{"id":"https://openalex.org/W2169980043","doi":"https://doi.org/10.1109/cvpr.2009.5206712","title":"Continuous depth estimation for multi-view stereo","display_name":"Continuous depth estimation for multi-view stereo","publication_year":2009,"publication_date":"2009-06-01","ids":{"openalex":"https://openalex.org/W2169980043","doi":"https://doi.org/10.1109/cvpr.2009.5206712","mag":"2169980043"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2009.5206712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2009.5206712","pdf_url":null,"source":{"id":"https://openalex.org/S4363607795","display_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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/A5032875389","display_name":"Yebin Liu","orcid":"https://orcid.org/0000-0003-3215-0225"},"institutions":[{"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":true,"raw_author_name":"Yebin Liu","raw_affiliation_strings":["Automation Department, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Automation Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058572381","display_name":"Xun Cao","orcid":"https://orcid.org/0000-0003-3094-4371"},"institutions":[{"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":"Xun Cao","raw_affiliation_strings":["Automation Department, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Automation Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080722708","display_name":"Qionghai Dai","orcid":"https://orcid.org/0000-0001-7043-3061"},"institutions":[{"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":"Qionghai Dai","raw_affiliation_strings":["Automation Department, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Automation Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100294547","display_name":"Wenli Xu","orcid":"https://orcid.org/0009-0006-9580-6710"},"institutions":[{"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":"Wenli Xu","raw_affiliation_strings":["Automation Department, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Automation Department, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032875389"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":2.8598,"has_fulltext":false,"cited_by_count":88,"citation_normalized_percentile":{"value":0.92998205,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2121","last_page":"2128"},"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":0.9962000250816345,"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":0.9958999752998352,"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/epipolar-geometry","display_name":"Epipolar geometry","score":0.8267780542373657},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.76961350440979},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7279871702194214},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7031717896461487},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6435415148735046},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5951035618782043},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49344196915626526},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4608864486217499},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4303207993507385},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3242149353027344},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2540762424468994},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21272289752960205},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10536450147628784}],"concepts":[{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.8267780542373657},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.76961350440979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7279871702194214},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7031717896461487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6435415148735046},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5951035618782043},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49344196915626526},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4608864486217499},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4303207993507385},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3242149353027344},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2540762424468994},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21272289752960205},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10536450147628784},{"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/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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2009.5206712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2009.5206712","pdf_url":null,"source":{"id":"https://openalex.org/S4363607795","display_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","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":"2009 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.352.4524","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.4524","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://media.au.tsinghua.edu.cn/cmvs/CVPR09-LiuYebin.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1536187505","https://openalex.org/W1539230104","https://openalex.org/W1553035673","https://openalex.org/W1587330868","https://openalex.org/W1633397821","https://openalex.org/W1781337833","https://openalex.org/W1853039947","https://openalex.org/W1867429401","https://openalex.org/W2008073424","https://openalex.org/W2034869517","https://openalex.org/W2042418341","https://openalex.org/W2064381770","https://openalex.org/W2104974755","https://openalex.org/W2107884096","https://openalex.org/W2117331827","https://openalex.org/W2117888987","https://openalex.org/W2119213281","https://openalex.org/W2119454625","https://openalex.org/W2129404737","https://openalex.org/W2132055825","https://openalex.org/W2137341062","https://openalex.org/W2147253850","https://openalex.org/W2152770403","https://openalex.org/W2156116778","https://openalex.org/W2160014001","https://openalex.org/W2167085613","https://openalex.org/W2167141871","https://openalex.org/W2167335287","https://openalex.org/W2171056981","https://openalex.org/W2583704680","https://openalex.org/W6632045966","https://openalex.org/W6634989299","https://openalex.org/W6637048030","https://openalex.org/W6638137768","https://openalex.org/W6638868286","https://openalex.org/W6652231383","https://openalex.org/W6677138286","https://openalex.org/W7010633129"],"related_works":["https://openalex.org/W132764016","https://openalex.org/W2148054235","https://openalex.org/W2132043085","https://openalex.org/W4251504644","https://openalex.org/W1721537561","https://openalex.org/W2369285629","https://openalex.org/W1992677668","https://openalex.org/W1482441085","https://openalex.org/W2966858528","https://openalex.org/W2151687600"],"abstract_inverted_index":{"Depth-map":[0],"merging":[1,27],"approaches":[2],"have":[3],"become":[4],"more":[5,7],"and":[6,17,62,124,152],"popular":[8],"in":[9,42,154],"multi-view":[10],"stereo":[11],"(MVS)":[12],"because":[13],"of":[14,22,50,127],"their":[15],"flexibility":[16],"superior":[18],"performance.":[19],"The":[20],"quality":[21],"depth":[23,36,71,76,92,110],"map":[24,37,72],"used":[25],"for":[26,30,69,94,136],"is":[28],"vital":[29],"accurate":[31,130],"3D":[32,116],"reconstruction.":[33],"While":[34],"traditional":[35],"estimation":[38],"has":[39],"been":[40],"performed":[41],"a":[43,51,82],"discrete":[44],"manner,":[45],"we":[46,57],"suggest":[47],"the":[48,66,108,128,133,142],"use":[49],"continuous":[52,70],"counterpart.":[53],"In":[54],"this":[55],"paper,":[56],"first":[58],"integrate":[59],"silhouette":[60],"information":[61],"epipolar":[63],"constraint":[64],"into":[65],"variational":[67],"method":[68],"estimation.":[73],"Then,":[74],"several":[75],"candidates":[77],"are":[78,97,112],"generated":[79],"based":[80],"on":[81],"multiple":[83],"starting":[84],"scales":[85],"(MSS)":[86],"framework.":[87],"From":[88],"these":[89],"candidates,":[90],"refined":[91],"maps":[93,111],"each":[95],"view":[96],"synthesized":[98],"according":[99,140],"to":[100,114,141],"path-based":[101],"NCC":[102],"(normalized":[103],"cross":[104],"correlation)":[105],"metric.":[106],"Finally,":[107],"multiview":[109],"merged":[113],"produce":[115],"models.":[117],"Our":[118],"algorithm":[119],"excels":[120],"at":[121],"detail":[122],"capture":[123],"produces":[125],"one":[126],"most":[129],"results":[131],"among":[132],"current":[134],"algorithms":[135],"sparse":[137],"MVS":[138],"datasets":[139],"Middlebury":[143],"benchmark.":[144],"Additionally,":[145],"our":[146],"approach":[147],"shows":[148],"its":[149],"outstanding":[150],"robustness":[151],"accuracy":[153],"free-viewpoint":[155],"video":[156],"scenario.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":9}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
