{"id":"https://openalex.org/W4414921819","doi":"https://doi.org/10.1109/iccv51701.2025.00544","title":"DiffuMatch: Category-Agnostic Spectral Diffusion Priors for Robust Non-Rigid Shape Matching","display_name":"DiffuMatch: Category-Agnostic Spectral Diffusion Priors for Robust Non-Rigid Shape Matching","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4414921819","doi":"https://doi.org/10.1109/iccv51701.2025.00544"},"language":"en","primary_location":{"id":"doi:10.1109/iccv51701.2025.00544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00544","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":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.23715","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110943268","display_name":"Emery Pierson","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139461","display_name":"Laboratoire d'Informatique de l'\u00c9cole Polytechnique","ror":"https://ror.org/04afed728","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I142476485","https://openalex.org/I4210139461","https://openalex.org/I4210145102","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Emery Pierson","raw_affiliation_strings":["LIX, Ecole Polytechnique"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIX, Ecole Polytechnique","institution_ids":["https://openalex.org/I4210139461"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100385656","display_name":"Lei Li","orcid":"https://orcid.org/0000-0002-4657-4718"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026634347","display_name":"Angela Dai","orcid":"https://orcid.org/0000-0002-6241-8782"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Angela Dai","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072368950","display_name":"Maks Ovsjanikov","orcid":"https://orcid.org/0000-0002-5867-4046"},"institutions":[{"id":"https://openalex.org/I4210139461","display_name":"Laboratoire d'Informatique de l'\u00c9cole Polytechnique","ror":"https://ror.org/04afed728","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I142476485","https://openalex.org/I4210139461","https://openalex.org/I4210145102","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Maks Ovsjanikov","raw_affiliation_strings":["LIX, Ecole Polytechnique"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIX, Ecole Polytechnique","institution_ids":["https://openalex.org/I4210139461"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110943268"],"corresponding_institution_ids":["https://openalex.org/I4210139461"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24791704,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5745","last_page":"5756"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9667999744415283,"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/T10057","display_name":"Face and Expression Recognition","score":0.9667999744415283,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9664999842643738,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9495999813079834,"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/regularization","display_name":"Regularization (linguistics)","score":0.5576000213623047},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.5309000015258789},{"id":"https://openalex.org/keywords/axiomatic-system","display_name":"Axiomatic system","score":0.4374000132083893},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4341999888420105},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4296000003814697},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4124000072479248},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4034999907016754},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.39410001039505005},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.36390000581741333}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5576000213623047},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5162000060081482},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5042999982833862},{"id":"https://openalex.org/C125773388","wikidata":"https://www.wikidata.org/wiki/Q792542","display_name":"Axiomatic system","level":3,"score":0.4374000132083893},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4341999888420105},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4296000003814697},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4034999907016754},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.39410001039505005},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3734000027179718},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35580000281333923},{"id":"https://openalex.org/C167729594","wikidata":"https://www.wikidata.org/wiki/Q17736","display_name":"Axiom","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33169999718666077},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3075999915599823},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C112604564","wikidata":"https://www.wikidata.org/wiki/Q7489226","display_name":"Shape analysis (program analysis)","level":3,"score":0.3001999855041504},{"id":"https://openalex.org/C51820054","wikidata":"https://www.wikidata.org/wiki/Q5508814","display_name":"Functional data analysis","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.2689000070095062},{"id":"https://openalex.org/C191640071","wikidata":"https://www.wikidata.org/wiki/Q5377056","display_name":"Energy functional","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/iccv51701.2025.00544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv51701.2025.00544","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:2507.23715","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.23715","pdf_url":"https://arxiv.org/pdf/2507.23715","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":"pmh:oai:HAL:hal-05434041v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05434041","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ICCV 2025 - International Conference on Computer Vision, Oct 2025, Honolulu, United States. ⟨10.48550/arXiv.2507.23715⟩","raw_type":"Conference papers"},{"id":"doi:10.48550/arxiv.2507.23715","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.23715","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2507.23715","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.23715","pdf_url":"https://arxiv.org/pdf/2507.23715","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"functional":[1,28,68,110,128,160,190],"maps":[2,129,161],"have":[3],"recently":[4],"emerged":[5],"as":[6,183],"a":[7,124,140,197],"powerful":[8],"tool":[9],"for":[10,34,61,67,101,221],"solving":[11],"non-rigid":[12,223],"shape":[13,164,224],"correspondence":[14],"tasks.":[15],"Methods":[16],"that":[17,105,169,210],"use":[18],"this":[19,44,97],"approach":[20],"combine":[21],"the":[22,27,47,52,63,72,77,80,83,92,102,131,149,154,170,205],"power":[23],"and":[24,37,55,79,109,175],"flexibility":[25],"of":[26,82,91,127,143,157,189],"map":[29,69,111],"framework,":[30],"with":[31,116],"data-driven":[32,117],"learning":[33,48],"improved":[35],"accuracy":[36,78],"generality.":[38],"However,":[39],"most":[40],"existing":[41],"methods":[42],"in":[43,130,204],"area":[45],"restrict":[46],"aspect":[49],"only":[50,86],"to":[51,87,152,215],"feature":[53],"functions":[54],"still":[56],"rely":[57],"on":[58,162],"axiomatic":[59,93,219],"modeling":[60],"formulating":[62],"training":[64,112],"loss":[65],"or":[66,187],"regularization":[70,108,213],"inside":[71],"networks.":[73],"This":[74],"limits":[75],"both":[76,106],"applicability":[81],"resulting":[84,150],"approaches":[85,220],"scenarios":[88],"where":[89],"assumptions":[90],"models":[94,172,203],"hold.":[95],"In":[96],"work,":[98],"we":[99,121,167],"show,":[100],"first":[103,122],"time,":[104],"in-network":[107],"can":[113,176],"be":[114],"replaced":[115],"methods.":[118],"For":[119],"this,":[120],"train":[123],"generative":[125,136],"model":[126,151],"spectral":[132,206],"domain":[133],"using":[134],"score-based":[135],"modeling,":[137],"built":[138],"from":[139,201],"large":[141],"collection":[142],"high-quality":[144],"maps.":[145,191],"We":[146],"then":[147],"exploit":[148],"promote":[153],"structural":[155],"properties":[156],"ground":[158],"truth":[159],"new":[163],"collections.":[165],"Remarkably,":[166],"demonstrate":[168,209],"learned":[171,212],"are":[173],"category-agnostic,":[174],"fully":[177],"replace":[178],"commonly":[179],"used":[180],"strategies":[181],"such":[182],"enforcing":[184],"Laplacian":[185],"commutativity":[186],"orthogonality":[188],"Our":[192,226],"key":[193],"technical":[194],"contribution":[195],"is":[196,228],"novel":[198],"distillation":[199],"strategy":[200],"diffusion":[202],"domain.":[207],"Experiments":[208],"our":[211],"leads":[214],"better":[216],"results":[217],"than":[218],"zero-shot":[222],"matching.":[225],"code":[227],"available":[229],"at:":[230],"https://github.com/daidedou/diffumatch/":[231]},"counts_by_year":[],"updated_date":"2026-05-06T06:03:25.996018","created_date":"2025-10-10T00:00:00"}