395","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20395","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20395","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20395","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Open-vocabulary":[0],"3D":[1,78],"instance":[2,79,134],"segmentation":[3,80],"is":[4],"a":[5,47,129,150],"core":[6],"capability":[7],"for":[8,18,123],"robotics":[9],"and":[10,40,96,127,161,166],"AR/VR,":[11],"but":[12],"prior":[13,107,155,171],"methods":[14,172],"trade":[15],"one":[16],"bottleneck":[17],"another:":[19],"multi-stage":[20,67],"2D+3D":[21,68],"pipelines":[22,109],"aggregate":[23],"foundation-model":[24],"outputs":[25],"at":[26,113],"hundreds":[27],"of":[28,63],"seconds":[29,55],"per":[30,56],"scene,":[31],"while":[32],"pseudo-labeled":[33],"end-to-end":[34],"approaches":[35],"rely":[36],"on":[37,159],"fragmented":[38],"masks":[39,135],"external":[41,141],"region":[42],"proposals.":[43,142],"We":[44,70],"present":[45],"SpaCeFormer,":[46],"proposal-free":[48,157],"space-curve":[49],"transformer":[50],"that":[51],"runs":[52],"in":[53],"0.12--0.30":[54],"scene":[57],"across":[58],"standard":[59],"benchmarks,":[60],"2--3":[61],"orders":[62],"magnitude":[64],"faster":[65],"than":[66,106],"pipelines.":[69],"pair":[71],"it":[72,100],"with":[73,120],"SpaCeFormer-3M,":[74],"the":[75,154],"largest":[76],"open-vocabulary":[77],"dataset":[81],"(3.0M":[82],"multi-view-consistent":[83],"captions":[84],"over":[85,153],"604K":[86],"instances":[87],"from":[88,137],"7.4K":[89],"scenes)":[90],"built":[91],"through":[92],"multi-view":[93,97,176],"mask":[94,104],"clustering":[95],"VLM":[98],"captioning;":[99],"reaches":[101],"21$\\times$":[102],"higher":[103],"recall":[105],"single-view":[108],"(54.3%":[110],"vs":[111],"2.5%":[112],"IoU$&gt;$0.5).":[114],"SpaCeFormer":[115],"combines":[116],"spatial":[117],"window":[118],"attention":[119],"Morton-curve":[121],"serialization":[122],"spatially":[124],"coherent":[125],"features,":[126],"uses":[128],"RoPE-enhanced":[130],"decoder":[131],"to":[132],"predict":[133],"directly":[136],"learned":[138],"queries":[139],"without":[140],"On":[143],"ScanNet200":[144],"we":[145,163],"achieve":[146],"11.1":[147],"zero-shot":[148],"mAP,":[149,168],"2.8$\\times$":[151],"improvement":[152],"best":[156],"method;":[158],"ScanNet++":[160],"Replica,":[162],"reach":[164],"22.9":[165],"24.1":[167],"surpassing":[169],"all":[170],"including":[173],"those":[174],"using":[175],"2D":[177],"inputs.":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-24T00:00:00"}
