{"id":"https://openalex.org/W7138221029","doi":"https://doi.org/10.1609/aaai.v40i5.37357","title":"UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning","display_name":"UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138221029","doi":"https://doi.org/10.1609/aaai.v40i5.37357"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i5.37357","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37357","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37357/41319","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37357/41319","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062494269","display_name":"Ankit Dhiman","orcid":"https://orcid.org/0000-0002-9451-2052"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ankit Dhiman","raw_affiliation_strings":["Indian Institute of Science, Bangalore\nSamsung R&D Institute India - Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore\nSamsung R&D Institute India - Bangalore","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121809791","display_name":"Srinath R","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Srinath R","raw_affiliation_strings":["Indian Institute of Science, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108779437","display_name":"Jaswanth Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Jaswanth Reddy","raw_affiliation_strings":["Indian Institute of Science, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore","institution_ids":["https://openalex.org/I59270414"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003154827","display_name":"Lokesh R Boregowda","orcid":"https://orcid.org/0009-0004-5966-4220"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Lokesh R Boregowda","raw_affiliation_strings":["Samsung R&D Institute India - Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute India - Bangalore","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062096014","display_name":"Venkatesh Babu Radhakrishnan","orcid":"https://orcid.org/0000-0002-1926-1804"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkatesh Babu Radhakrishnan","raw_affiliation_strings":["Indian Institute of Science, Bangalore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science, Bangalore","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38547486,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"5","first_page":"3587","last_page":"3595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.8197000026702881,"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":0.8197000026702881,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.043299999088048935,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.021199999377131462,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/segmentation","display_name":"Segmentation","score":0.6840000152587891},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6621999740600586},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6550999879837036},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5848000049591064},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5719000101089478},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.43959999084472656},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41370001435279846},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4004000127315521},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.37599998712539673}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7734000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7253999710083008},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6840000152587891},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6621999740600586},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6550999879837036},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5848000049591064},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5719000101089478},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.37599998712539673},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36390000581741333},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.30390000343322754},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.29030001163482666},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.26510000228881836},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.2558000087738037},{"id":"https://openalex.org/C23690007","wikidata":"https://www.wikidata.org/wiki/Q1411145","display_name":"Radiance","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i5.37357","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37357","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37357/41319","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/37357","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/37357","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i5.37357","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i5.37357","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37357/41319","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310071","display_name":"Indian Institute of Science","ror":"https://ror.org/04dese585"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138221029.pdf","grobid_xml":"https://content.openalex.org/works/W7138221029.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"3D":[0,43],"Gaussian":[1,91],"Splatting":[2],"(3DGS)":[3],"and":[4,79,168,177,183],"Neural":[5],"Radiance":[6],"Fields":[7],"(NeRF)":[8],"have":[9],"advanced":[10],"novel-view":[11],"synthesis.":[12],"Recent":[13],"methods":[14,46],"extend":[15],"multi-view":[16],"2D":[17,35],"segmentation":[18,23,89],"to":[19,41,143,155],"3D,":[20],"enabling":[21],"instance/semantic":[22],"for":[24,65,88],"better":[25],"scene":[26],"understanding.":[27],"A":[28],"key":[29],"challenge":[30],"is":[31,95],"the":[32,109,122,127,144,156,162,180],"inconsistency":[33],"of":[34],"instance":[36,100],"labels":[37,64,101],"across":[38],"views,":[39],"leading":[40],"poor":[42],"predictions.":[44],"Existing":[45],"use":[47],"a":[48,69,84,103,152],"two-stage":[49],"approach":[50],"in":[51,90],"which":[52,165],"some":[53],"rely":[54],"on":[55,179],"contrastive":[56],"learning":[57],"with":[58],"hyperparameter-sensitive":[59],"clustering,":[60],"while":[61],"others":[62],"preprocess":[63],"consistency.":[66],"We":[67],"propose":[68,132],"unified":[70,113],"framework":[71,114],"that":[72],"merges":[73],"these":[74,136],"steps,":[75],"reducing":[76],"training":[77,167],"time":[78],"improving":[80],"performance":[81],"by":[82],"introducing":[83],"learnable":[85],"feature":[86,145,158],"embedding":[87,94],"primitives.":[92],"This":[93],"then":[96],"efficiently":[97],"decoded":[98],"into":[99],"through":[102],"novel":[104],"\"Embedding-to-Label\"":[105],"process,":[106],"effectively":[107],"integrating":[108],"optimization.":[110],"While":[111],"this":[112],"offers":[115],"substantial":[116],"benefits,":[117],"we":[118,131,150],"observed":[119],"artifacts":[120],"at":[121],"object":[123,128],"boundaries.":[124,137],"To":[125],"address":[126],"boundary":[129],"issues,":[130],"hard-mining":[133],"samples":[134],"along":[135],"However,":[138],"directly":[139],"applying":[140],"hard":[141],"mining":[142],"embeddings":[146,159],"proved":[147],"unstable.":[148],"Therefore,":[149],"apply":[151],"linear":[153],"layer":[154],"rasterized":[157],"before":[160],"calculating":[161],"triplet":[163],"loss,":[164],"stabilizes":[166],"significantly":[169],"improves":[170],"performance.":[171],"Our":[172],"method":[173],"outperforms":[174],"baselines":[175],"qualitatively":[176],"quantitatively":[178],"ScanNet,":[181],"Replica3D,":[182],"Messy-Rooms":[184],"datasets.":[185]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}
