{"id":"https://openalex.org/W3203349069","doi":"https://doi.org/10.1109/access.2021.3117710","title":"Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching","display_name":"Non-Learning Stereo-Aided Depth Completion Under Mis-Projection via Selective Stereo Matching","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3203349069","doi":"https://doi.org/10.1109/access.2021.3117710","mag":"3203349069"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3117710","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3117710","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09558759.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":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09558759.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032965190","display_name":"Yasuhiro Yao","orcid":"https://orcid.org/0000-0002-4195-8229"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasuhiro Yao","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, 153-0041 Japan and NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan. (e-mail: yao-yasuhiro@g.ecc.u-tokyo.ac.jp)"],"raw_orcid":"https://orcid.org/0000-0002-4195-8229","affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, 153-0041 Japan and NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan. (e-mail: yao-yasuhiro@g.ecc.u-tokyo.ac.jp)","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027278922","display_name":"Ryoichi Ishikawa","orcid":"https://orcid.org/0000-0001-6904-3437"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryoichi Ishikawa","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, 153-0041 Japan"],"raw_orcid":"https://orcid.org/0000-0001-6904-3437","affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, 153-0041 Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103334343","display_name":"Shingo Ando","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shingo Ando","raw_affiliation_strings":["NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065638415","display_name":"Kana Kurata","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kana Kurata","raw_affiliation_strings":["NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029497516","display_name":"Naoki Ito","orcid":"https://orcid.org/0000-0002-1884-8494"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Naoki Ito","raw_affiliation_strings":["NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069602957","display_name":"Jun Shimamura","orcid":"https://orcid.org/0000-0002-3424-253X"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jun Shimamura","raw_affiliation_strings":["NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan"],"raw_orcid":"https://orcid.org/0000-0002-3424-253X","affiliations":[{"raw_affiliation_string":"NTT Human Informatics Laboratories, Kanagawa, 239-0847 Japan","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021438586","display_name":"Takeshi Oishi","orcid":"https://orcid.org/0000-0002-2010-2608"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takeshi Oishi","raw_affiliation_strings":["Institute of Industrial Science, The University of Tokyo, Tokyo, 153-0041 Japan"],"raw_orcid":"https://orcid.org/0000-0002-2010-2608","affiliations":[{"raw_affiliation_string":"Institute of Industrial Science, The University of Tokyo, Tokyo, 153-0041 Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.3769,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.60560527,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"9","issue":null,"first_page":"136674","last_page":"136686"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9991999864578247,"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":0.9987999796867371,"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/depth-map","display_name":"Depth map","score":0.8071891665458679},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7350590229034424},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.671180784702301},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6589232683181763},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6285049319267273},{"id":"https://openalex.org/keywords/measured-depth","display_name":"Measured depth","score":0.5000803470611572},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.45532846450805664},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.4136485457420349},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32752376794815063},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20188850164413452},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.09509596228599548},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09192553162574768}],"concepts":[{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.8071891665458679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7350590229034424},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.671180784702301},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6589232683181763},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6285049319267273},{"id":"https://openalex.org/C113346285","wikidata":"https://www.wikidata.org/wiki/Q6804193","display_name":"Measured depth","level":2,"score":0.5000803470611572},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.45532846450805664},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.4136485457420349},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32752376794815063},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20188850164413452},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.09509596228599548},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09192553162574768},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2021.3117710","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3117710","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09558759.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:arXiv.org:2210.01436","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.01436","pdf_url":"https://arxiv.org/pdf/2210.01436","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:doaj.org/article:3a394510d6794b93bfe14bbbba86ca61","is_oa":true,"landing_page_url":"https://doaj.org/article/3a394510d6794b93bfe14bbbba86ca61","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 9, Pp 136674-136686 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3117710","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3117710","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09558759.