{"id":"https://openalex.org/W4200014833","doi":"https://doi.org/10.1109/dicta52665.2021.9647104","title":"Deep learning based stereo cost aggregation on a small dataset","display_name":"Deep learning based stereo cost aggregation on a small dataset","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200014833","doi":"https://doi.org/10.1109/dicta52665.2021.9647104"},"language":"en","primary_location":{"id":"doi:10.1109/dicta52665.2021.9647104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta52665.2021.9647104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-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/A5107844739","display_name":"Rongcheng Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"funder","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Rongcheng Wu","raw_affiliation_strings":["CSE, University of New South Wales CSIRO Data61,Sydney,Australia","CSE, University of New South Wales CSIRO Data61, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"CSE, University of New South Wales CSIRO Data61,Sydney,Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I1292875679"]},{"raw_affiliation_string":"CSE, University of New South Wales CSIRO Data61, Sydney, Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I31746571","https://openalex.org/I1292875679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041589060","display_name":"Changming Sun","orcid":"https://orcid.org/0000-0001-5943-1989"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"funder","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Changming Sun","raw_affiliation_strings":["CSIRO Data61 CSE, University of New South Wales,Sydney,Australia","CSIRO Data61 CSE, University of New South Wales, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"CSIRO Data61 CSE, University of New South Wales,Sydney,Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I1292875679"]},{"raw_affiliation_string":"CSIRO Data61 CSE, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I42894916","https://openalex.org/I31746571","https://openalex.org/I1292875679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064487134","display_name":"Zhaoying Liu","orcid":"https://orcid.org/0000-0001-6991-0123"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoying Liu","raw_affiliation_strings":["Faculty of Information Technology Beijing University of Technology,Beijing,China","Faculty of Information Technology Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology Beijing University of Technology,Beijing,China","institution_ids":["https://openalex.org/I37796252"]},{"raw_affiliation_string":"Faculty of Information Technology Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055952724","display_name":"Arcot Sowmya","orcid":"https://orcid.org/0000-0001-9236-5063"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Arcot Sowmya","raw_affiliation_strings":["CSE, University of New South Wales,Sydney,Australia","CSE, University of New South Wales, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"CSE, University of New South Wales,Sydney,Australia","institution_ids":["https://openalex.org/I31746571"]},{"raw_affiliation_string":"CSE, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5107844739"],"corresponding_institution_ids":["https://openalex.org/I1292875679","https://openalex.org/I31746571","https://openalex.org/I42894916"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16248366,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"08"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.998199999332428,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.8418360352516174},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7938218712806702},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7898520231246948},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.619932234287262},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5587093234062195},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5441670417785645},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5225322246551514},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4992527961730957},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4777342677116394},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4230252504348755},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4153795540332794},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3595086336135864},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10629922151565552}],"concepts":[{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.8418360352516174},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7938218712806702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7898520231246948},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.619932234287262},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5587093234062195},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5441670417785645},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5225322246551514},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4992527961730957},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4777342677116394},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4230252504348755},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4153795540332794},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3595086336135864},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10629922151565552},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta52665.2021.9647104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta52665.2021.9647104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W63091017","https://openalex.org/W1513100184","https://openalex.org/W1591801644","https://openalex.org/W1674866864","https://openalex.org/W1955055330","https://openalex.org/W1996126470","https://openalex.org/W2104974755","https://openalex.org/W2117248802","https://openalex.org/W2144041313","https://openalex.org/W2150066425","https://openalex.org/W2151103935","https://openalex.org/W2155430240","https://openalex.org/W2259424905","https://openalex.org/W2440384215","https://openalex.org/W2471695703","https://openalex.org/W2560788656","https://openalex.org/W2561377267","https://openalex.org/W2604231069","https://openalex.org/W2963496125","https://openalex.org/W2963619659","https://openalex.org/W2968164956","https://openalex.org/W3034514115","https://openalex.org/W3035241330","https://openalex.org/W3105975204","https://openalex.org/W6635446068"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W2094920358","https://openalex.org/W2041448692","https://openalex.org/W2247121321","https://openalex.org/W2391926582","https://openalex.org/W2087391438","https://openalex.org/W1966831329","https://openalex.org/W2316074893","https://openalex.org/W2020188645","https://openalex.org/W2049930962"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"(DL)":[2],"has":[3],"been":[4],"used":[5],"in":[6,37,53,128],"many":[7],"computer":[8],"vision":[9],"tasks":[10],"including":[11],"stereo":[12,30,80,130],"matching.":[13],"However,":[14],"DL":[15,78],"is":[16,32,83,124,188,191],"data":[17],"hungry,":[18],"and":[19,103,152,173,179],"a":[20,66,89,99,110,125,184],"large":[21,45,100],"number":[22],"of":[23,41,77,92,143],"highly":[24],"accurate":[25],"real-world":[26,86],"training":[27,164],"images":[28],"for":[29,85,119],"matching":[31,81,120,131],"too":[33],"expensive":[34],"to":[35,73,97,133,200],"acquire":[36],"practice.":[38],"The":[39],"majority":[40],"studies":[42],"rely":[43],"on":[44,166,175,192],"simulated":[46,101],"datasets":[47],"during":[48],"training,":[49],"which":[50],"inevitably":[51],"results":[52],"domain":[54],"shift":[55],"problems":[56],"that":[57,82,123,190],"are":[58],"commonly":[59],"compensated":[60],"by":[61,163],"fine-tuning.":[62,105],"This":[63],"work":[64,158],"proposes":[65],"recursive":[67,146],"3D":[68],"convolutional":[69],"neural":[70],"network":[71],"(CNN)":[72],"improve":[74],"the":[75,129,141,144,167,195,201,206],"accuracy":[76],"based":[79,116,154],"suitable":[84],"scenarios":[87],"with":[88,194],"small":[90],"set":[91],"available":[93],"images,":[94],"without":[95,104],"having":[96],"use":[98],"dataset":[102,172],"In":[106],"addition,":[107],"we":[108],"propose":[109],"novel":[111],"scale-invariant":[112],"feature":[113],"transform":[114],"(SIFT)":[115],"adaptive":[117,155],"window":[118],"cost":[121],"computation":[122],"crucial":[126],"step":[127],"pipeline":[132],"enhance":[134],"accuracy.":[135],"Extensive":[136],"end-to-end":[137],"comparative":[138],"experiments":[139],"demonstrate":[140],"superiority":[142],"proposed":[145],"3":[147],"<tex":[148],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[149],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$D$</tex>":[150],"CNN":[151],"SIFT":[153],"windows.":[156],"Our":[157],"achieves":[159],"effective":[160],"generalization":[161],"corroborated":[162],"solely":[165],"indoor":[168],"Middlebury":[169,207],"Stereo":[170,208],"2014":[171],"validating":[174],"outdoor":[176],"KITTI":[177,180],"2012":[178],"2015":[181],"datasets.":[182],"As":[183],"comparison,":[185],"our":[186],"bad-4.0-error":[187],"24.2":[189],"par":[193],"AANet":[196],"(CVPR2020)":[197],"method":[198],"according":[199],"publicly":[202],"evaluated":[203],"report":[204],"from":[205],"Evaluation":[209],"Benchmark.":[210]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
