{"id":"https://openalex.org/W2987863591","doi":"https://doi.org/10.1109/access.2019.2950924","title":"Learning Efficient Stereo Matching Network With Depth Discontinuity Aware Super-Resolution","display_name":"Learning Efficient Stereo Matching Network With Depth Discontinuity Aware Super-Resolution","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2987863591","doi":"https://doi.org/10.1109/access.2019.2950924","mag":"2987863591"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2950924","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2950924","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890688.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890688.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091734238","display_name":"Chenggang Guo","orcid":"https://orcid.org/0000-0003-4504-8692"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chenggang Guo","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-4504-8692","affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057602298","display_name":"Dongyi Chen","orcid":"https://orcid.org/0000-0001-8112-183X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyi Chen","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090589471","display_name":"Zhiqi Huang","orcid":"https://orcid.org/0000-0002-1506-1063"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqi Huang","raw_affiliation_strings":["School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091734238"],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.5308,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.86742299,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"159712","last_page":"159723"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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":1.0,"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":0.9994999766349792,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/upsampling","display_name":"Upsampling","score":0.9056408405303955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7713888883590698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7527161836624146},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.6996403336524963},{"id":"https://openalex.org/keywords/discontinuity","display_name":"Discontinuity (linguistics)","score":0.6968727111816406},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.624287486076355},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5204013586044312},{"id":"https://openalex.org/keywords/subnetwork","display_name":"Subnetwork","score":0.4827508330345154},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.462988018989563},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44725117087364197},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.44277745485305786},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4345104992389679},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.42776763439178467},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2389942705631256},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18033969402313232}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.9056408405303955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7713888883590698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7527161836624146},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.6996403336524963},{"id":"https://openalex.org/C2777042112","wikidata":"https://www.wikidata.org/wiki/Q5281658","display_name":"Discontinuity (linguistics)","level":2,"score":0.6968727111816406},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.624287486076355},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5204013586044312},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.4827508330345154},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.462988018989563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44725117087364197},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.44277745485305786},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4345104992389679},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.42776763439178467},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2389942705631256},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18033969402313232},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2950924","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2950924","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890688.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6e3540e7d0f44b2db84460ddab7c5d22","is_oa":true,"landing_page_url":"https://doaj.org/article/6e3540e7d0f44b2db84460ddab7c5d22","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 159712-159723 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2950924","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2950924","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08890688.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.5600000023841858}],"awards":[{"id":"https://openalex.org/G3487587305","display_name":null,"funder_award_id":"61572110","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6577396245","display_name":null,"funder_award_id":"2017YFB1002803","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2987863591.pdf","grobid_xml":"https://content.openalex.org/works/W2987863591.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1885185971","https://openalex.org/W1955055330","https://openalex.org/W1975762674","https://openalex.org/W2104974755","https://openalex.org/W2144041313","https://openalex.org/W2150066425","https://openalex.org/W2165114467","https://openalex.org/W2214868166","https://openalex.org/W2259424905","https://openalex.org/W2286655030","https://openalex.org/W2440384215","https://openalex.org/W2494890720","https://openalex.org/W2503186844","https://openalex.org/W2503339013","https://openalex.org/W2520322935","https://openalex.org/W2532511219","https://openalex.org/W2583968476","https://openalex.org/W2604231069","https://openalex.org/W2640966949","https://openalex.org/W2770189357","https://openalex.org/W2883040756","https://openalex.org/W2886944874","https://openalex.org/W2897501237","https://openalex.org/W2901577856","https://openalex.org/W2902593794","https://openalex.org/W2936215687","https://openalex.org/W2949964146","https://openalex.org/W2952813711","https://openalex.org/W2955092942","https://openalex.org/W2963231581","https://openalex.org/W2963277791","https://openalex.org/W2963617879","https://openalex.org/W2963619659","https://openalex.org/W2963679601","https://openalex.org/W2963872790","https://openalex.org/W2963955021","https://openalex.org/W2964116251","https://openalex.org/W2968826970","https://openalex.org/W4300595868","https://openalex.org/W6748117778","https://openalex.org/W6749538343","https://openalex.org/W6752272000","https://openalex.org/W6755076178","https://openalex.org/W6756102510","https://openalex.org/W6760351814","https://openalex.org/W6760905588","https://openalex.org/W6761320738","https://openalex.org/W6761500480","https://openalex.org/W6762374519"],"related_works":["https://openalex.org/W2795471480","https://openalex.org/W2520322935","https://openalex.org/W2104324080","https://openalex.org/W2027589961","https://openalex.org/W2155505427","https://openalex.org/W2140069086","https://openalex.org/W3005941135","https://openalex.org/W2171321620","https://openalex.org/W2537394792","https://openalex.org/W2539424392"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5],"shown":[6],"great":[7],"potential":[8],"to":[9,65,100,124,136,144,156],"provide":[10],"accurate":[11],"depth":[12,58,86,118,128,175],"estimation":[13,55],"based":[14],"on":[15,22,34,71],"stereo":[16,25,80,183],"images.":[17],"Previous":[18],"work":[19],"has":[20,31],"focused":[21],"developing":[23],"robust":[24],"matching":[26,81,184],"architectures,":[27],"while":[28],"little":[29],"attention":[30],"been":[32],"paid":[33],"improving":[35],"the":[36,51,57,78,95,101,105,108,114,126,139,145,149,158,163,169,174,181],"network":[37,170,185],"efficiency.":[38],"In":[39],"this":[40],"paper,":[41],"we":[42,63],"propose":[43,64,155],"an":[44],"efficient":[45],"Siamese":[46],"CNN":[47],"architecture":[48],"that":[49,138,167,180],"combines":[50],"low":[52,73,96],"resolution":[53,74,97],"disparity":[54,98,151,165],"and":[56,68,191],"discontinuity":[59,87,176],"aware":[60,88,172],"super-resolution.":[61],"Specifically,":[62],"construct,":[66],"filter":[67],"perform":[69],"regression":[70],"a":[72,187],"cost":[75],"volume":[76],"through":[77],"designed":[79,135],"backbone":[82],"network.":[83],"A":[84,130],"fast":[85],"super-resolution":[89],"subnetwork":[90],"is":[91,134,142],"proposed":[92,182],"for":[93],"upsampling":[94,132],"map":[99,166],"desired":[102],"resolution.":[103],"Under":[104],"guidance":[106],"of":[107,148,162,173],"intensity":[109],"edge":[110,119],"features":[111],"extracted":[112],"from":[113],"left":[115],"color":[116],"image,":[117],"residuals":[120],"are":[121],"hierarchically":[122],"learned":[123],"refine":[125],"upsampled":[127],"map.":[129,152],"delayed":[131],"structure":[133],"ensure":[137],"computational":[140],"complexity":[141],"proportional":[143],"spatial":[146],"size":[147],"input":[150],"We":[153],"also":[154],"supervise":[157],"first":[159],"derivative":[160],"loss":[161],"predicted":[164],"makes":[168],"adaptively":[171],"edges.":[177],"Experiments":[178],"show":[179],"achieves":[186],"comparable":[188],"prediction":[189],"accuracy":[190],"much":[192],"faster":[193],"running":[194],"speed":[195],"compared":[196],"with":[197],"state-of-the-art":[198],"methods.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":13}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}