{"id":"https://openalex.org/W4387129505","doi":"https://doi.org/10.1109/tiv.2023.3320399","title":"Adaptive Neighbor Embedding for Efficient Stereo Matching","display_name":"Adaptive Neighbor Embedding for Efficient Stereo Matching","publication_year":2023,"publication_date":"2023-09-28","ids":{"openalex":"https://openalex.org/W4387129505","doi":"https://doi.org/10.1109/tiv.2023.3320399"},"language":"en","primary_location":{"id":"doi:10.1109/tiv.2023.3320399","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2023.3320399","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081671837","display_name":"Aixin Chong","orcid":"https://orcid.org/0000-0002-3766-4770"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ai-Xin Chong","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3766-4770","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053811457","display_name":"Hui Yin","orcid":"https://orcid.org/0000-0002-4226-4368"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Yin","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4226-4368","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049355981","display_name":"Jin Wan","orcid":"https://orcid.org/0000-0001-9245-0110"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Wan","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9245-0110","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100766841","display_name":"Yanting Liu","orcid":"https://orcid.org/0000-0002-0828-4023"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan-Ting Liu","raw_affiliation_strings":["Key Laboratory of Beijing for Railway Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0828-4023","affiliations":[{"raw_affiliation_string":"Key Laboratory of Beijing for Railway Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101697227","display_name":"Qianqian Du","orcid":"https://orcid.org/0000-0002-6690-1819"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian-Qian Du","raw_affiliation_strings":["Key Laboratory of Beijing for Railway Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6690-1819","affiliations":[{"raw_affiliation_string":"Key Laboratory of Beijing for Railway Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3368,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.59073082,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"9","issue":"1","first_page":"2449","last_page":"2458"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9948999881744385,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9921000003814697,"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/embedding","display_name":"Embedding","score":0.8011934757232666},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6637685298919678},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6633508205413818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6370810866355896},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5813748240470886},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5609691143035889},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5581896305084229},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5531635284423828},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5307695269584656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.508349597454071},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4808236360549927},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.41133439540863037},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40626052021980286},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.29987648129463196},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24160224199295044}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.8011934757232666},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6637685298919678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6633508205413818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6370810866355896},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5813748240470886},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5609691143035889},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5581896305084229},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5531635284423828},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5307695269584656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.508349597454071},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4808236360549927},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.41133439540863037},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40626052021980286},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29987648129463196},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24160224199295044},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tiv.2023.3320399","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2023.3320399","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Vehicles","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.75,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G61855668","display_name":null,"funder_award_id":"51827813","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1772650917","https://openalex.org/W1921093919","https://openalex.org/W1984475609","https://openalex.org/W2119112357","https://openalex.org/W2150066425","https://openalex.org/W2259424905","https://openalex.org/W2601564443","https://openalex.org/W2604231069","https://openalex.org/W2886944874","https://openalex.org/W2890417831","https://openalex.org/W2952813711","https://openalex.org/W2962802300","https://openalex.org/W2963277791","https://openalex.org/W2963316559","https://openalex.org/W2963617879","https://openalex.org/W2963619659","https://openalex.org/W2963814095","https://openalex.org/W2966926453","https://openalex.org/W2991514044","https://openalex.org/W2997575304","https://openalex.org/W3034514115","https://openalex.org/W3034653753","https://openalex.org/W3048395011","https://openalex.org/W3104104444","https://openalex.org/W3120143319","https://openalex.org/W3172573759","https://openalex.org/W3216406996","https://openalex.org/W4214862245","https://openalex.org/W4312388571","https://openalex.org/W4312846900","https://openalex.org/W4321366760","https://openalex.org/W6631190155","https://openalex.org/W6638039622","https://openalex.org/W6784085021"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2153677915","https://openalex.org/W128856181"],"abstract_inverted_index":{"With":[0],"the":[1,30,39,50,84,93,169],"remarkable":[2],"achievements":[3],"of":[4,87,171],"stereo":[5,17,46,76,154],"matching":[6,18,155],"algorithms":[7,19],"based":[8,20],"on":[9,21],"Convolutional":[10],"neural":[11],"Networks":[12],"(CNNs),":[13],"more":[14,16],"and":[15,37,97,120,140,157],"CNNs":[22],"are":[23],"applied":[24],"to":[25,58],"intelligent":[26],"driving":[27],"research.":[28],"Whereas,":[29],"existing":[31,153],"convolution":[32,107,113],"operators":[33,150],"cannot":[34],"fully":[35],"mine":[36],"utilize":[38],"structure":[40],"information,":[41],"which":[42],"is":[43,72,89],"indispensable":[44],"for":[45,74,151],"matching,":[47],"especially":[48,174],"in":[49,175],"regions":[51],"with":[52],"discontinuous":[53],"disparity.":[54],"In":[55,78],"this":[56],"work,":[57],"ameliorate":[59],"these":[60],"issues,":[61],"an":[62],"adaptive":[63,99,110,125],"neighbor":[64,94,100,111,126],"embedding":[65,112,127],"paradigm":[66],"(":[67,114,129],"<italic":[68,80,115,130,136,141],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[69,81,116,131,137,142],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">ANE</i>":[70,82],"paradigm)":[71],"proposed":[73],"deep":[75],"networks.":[77],"our":[79,172],"paradigm,":[83],"discriminative":[85],"ability":[86],"features":[88],"improved":[90],"by":[91,163],"mining":[92],"correlation":[95],"knowledge":[96],"performing":[98],"aggregation.":[101],"Specifically,":[102],"we":[103],"design":[104],"a":[105],"new":[106],"operator,":[108],"termed":[109,124],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">ANE":[117,132,138,143],"conv</i>":[118,139],")":[119],"its":[121],"simplified":[122],"version,":[123],"filter":[128],"filter</i>":[133,144],").":[134],"Both":[135],"can":[145,158],"be":[146,159],"used":[147],"as":[148],"plug-and-play":[149],"all":[152],"models":[156],"easily":[160],"trained":[161],"end-to-end":[162],"standard":[164],"back-propagation.":[165],"Extensive":[166],"experiments":[167],"emphasize":[168],"performance":[170],"approaches,":[173],"high":[176],"accuracy":[177],"metric.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}