{"id":"https://openalex.org/W7163803962","doi":"https://doi.org/10.3390/jimaging12060253","title":"3D Geometry-Aware Efficient Feature Matching for Weakly Textured Scenes","display_name":"3D Geometry-Aware Efficient Feature Matching for Weakly Textured Scenes","publication_year":2026,"publication_date":"2026-06-07","ids":{"openalex":"https://openalex.org/W7163803962","doi":"https://doi.org/10.3390/jimaging12060253","pmid":"https://pubmed.ncbi.nlm.nih.gov/42346916"},"language":"en","primary_location":{"id":"doi:10.3390/jimaging12060253","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging12060253","pdf_url":null,"source":{"id":"https://openalex.org/S2736465063","display_name":"Journal of Imaging","issn_l":"2313-433X","issn":["2313-433X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/jimaging12060253","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138080386","display_name":"Libo Sun","orcid":"https://orcid.org/0000-0002-7838-9410"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libo Sun","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"],"raw_orcid":"https://orcid.org/0000-0002-7838-9410","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090204917","display_name":"Yidong Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yidong Yan","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"],"raw_orcid":"https://orcid.org/0009-0002-8746-5937","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138078072","display_name":"Wenqi Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqi Yang","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5138072137","display_name":"Wenhu Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhu Qin","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1782},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85369919,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":"6","first_page":"253","last_page":"253"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.6352999806404114,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.6352999806404114,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.10499999672174454,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.07500000298023224,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.629800021648407},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5126000046730042},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4796000123023987},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47920000553131104},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4244999885559082},{"id":"https://openalex.org/keywords/fusion-mechanism","display_name":"Fusion mechanism","score":0.4156000018119812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4047999978065491},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.38839998841285706}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7594000101089478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7498000264167786},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.629800021648407},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5648000240325928},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5126000046730042},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.4156000018119812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3596999943256378},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3434999883174896},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.2712000012397766},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.27090001106262207}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/jimaging12060253","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging12060253","pdf_url":null,"source":{"id":"https://openalex.org/S2736465063","display_name":"Journal of Imaging","issn_l":"2313-433X","issn":["2313-433X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging","raw_type":"journal-article"},{"id":"pmid:42346916","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42346916","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of imaging","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:0e1bf5114e65466daa45a9ba54e6a051","is_oa":false,"landing_page_url":"https://doaj.org/article/0e1bf5114e65466daa45a9ba54e6a051","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"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":"Journal of Imaging, Vol 12, Iss 6, p 253 (2026)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13301209","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13301209/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"J Imaging","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/jimaging12060253","is_oa":true,"landing_page_url":"https://doi.org/10.3390/jimaging12060253","pdf_url":null,"source":{"id":"https://openalex.org/S2736465063","display_name":"Journal of Imaging","issn_l":"2313-433X","issn":["2313-433X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Imaging","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7849372029304504,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Local":[0],"feature":[1,48,96,107],"matching":[2],"plays":[3],"a":[4,45,87,91,111,117,122,145],"critical":[5],"role":[6],"in":[7,14,100,171,185,218],"robotic":[8,61],"SLAM":[9],"and":[10,28,74,90,98,121,155,167,177,213],"visual":[11,178],"localization.":[12],"However,":[13],"weakly":[15,101,187],"textured":[16,102,188],"indoor":[17,103],"industrial":[18,104,189],"environments,":[19],"lightweight":[20,46],"appearance-based":[21],"methods":[22],"often":[23],"struggle":[24],"to":[25,68,94,132,148],"learn":[26],"discriminative":[27],"stable":[29],"local":[30,134],"features.":[31],"To":[32,51],"address":[33,52],"this":[34,36,79],"challenge,":[35],"paper":[37],"proposes":[38],"GAEFeat,":[39],"short":[40],"for":[41,216],"Geometry-Aware":[42],"Efficient":[43],"Feature,":[44],"vision-geometric":[47],"learning":[49,97],"network.":[50],"the":[53,130,142,202],"scarcity":[54],"of":[55,116,197],"specialized":[56],"training":[57],"data,":[58],"we":[59,81,109],"integrated":[60],"arm":[62],"pose":[63,173],"priors":[64,154],"with":[65,181],"depth":[66],"information":[67],"automatically":[69],"generate":[70],"cross-view":[71],"supervision":[72],"signals":[73],"surface-normal":[75],"labels.":[76],"Based":[77],"on":[78,201],"strategy,":[80],"constructed":[82],"two":[83],"complementary":[84],"datasets,":[85],"including":[86],"simulated":[88],"dataset":[89],"real-world":[92],"dataset,":[93],"support":[95],"evaluation":[99],"environments.":[105,221],"For":[106],"extraction,":[108],"design":[110],"dual":[112],"enhancement":[113,124],"mechanism":[114,147],"consisting":[115],"geometric":[118,153],"auxiliary":[119],"branch":[120],"geometry-aware":[123],"(GAE)":[125],"module.":[126],"The":[127,191],"former":[128],"guides":[129],"network":[131],"perceive":[133],"surface":[135,138],"structures":[136],"through":[137],"normal":[139],"supervision,":[140],"while":[141],"latter":[143],"utilizes":[144],"gating":[146],"achieve":[149],"deep":[150],"fusion":[151],"between":[152],"2D":[156],"texture":[157],"descriptors.":[158],"Experimental":[159],"results":[160],"demonstrate":[161],"that":[162],"GAEFeat":[163],"achieves":[164,193],"strong":[165],"robustness":[166],"high":[168],"inference":[169,195],"efficiency":[170],"relative":[172],"estimation,":[174,176],"homography":[175],"localization":[179],"tasks,":[180],"particularly":[182],"notable":[183],"advantages":[184],"near-field,":[186],"scenes.":[190],"framework":[192],"an":[194],"latency":[196],"only":[198],"3.9":[199],"ms":[200],"NVIDIA":[203],"Jetson":[204],"AGX":[205],"Orin":[206],"edge":[207,219],"platform,":[208],"demonstrating":[209],"its":[210],"real-time":[211],"capability":[212],"practical":[214],"potential":[215],"deployment":[217],"computing":[220]},"counts_by_year":[],"updated_date":"2026-07-27T08:26:11.824852","created_date":"2026-06-08T00:00:00"}
