{"id":"https://openalex.org/W7154028152","doi":"https://doi.org/10.48550/arxiv.2604.09142","title":"Geometry Reinforced Efficient Attention Tuning Equipped with Normals for Robust Stereo Matching","display_name":"Geometry Reinforced Efficient Attention Tuning Equipped with Normals for Robust Stereo Matching","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154028152","doi":"https://doi.org/10.48550/arxiv.2604.09142"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09142","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.09142","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133550298","display_name":"Jiahao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiahao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055839979","display_name":"Xinhong Chen","orcid":"https://orcid.org/0009-0008-3020-5580"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xinhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005920411","display_name":"Zhengmin Jiang","orcid":"https://orcid.org/0000-0002-6498-0234"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Zhengmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133530980","display_name":"Cheng Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Cheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048205934","display_name":"Yung\u2010Hui Li","orcid":"https://orcid.org/0000-0002-0475-3689"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yung-Hui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133538408","display_name":"Jianping Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianping","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/T10531","display_name":"Advanced Vision and Imaging","score":0.9289000034332275,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9289000034332275,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.013500000350177288,"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/T11019","display_name":"Image Enhancement Techniques","score":0.008100000210106373,"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/discriminative-model","display_name":"Discriminative model","score":0.7348999977111816},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7232000231742859},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5817000269889832},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4693000018596649},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45730000734329224},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.45100000500679016},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43689998984336853},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42419999837875366},{"id":"https://openalex.org/keywords/geometric-primitive","display_name":"Geometric primitive","score":0.3930000066757202}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.784500002861023},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7348999977111816},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7232000231742859},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6777999997138977},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5817000269889832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5748000144958496},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4693000018596649},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.45100000500679016},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C181095308","wikidata":"https://www.wikidata.org/wiki/Q1541599","display_name":"Geometric primitive","level":2,"score":0.3930000066757202},{"id":"https://openalex.org/C68537008","wikidata":"https://www.wikidata.org/wiki/Q247932","display_name":"Stereopsis","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3458000123500824},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3034999966621399},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29280000925064087},{"id":"https://openalex.org/C31451488","wikidata":"https://www.wikidata.org/wiki/Q5157586","display_name":"Computer stereo vision","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26570001244544983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09142","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.09142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09142","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":"Preprint"},"sustainable_development_goals":[{"score":0.7336865663528442,"display_name":"Reduced inequalities","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":{"Despite":[0],"remarkable":[1],"advances":[2],"in":[3,32,36,92,127],"image-driven":[4],"stereo":[5],"matching":[6],"over":[7],"the":[8,67,97,119,135,189],"past":[9],"decade,":[10],"Synthetic-to-Realistic":[11],"ZeroShot":[12],"(Syn-to-Real)":[13],"generalization":[14,21],"remains":[15],"an":[16],"open":[17],"challenge.":[18],"This":[19],"suboptimal":[20],"performance":[22],"mainly":[23],"stems":[24],"from":[25],"cross-domain":[26],"shifts":[27],"and":[28,40,60,95,108,146,161,192,200,211],"ill-posed":[29],"ambiguities":[30,148],"inherent":[31],"image":[33,70,93,99],"textures,":[34],"particularly":[35],"occluded,":[37],"textureless,":[38],"repetitive,":[39],"non-Lambertian":[41,128,190],"(specular/transparent)":[42],"regions.":[43,129],"To":[44],"improve":[45],"Synto-Real":[46],"generalization,":[47],"we":[48],"propose":[49],"GREATEN,":[50],"a":[51,81,113],"framework":[52,74],"that":[53],"incorporates":[54],"surface":[55],"normals":[56],"as":[57,172],"domain-invariant,":[58],"object-intrinsic,":[59],"discriminative":[61,109],"geometric":[62,103],"cues":[63,91,126],"to":[64,105,197,222],"compensate":[65],"for":[66,143],"limitations":[68],"of":[69,76,121,141],"textures.":[71],"The":[72],"proposed":[73],"consists":[75],"three":[77],"key":[78],"components.":[79],"First,":[80],"Gated":[82],"Contextual-Geometric":[83],"Fusion":[84],"(GCGF)":[85],"module":[86],"adaptively":[87],"suppresses":[88],"unreliable":[89],"contextual":[90],"features":[94,100,104],"fuses":[96],"filtered":[98],"with":[101,218],"normal-driven":[102],"construct":[106],"domain-invariant":[107],"contextual-geometric":[110],"representations.":[111],"Second,":[112],"Specular-Transparent":[114],"Augmentation":[115],"(STA)":[116],"strategy":[117],"improves":[118],"robustness":[120],"GCGF":[122],"against":[123],"misleading":[124],"visual":[125],"Third,":[130],"sparse":[131],"attention":[132],"designs":[133],"preserve":[134],"fine-grained":[136],"global":[137],"feature":[138],"extraction":[139],"capability":[140],"GREATStereo":[142],"handling":[144],"occlusion":[145],"texture-related":[147],"while":[149],"substantially":[150],"reducing":[151],"computational":[152],"overhead,":[153],"including":[154],"Sparse":[155,158],"Spatial":[156],"(SSA),":[157],"Dual-Matching":[159],"(SDMA),":[160],"Simple":[162],"Volume":[163],"(SVA)":[164],"attentions.":[165],"Trained":[166],"exclusively":[167],"on":[168,185,188,194,216],"synthetic":[169],"data":[170],"such":[171],"SceneFlow,":[173],"GREATEN-IGEV":[174,205],"achieves":[175],"outstanding":[176],"Syn-to-Real":[177],"performance.":[178],"Specifically,":[179],"it":[180],"reduces":[181],"errors":[182],"by":[183],"30%":[184],"ETH3D,":[186],"8.5%":[187],"Booster,":[191],"14.1%":[193],"KITTI-2015,":[195],"compared":[196],"FoundationStereo,":[198],"Monster-Stereo,":[199],"DEFOM-Stereo,":[201],"respectively.":[202],"In":[203],"addition,":[204],"runs":[206],"19.2%":[207],"faster":[208],"than":[209],"GREAT-IGEV":[210],"supports":[212],"high-resolution":[213],"(3K)":[214],"inference":[215],"Middlebury":[217],"disparity":[219],"ranges":[220],"up":[221],"768.":[223]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-14T00:00:00"}