{"id":"https://openalex.org/W4283322684","doi":"https://doi.org/10.1109/icip46576.2022.9897772","title":"Enhancing Multi-View Stereo with Contrastive Matching and Weighted Focal Loss","display_name":"Enhancing Multi-View Stereo with Contrastive Matching and Weighted Focal Loss","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4283322684","doi":"https://doi.org/10.1109/icip46576.2022.9897772"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897772","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5013886309","display_name":"Yikang Ding","orcid":"https://orcid.org/0000-0001-6916-9371"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yikang Ding","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School","Shenzhen International Graduate School, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101409888","display_name":"Zhenyang Li","orcid":"https://orcid.org/0000-0002-4688-8354"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyang Li","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School","Shenzhen International Graduate School, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018139146","display_name":"Dihe Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dihe Huang","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School","Shenzhen International Graduate School, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765837","display_name":"Zhiheng Li","orcid":"https://orcid.org/0000-0002-1523-1114"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiheng Li","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School","Shenzhen International Graduate School, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100324056","display_name":"Kai Zhang","orcid":"https://orcid.org/0000-0003-3850-5429"},"institutions":[{"id":"https://openalex.org/I1320351128","display_name":"Delta Air Lines (United States)","ror":"https://ror.org/03g9c1e75","country_code":"US","type":"company","lineage":["https://openalex.org/I1320351128"]},{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I4210121989","display_name":"Pearl River Hydraulic Research Institute","ror":"https://ror.org/036g8gs02","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210121989"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Kai Zhang","raw_affiliation_strings":["Tsinghua University,Shenzhen International Graduate School","Pearl River Delta, Research Institute of Tsinghua","Shenzhen International Graduate School, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Shenzhen International Graduate School","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Pearl River Delta, Research Institute of Tsinghua","institution_ids":["https://openalex.org/I1320351128","https://openalex.org/I4210121989"]},{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3698,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.66110761,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"821","last_page":"825"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.996999979019165,"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":0.9955000281333923,"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/matching","display_name":"Matching (statistics)","score":0.7383604049682617},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.697347104549408},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6329001188278198},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.555812656879425},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5057888031005859},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.48062795400619507},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.4771430194377899},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4696860611438751},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.451141357421875},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.444646954536438},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4210895895957947},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33626997470855713},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20292463898658752},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17711704969406128},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10625863075256348}],"concepts":[{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.7383604049682617},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.697347104549408},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6329001188278198},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.555812656879425},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5057888031005859},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.48062795400619507},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4771430194377899},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4696860611438751},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.451141357421875},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.444646954536438},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4210895895957947},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33626997470855713},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20292463898658752},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17711704969406128},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10625863075256348},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897772","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2338968644","https://openalex.org/W2519683295","https://openalex.org/W2565639579","https://openalex.org/W2738551266","https://openalex.org/W2926429807","https://openalex.org/W2962793285","https://openalex.org/W2967693513","https://openalex.org/W2982169158","https://openalex.org/W3009561768","https://openalex.org/W3034530552","https://openalex.org/W3034564916","https://openalex.org/W3034600477","https://openalex.org/W3035257660","https://openalex.org/W3035477536","https://openalex.org/W3097305369","https://openalex.org/W3127683460","https://openalex.org/W3170262190","https://openalex.org/W3204267695","https://openalex.org/W3204354488","https://openalex.org/W4206424847","https://openalex.org/W4281748179","https://openalex.org/W4306157941","https://openalex.org/W4312253793","https://openalex.org/W6774314701","https://openalex.org/W6774670964","https://openalex.org/W6838080340"],"related_works":["https://openalex.org/W2069592018","https://openalex.org/W2075740387","https://openalex.org/W3135697610","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W2358990940","https://openalex.org/W1647606319","https://openalex.org/W2922442631","https://openalex.org/W2093931120"],"abstract_inverted_index":{"Learning-based":[0],"multi-view":[1],"stereo":[2],"(MVS)":[3],"methods":[4,12],"have":[5],"made":[6],"impressive":[7],"progress":[8],"and":[9,19,43,65,71,117,119,128],"surpassed":[10],"traditional":[11],"in":[13,60,101],"recent":[14],"years.":[15],"However,":[16],"their":[17],"accuracy":[18],"completeness":[20],"are":[21],"still":[22],"struggling.":[23],"In":[24],"this":[25],"paper,":[26],"we":[27,47],"propose":[28,48,84],"a":[29,49,85],"new":[30],"method":[31,124],"to":[32,104,108],"enhance":[33],"the":[34,56,73,78,96,105],"performance":[35,127],"of":[36,80,98],"existing":[37],"networks":[38],"inspired":[39],"by":[40],"contrastive":[41,74],"learning":[42],"feature":[44],"matching.":[45],"First,":[46],"Contrast":[50],"Matching":[51],"Loss":[52,88],"(CML),":[53],"which":[54,94],"treats":[55],"correct":[57],"matching":[58],"points":[59,67],"depth-dimension":[61],"as":[62,68],"positive":[63],"sample":[64],"other":[66],"negative":[69],"samples,":[70],"computes":[72],"loss":[75,106],"based":[76],"on":[77,114],"similarity":[79],"features.":[81],"We":[82],"further":[83],"Weighted":[86],"Focal":[87],"(WFL)":[89],"for":[90],"better":[91],"classification":[92],"capability,":[93],"weakens":[95],"contribution":[97],"low-confidence":[99],"pixels":[100],"unimportant":[102],"areas":[103],"according":[107],"predicted":[109],"confidence.":[110],"Extensive":[111],"experiments":[112],"performed":[113],"DTU,":[115],"Tanks":[116],"Temples":[118],"BlendedMVS":[120],"datasets":[121],"show":[122],"our":[123],"achieves":[125],"state-of-the-art":[126],"significant":[129],"improvement":[130],"over":[131],"baseline":[132],"network.":[133]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}