{"id":"https://openalex.org/W3195763721","doi":"https://doi.org/10.1109/icip42928.2021.9506469","title":"M3VSNET: Unsupervised Multi-Metric Multi-View Stereo Network","display_name":"M3VSNET: Unsupervised Multi-Metric Multi-View Stereo Network","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3195763721","doi":"https://doi.org/10.1109/icip42928.2021.9506469","mag":"3195763721"},"language":"en","primary_location":{"id":"doi:10.1109/icip42928.2021.9506469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101611516","display_name":"Baichuan Huang","orcid":"https://orcid.org/0000-0003-4010-8545"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210109870","display_name":"Vi Technology (United States)","ror":"https://ror.org/016mnbp44","country_code":"US","type":"company","lineage":["https://openalex.org/I4210109870"]},{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Baichuan Huang","raw_affiliation_strings":["Wuhan University","Megvii Technology Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"Megvii Technology Limited","institution_ids":["https://openalex.org/I4210109870","https://openalex.org/I4401726805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102758362","display_name":"Hongwei Yi","orcid":"https://orcid.org/0000-0001-8669-2009"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Yi","raw_affiliation_strings":["Peking University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077909510","display_name":"Can Huang","orcid":"https://orcid.org/0000-0002-3675-6585"},"institutions":[{"id":"https://openalex.org/I4210109870","display_name":"Vi Technology (United States)","ror":"https://ror.org/016mnbp44","country_code":"US","type":"company","lineage":["https://openalex.org/I4210109870"]},{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Can Huang","raw_affiliation_strings":["Megvii Technology Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Megvii Technology Limited","institution_ids":["https://openalex.org/I4210109870","https://openalex.org/I4401726805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029738461","display_name":"Yijia He","orcid":"https://orcid.org/0000-0003-4358-7393"},"institutions":[{"id":"https://openalex.org/I4210109870","display_name":"Vi Technology (United States)","ror":"https://ror.org/016mnbp44","country_code":"US","type":"company","lineage":["https://openalex.org/I4210109870"]},{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yijia He","raw_affiliation_strings":["Megvii Technology Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Megvii Technology Limited","institution_ids":["https://openalex.org/I4210109870","https://openalex.org/I4401726805"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035970188","display_name":"Jingbin Liu","orcid":"https://orcid.org/0000-0001-6216-0956"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingbin Liu","raw_affiliation_strings":["Wuhan University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075354330","display_name":"Xiao Liu","orcid":"https://orcid.org/0000-0002-9163-0280"},"institutions":[{"id":"https://openalex.org/I4210109870","display_name":"Vi Technology (United States)","ror":"https://ror.org/016mnbp44","country_code":"US","type":"company","lineage":["https://openalex.org/I4210109870"]},{"id":"https://openalex.org/I4401726805","display_name":"Megvii (China)","ror":"https://ror.org/040b32p69","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726805"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiao Liu","raw_affiliation_strings":["Megvii Technology Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Megvii Technology Limited","institution_ids":["https://openalex.org/I4210109870","https://openalex.org/I4401726805"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":67,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3163","last_page":"3167"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9976999759674072,"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":0.9886000156402588,"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/point-cloud","display_name":"Point cloud","score":0.7046031951904297},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6836061477661133},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6492077112197876},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6155180931091309},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5940295457839966},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5777689814567566},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4975781738758087},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.49338117241859436},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4630814492702484},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44623783230781555},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.41298553347587585},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3450753092765808},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21377071738243103}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7046031951