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":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8100000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320909","display_name":"Keio University","ror":"https://ror.org/02kn6nx58"},{"id":"https://openalex.org/F4320322832","display_name":"University of Tokyo","ror":"https://ror.org/057zh3y96"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3203349069.pdf","grobid_xml":"https://content.openalex.org/works/W3203349069.grobid-xml"},"referenced_works_count":63,"referenced_works":["https://openalex.org/W55377555","https://openalex.org/W1926943707","https://openalex.org/W1990398405","https://openalex.org/W2028542749","https://openalex.org/W2085261163","https://openalex.org/W2092663520","https://openalex.org/W2098678088","https://openalex.org/W2104600947","https://openalex.org/W2108134361","https://openalex.org/W2109945199","https://openalex.org/W2115579991","https://openalex.org/W2117190576","https://openalex.org/W2140836349","https://openalex.org/W2151626079","https://openalex.org/W2152354413","https://openalex.org/W2152864241","https://openalex.org/W2153388956","https://openalex.org/W2217105717","https://openalex.org/W2288951236","https://openalex.org/W2412644588","https://openalex.org/W2474236724","https://openalex.org/W2512689497","https://openalex.org/W2567028727","https://openalex.org/W2770902190","https://openalex.org/W2892161145","https://openalex.org/W2899479761","https://openalex.org/W2914232966","https://openalex.org/W2955860317","https://openalex.org/W2961926014","https://openalex.org/W2963316641","https://openalex.org/W2963793227","https://openalex.org/W2963867516","https://openalex.org/W2964326562","https://openalex.org/W2966927056","https://openalex.org/W2969202876","https://openalex.org/W2986701260","https://openalex.org/W2991593752","https://openalex.org/W2992464978","https://openalex.org/W2998031326","https://openalex.org/W3003796433","https://openalex.org/W3009783082","https://openalex.org/W3037631969","https://openalex.org/W3041459390","https://openalex.org/W3080631149","https://openalex.org/W3093769466","https://openalex.org/W3099306897","https://openalex.org/W3100569787","https://openalex.org/W3109128945","https://openalex.org/W3114961520","https://openalex.org/W3120624712","https://openalex.org/W3137106869","https://openalex.org/W3145086533","https://openalex.org/W3165495321","https://openalex.org/W3175561084","https://openalex.org/W4241716071","https://openalex.org/W6674866097","https://openalex.org/W6676402299","https://openalex.org/W6676600194","https://openalex.org/W6688774849","https://openalex.org/W6781065823","https://openalex.org/W6782475300","https://openalex.org/W6784085021","https://openalex.org/W6797492637"],"related_works":["https://openalex.org/W2915493008","https://openalex.org/W2089613850","https://openalex.org/W3009665706","https://openalex.org/W4385413672","https://openalex.org/W4399095504","https://openalex.org/W4399095480","https://openalex.org/W4386918840","https://openalex.org/W3125097393","https://openalex.org/W2091733721","https://openalex.org/W2086667681"],"abstract_inverted_index":{"We":[0],"propose":[1,84],"a":[2,8,14,23,150,166],"non-learning":[3],"depth":[4,10,31,43,72,94,113,131,143,168,172,184,197,224,232],"completion":[5,32,233],"method":[6,188,221],"for":[7,96],"sparse":[9,42],"map":[11,44,169],"captured":[12],"using":[13],"light":[15],"detection":[16],"and":[17,202],"ranging":[18],"(LiDAR)":[19],"sensor":[20],"guided":[21],"by":[22,75],"pair":[24],"of":[25,120,129,195],"stereo":[26,86],"images.":[27],"Generally,":[28],"conventional":[29],"stereo-aided":[30,183],"methods":[33],"have":[34,63],"two":[35],"limiations.":[36],"(i)":[37],"They":[38,62],"assume":[39],"the":[40,49,53,67,71,80,91,137,142,146,180,186,190,196,210,219,223],"given":[41],"is":[45,55,73,149],"accurately":[46],"aligned":[47],"to":[48,57,164,199,227],"input":[50],"image,":[51],"whereas":[52],"alignment":[54],"difficult":[56],"achieve":[58],"in":[59,66,127,209],"practice.":[60],"(ii)":[61],"limited":[64],"accuracy":[65,132],"long":[68,211],"range":[69],"because":[70,133],"estimated":[74],"pixel":[76,99],"disparity.":[77],"To":[78],"solve":[79],"abovementioned":[81],"limitations,":[82],"we":[83,154],"selective":[85],"matching":[87],"(SSM)":[88],"that":[89],"searches":[90],"most":[92],"appropriate":[93],"value":[95],"each":[97],"image":[98],"from":[100,145,230],"its":[101],"neighborly":[102],"projected":[103],"LiDAR":[104,138],"points":[105],"based":[106],"on":[107],"an":[108,125],"energy":[109],"minimization":[110],"framework.":[111],"This":[112],"selection":[114],"approach":[115],"can":[116],"handle":[117],"any":[118],"type":[119],"mis-projection.":[121],"Moreover,":[122,213],"SSM":[123,148],"has":[124],"advantage":[126],"terms":[128],"long-range":[130],"it":[134],"directly":[135],"uses":[136],"measurement":[139],"rather":[140],"than":[141],"acquired":[144],"stereo.":[147],"discrete":[151],"process;":[152],"thus,":[153],"apply":[155],"variational":[156],"smoothing":[157],"with":[158,179],"binary":[159],"anisotropic":[160],"diffusion":[161],"tensor":[162],"(B-ADT)":[163],"generate":[165],"continuous":[167],"while":[170],"preserving":[171],"discontinuity":[173],"across":[174],"object":[175],"boundaries.":[176],"Experimentally,":[177],"compared":[178],"previous":[181,231],"state-of-the-art":[182],"completion,":[185],"proposed":[187,220],"reduced":[189,222],"mean":[191],"absolute":[192],"error":[193],"(MAE)":[194],"estimation":[198,208,225],"0.65":[200],"times":[201,229],"demonstrated":[203],"approximately":[204],"twice":[205],"more":[206],"accurate":[207],"range.":[212],"under":[214],"various":[215],"LiDAR-camera":[216],"calibration":[217],"errors,":[218],"MAE":[226],"0.34-0.93":[228],"methods.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
