{"id":"https://openalex.org/W7155158238","doi":"https://doi.org/10.48550/arxiv.2604.19135","title":"Diff-SBSR: Learning Multimodal Feature-Enhanced Diffusion Models for Zero-Shot Sketch-Based 3D Shape Retrieval","display_name":"Diff-SBSR: Learning Multimodal Feature-Enhanced Diffusion Models for Zero-Shot Sketch-Based 3D Shape Retrieval","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155158238","doi":"https://doi.org/10.48550/arxiv.2604.19135"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.19135","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19135","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.19135","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134286201","display_name":"Hang Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cheng, Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071033066","display_name":"Fanhe Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Fanhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134307058","display_name":"Long Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5134286201"],"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":0.8788999915122986,"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":0.8788999915122986,"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":0.02810000069439411,"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/T11448","display_name":"Face recognition and analysis","score":0.015799999237060547,"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/sketch","display_name":"Sketch","score":0.8256000280380249},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6643000245094299},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.621999979019165},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.4805999994277954},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4562000036239624},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4025999903678894},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.3790000081062317},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.37599998712539673}],"concepts":[{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.8256000280380249},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7835000157356262},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6643000245094299},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6047999858856201},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.4805999994277954},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4562000036239624},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.3790000081062317},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C2776674983","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image editing","level":3,"score":0.36469998955726624},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35269999504089355},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3163999915122986},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30000001192092896},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.19135","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19135","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":"doi:10.48550/arxiv.2604.19135","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19135","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.730117678642273,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"the":[3,29,35,126,140,186],"first":[4],"exploration":[5],"of":[6,31,38,142],"text-to-image":[7],"diffusion":[8,48,128],"models":[9,49,91],"for":[10,62,83],"zero-shot":[11,25,63],"sketch-based":[12,18],"3D":[13,19,88],"shape":[14,20,56],"retrieval":[15,21],"(ZS-SBSR).":[16],"Existing":[17],"methods":[22],"struggle":[23,92],"in":[24,225],"settings":[26],"due":[27,95],"to":[28,73,96,189,202],"absence":[30],"category":[32],"supervision":[33],"and":[34,54,75,86,100,133,146,155,166,205],"extreme":[36,98],"sparsity":[37],"sketch":[39,149,203],"inputs.":[40],"Our":[41],"key":[42],"insight":[43],"is":[44],"that":[45,124,218],"large-scale":[46],"pretrained":[47,162],"inherently":[50],"exhibit":[51],"open-vocabulary":[52],"capability":[53],"strong":[55],"bias,":[57],"making":[58],"them":[59],"well":[60],"suited":[61],"visual":[64,132,157,164],"retrieval.":[65],"We":[66],"leverage":[67],"a":[68,104,120,161],"frozen":[69,127],"Stable":[70],"Diffusion":[71,90],"backbone":[72,129],"extract":[74],"aggregate":[76],"discriminative":[77],"representations":[78],"from":[79,108,136,160],"intermediate":[80],"U-Net":[81],"layers":[82],"both":[84],"sketches":[85,94],"rendered":[87],"views.":[89],"with":[93,130,176],"their":[97],"abstraction":[99],"sparsity,":[101],"compounded":[102],"by":[103,171,181],"significant":[105],"domain":[106],"gap":[107],"natural":[109],"images.":[110],"To":[111],"address":[112],"this":[113],"limitation":[114],"without":[115],"costly":[116],"retraining,":[117],"we":[118,152,184],"introduce":[119],"multimodal":[121],"feature-enhanced":[122],"strategy":[123],"conditions":[125],"complementary":[131],"textual":[134,169,178],"cues":[135],"CLIP,":[137],"explicitly":[138],"enhancing":[139],"ability":[141],"semantic":[143],"context":[144],"capture":[145],"concentrating":[147],"on":[148,213],"contours.":[150],"Specifically,":[151],"inject":[153],"global":[154],"local":[156],"features":[158],"derived":[159],"CLIP":[163],"encoder,":[165],"incorporate":[167],"enriched":[168],"guidance":[170],"combining":[172],"learnable":[173],"soft":[174],"prompts":[175],"hard":[177],"descriptions":[179],"generated":[180],"BLIP.":[182],"Furthermore,":[183],"employ":[185],"Circle-T":[187],"loss":[188],"dynamically":[190],"strengthen":[191],"positive-pair":[192],"attraction":[193],"once":[194],"negative":[195],"samples":[196],"are":[197],"sufficiently":[198],"separated,":[199],"thereby":[200],"adapting":[201],"noise":[204],"enabling":[206],"more":[207],"effective":[208],"sketch-3D":[209],"alignment.":[210],"Extensive":[211],"experiments":[212],"two":[214],"public":[215],"benchmarks":[216],"demonstrate":[217],"our":[219],"method":[220],"consistently":[221],"outperforms":[222],"state-of-the-art":[223],"approaches":[224],"ZS-SBSR.":[226]},"counts_by_year":[],"updated_date":"2026-04-23T06:20:18.424754","created_date":"2026-04-23T00:00:00"}