904297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6836061477661133},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6492077112197876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6155180931091309},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5940295457839966},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5777689814567566},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4975781738758087},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.49338117241859436},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4630814492702484},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44623783230781555},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41298553347587585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3450753092765808},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21377071738243103},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip42928.2021.9506469","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506469","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1539230104","https://openalex.org/W1993120651","https://openalex.org/W2064381770","https://openalex.org/W2085905957","https://openalex.org/W2099443716","https://openalex.org/W2117841635","https://openalex.org/W2128052895","https://openalex.org/W2129404737","https://openalex.org/W2137341062","https://openalex.org/W2156598602","https://openalex.org/W2160014001","https://openalex.org/W2163309730","https://openalex.org/W2205172244","https://openalex.org/W2331128040","https://openalex.org/W2338968644","https://openalex.org/W2464339273","https://openalex.org/W2519683295","https://openalex.org/W2561074213","https://openalex.org/W2565639579","https://openalex.org/W2609883120","https://openalex.org/W2738551266","https://openalex.org/W2745144057","https://openalex.org/W2753893487","https://openalex.org/W2788857104","https://openalex.org/W2808629189","https://openalex.org/W2903640603","https://openalex.org/W2926429807","https://openalex.org/W2943956032","https://openalex.org/W2952181039","https://openalex.org/W2962793285","https://openalex.org/W2963906250","https://openalex.org/W2970971581","https://openalex.org/W2982336381","https://openalex.org/W2992718396","https://openalex.org/W3011520397","https://openalex.org/W3034530552","https://openalex.org/W3102132650","https://openalex.org/W3108346482","https://openalex.org/W4249710618","https://openalex.org/W4295312788","https://openalex.org/W6736677999","https://openalex.org/W6761815369","https://openalex.org/W6764637314","https://openalex.org/W6771702536","https://openalex.org/W6772098959"],"related_works":["https://openalex.org/W2047973478","https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W3016928466","https://openalex.org/W4389574804","https://openalex.org/W2067569035","https://openalex.org/W2090985514","https://openalex.org/W2371138613","https://openalex.org/W2562256921","https://openalex.org/W2075445622"],"abstract_inverted_index":{"The":[0],"present":[1],"Multi-view":[2],"stereo":[3],"(MVS)":[4],"methods":[5],"with":[6,15,160],"supervised":[7,142],"learning-based":[8],"networks":[9],"have":[10],"an":[11],"impressive":[12],"performance":[13,139],"comparing":[14],"traditional":[16],"MVS":[17,47],"methods.":[18],"However,":[19],"the":[20,65,89,102,106,113,118,132,145,150,155],"ground-truth":[21],"depth":[22,120],"maps":[23],"for":[24,55],"training":[25],"are":[26,32],"hard":[27],"to":[28,87,111],"be":[29],"obtained":[30],"and":[31,67,83,115,136,148],"within":[33],"limited":[34],"kinds":[35],"of":[36,69,95,117],"scenarios.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41,73,99],"propose":[42,74],"a":[43,75],"novel":[44,76],"unsupervised":[45,134],"multi-metric":[46,77],"network,":[48],"named":[49],"M":[50,126],"<sup":[51,127],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[52,128],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</sup>":[53,129],"VSNet,":[54],"dense":[56],"point":[57,70,108],"cloud":[58,71,109],"reconstruction":[59],"without":[60],"any":[61],"supervision.":[62],"To":[63],"improve":[64,112],"robustness":[66],"completeness":[68],"reconstruction,":[72],"loss":[78,85],"function":[79,86],"that":[80,125],"combines":[81],"pixel-wise":[82],"feature-wise":[84],"learn":[88],"inherent":[90],"constraints":[91],"from":[92],"different":[93],"perspectives":[94],"matching":[96],"correspondences.":[97],"Besides,":[98],"also":[100],"incorporate":[101],"normal-depth":[103],"consistency":[104],"in":[105],"3D":[107],"format":[110],"accuracy":[114],"continuity":[116],"estimated":[119],"maps.":[121],"Experimental":[122],"results":[123],"show":[124],"VSNet":[130],"establishes":[131],"state-of-the-arts":[133],"method":[135],"achieves":[137],"better":[138],"than":[140],"previous":[141],"MVSNet":[143],"on":[144,154],"DTU":[146],"dataset":[147],"demonstrates":[149],"powerful":[151],"generalization":[152],"ability":[153],"Tanks":[156],"&":[157],"Temples":[158],"benchmark":[159],"effective":[161],"improvement.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}