{"id":"https://openalex.org/W7140697701","doi":"https://doi.org/10.48550/arxiv.2603.23730","title":"An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models","display_name":"An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140697701","doi":"https://doi.org/10.48550/arxiv.2603.23730"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23730","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23730","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.2603.23730","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088597828","display_name":"Sneha Paul","orcid":"https://orcid.org/0000-0001-7731-4196"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paul, Sneha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022678420","display_name":"Zachary Patterson","orcid":"https://orcid.org/0000-0001-8878-7845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patterson, Zachary","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090600716","display_name":"Nizar Bouguila","orcid":"https://orcid.org/0000-0001-7224-7940"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bouguila, Nizar","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":0.9060999751091003,"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":0.9060999751091003,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.00930000003427267,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.007600000128149986,"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/overfitting","display_name":"Overfitting","score":0.7423999905586243},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6330000162124634},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5698000192642212},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5447999835014343},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.501800000667572},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4740999937057495},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4731000065803528},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45840001106262207},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45410001277923584},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.40070000290870667}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7423999905586243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6887000203132629},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6330000162124634},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5698000192642212},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5447999835014343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.531000018119812},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.501800000667572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48820000886917114},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.38190001249313354},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.34850001335144043},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.314300000667572},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23730","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23730","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.2603.23730","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23730","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Point":[0],"cloud":[1],"foundation":[2],"models":[3],"demonstrate":[4,151],"strong":[5],"generalization,":[6],"yet":[7],"adapting":[8],"them":[9],"to":[10,22,83,126,167],"downstream":[11],"tasks":[12],"remains":[13],"challenging":[14],"in":[15,162],"low-data":[16],"regimes.":[17],"Full":[18],"fine-tuning":[19,33],"often":[20],"leads":[21],"overfitting":[23],"and":[24,65,108,130,147,165],"significant":[25],"drift":[26],"from":[27],"pre-trained":[28,75],"representations,":[29],"while":[30,77,174],"existing":[31],"parameter-efficient":[32,66],"(PEFT)":[34],"methods":[35],"mitigate":[36],"this":[37],"issue":[38],"by":[39],"introducing":[40],"additional":[41,93],"trainable":[42],"components":[43],"at":[44],"the":[45,61,74,103],"cost":[46],"of":[47,73],"increased":[48],"inference-time":[49],"latency.":[50],"We":[51,111],"propose":[52],"Momentum-Consistency":[53],"Fine-Tuning":[54],"(MCFT),":[55],"an":[56],"adapter-free":[57],"approach":[58],"that":[59,121,134,152],"bridges":[60],"gap":[62],"between":[63],"full":[64],"fine-tuning.":[67],"MCFT":[68,90,114,153],"selectively":[69],"fine-tunes":[70],"a":[71,79,98,118,131,159,168],"portion":[72],"encoder":[76],"enforcing":[78],"momentum-based":[80],"consistency":[81],"constraint":[82],"preserve":[84],"task-agnostic":[85],"representations.":[86],"Unlike":[87],"PEFT":[88],"methods,":[89,157],"introduces":[91],"no":[92],"representation":[94],"learning":[95],"parameters":[96],"beyond":[97],"standard":[99],"task":[100],"head,":[101],"maintaining":[102],"original":[104],"model's":[105],"parameter":[106],"count":[107],"inference":[109],"efficiency.":[110],"further":[112],"extend":[113],"with":[115,171],"two":[116],"variants:":[117],"semi-supervised":[119,172],"framework":[120],"leverages":[122],"abundant":[123],"unlabeled":[124],"data":[125],"enhance":[127],"few-shot":[128],"performance,":[129],"pruning-based":[132],"variant":[133],"improves":[135],"computational":[136],"efficiency":[137],"through":[138],"structured":[139],"layer":[140],"removal.":[141],"Extensive":[142],"experiments":[143],"on":[144],"object":[145],"recognition":[146],"part":[148],"segmentation":[149],"benchmarks":[150],"consistently":[154],"outperforms":[155],"prior":[156],"achieving":[158],"3.30%":[160],"gain":[161],"5-shot":[163],"settings":[164],"up":[166],"6.13%":[169],"improvement":[170],"learning,":[173],"remaining":[175],"well-suited":[176],"for":[177],"resource-constrained":[178],"deployment.":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-27T00:00:00"}