{"id":"https://openalex.org/W4416119137","doi":"https://doi.org/10.1109/iccv51701.2025.00541","title":"GARF: Learning Generalizable 3D Reassembly for Real-World Fractures","display_name":"GARF: Learning Generalizable 3D Reassembly for Real-World Fractures","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416119137","doi":"https://doi.org/10.1109/iccv51701.2025.00541"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2504.05400","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101105931","display_name":"Sihang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sihang Li","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120645613","display_name":"Zeyu Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zeyu Jiang","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110803972","display_name":"G. Chen","orcid":"https://orcid.org/0000-0002-8253-2830"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Grace Chen","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102008316","display_name":"Chenyang Xu","orcid":"https://orcid.org/0000-0002-3127-0017"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenyang Xu","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Siqi Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siqi Tan","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120699218","display_name":"Xue Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xue Wang","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008691993","display_name":"Irving Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Irving Fang","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061722640","display_name":"Kristof Zyskowski","orcid":"https://orcid.org/0000-0002-5680-6412"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kristof Zyskowski","raw_affiliation_strings":["Yale University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale University","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065339316","display_name":"Shannon P. McPherron","orcid":"https://orcid.org/0000-0002-2063-468X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shannon P. McPherron","raw_affiliation_strings":["Max Planck Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Max Planck Institute","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055371516","display_name":"\u0420\u0430\u0434\u0443 \u0419\u043e\u0432\u0438\u0442\u0430","orcid":"https://orcid.org/0000-0001-9531-1159"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Radu Iovita","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chen Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Feng","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jing Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["New York University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":12,"corresponding_author_ids":["https://openalex.org/A5101105931"],"corresponding_institution_ids":["https://openalex.org/I57206974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3138062,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5711","last_page":"5721"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9404000043869019,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9404000043869019,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.019899999722838402,"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":0.011699999682605267,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.6656000018119812},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.628600001335144},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.6248000264167786},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6144999861717224},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4616999924182892},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.435699999332428},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4020000100135803}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6876999735832214},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6733999848365784},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.6656000018119812},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.628600001335144},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.6248000264167786},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6144999861717224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5580000281333923},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iccv51701.2025.00541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2504.05400","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.05400","pdf_url":"https://arxiv.org/pdf/2504.05400","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2504.05400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2504.05400","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2504.05400","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.05400","pdf_url":"https://arxiv.org/pdf/2504.05400","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G736372750","display_name":null,"funder_award_id":"2152565,2238968,2322242,2426993","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"3D":[0,60,166],"reassembly":[1,61],"is":[2,28],"a":[3,58,111],"challenging":[4],"spatial":[5],"intelligence":[6],"task":[7],"with":[8,77,103],"broad":[9],"applications":[10],"across":[11,123,173],"scientific":[12],"domains.":[13],"While":[14],"large-scale":[15],"synthetic":[16,38,141,161],"datasets":[17,39],"have":[18,131],"fueled":[19],"promising":[20],"learning-based":[21],"approaches,":[22],"their":[23],"generalizability":[24],"to":[25,42,70,93,163],"different":[26],"domains":[27],"limited.":[29],"Critically,":[30],"it":[31],"remains":[32],"uncertain":[33],"whether":[34],"models":[35],"trained":[36],"on":[37,139,158,160],"can":[40],"generalize":[41],"real-world":[43,64,120,143,165],"fractures":[44],"where":[45],"breakage":[46],"patterns":[47],"are":[48,186],"more":[49],"complex.":[50],"To":[51],"bridge":[52],"this":[53],"gap,":[54],"we":[55,87,108],"propose":[56],"GARF,":[57],"generalizable":[59],"framework":[62],"for":[63,114],"fractures.":[65,100],"GARF":[66],"leverages":[67],"fracture-aware":[68],"pretraining":[69],"learn":[71],"fracture":[72,121,179],"features":[73],"from":[74],"individual":[75],"fragments,":[76],"flow":[78],"matching":[79],"enabling":[80],"precise":[81],"6-DoF":[82],"alignments.":[83],"At":[84],"inference":[85],"time,":[86],"introduce":[88],"one-step":[89],"preassembly,":[90],"improving":[91],"robustness":[92],"unseen":[94,174],"objects":[95],"and":[96,106,116,127,142,150,177,184],"varying":[97],"numbers":[98],"of":[99],"In":[101],"collaboration":[102],"archaeologists,":[104],"paleoanthropologists,":[105],"ornithologists,":[107],"curate":[109],"Fractura,":[110],"diverse":[112,178],"dataset":[113],"vision":[115],"learning":[117],"communities,":[118],"featuring":[119],"types":[122],"ceramics,":[124],"bones,":[125],"eggshells,":[126],"lithics.":[128],"Comprehensive":[129],"experiments":[130],"shown":[132],"our":[133],"approach":[134],"consistently":[135],"outperforms":[136],"state-of-the-art":[137],"methods":[138],"both":[140],"datasets,":[144],"achieving":[145],"82.87\\%":[146],"lower":[147],"rotation":[148],"error":[149],"25.15\\%":[151],"higher":[152],"part":[153],"accuracy.":[154],"This":[155],"sheds":[156],"light":[157],"training":[159],"data":[162,183],"advance":[164],"puzzle":[167],"solving,":[168],"demonstrating":[169],"its":[170],"strong":[171],"generalization":[172],"object":[175],"shapes":[176],"types.":[180],"GARF's":[181],"code,":[182],"demo":[185],"available":[187],"at":[188],"https://ai4ce.github.io/GARF/.":[189]},"counts_by_year":[],"updated_date":"2026-05-06T06:03:25.996018","created_date":"2025-10-10T00:00:00"}
