{"id":"https://openalex.org/W2609550078","doi":"https://doi.org/10.1109/icpr.2016.7899985","title":"Geometric verification using semi-2D constraints for 3D object retrieval","display_name":"Geometric verification using semi-2D constraints for 3D object retrieval","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2609550078","doi":"https://doi.org/10.1109/icpr.2016.7899985","mag":"2609550078"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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/A5101805486","display_name":"Kohei Matsuzaki","orcid":"https://orcid.org/0000-0001-9386-2192"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kohei Matsuzaki","raw_affiliation_strings":["KDDI R&D Laboratories, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI R&D Laboratories, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068788899","display_name":"Yusuke Uchida","orcid":"https://orcid.org/0000-0002-6932-1465"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]},{"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":"Yusuke Uchida","raw_affiliation_strings":["KDDI R&D Laboratories, Inc., Saitama, Japan","University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI R&D Laboratories, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]},{"raw_affiliation_string":"University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068660571","display_name":"Shigeyuki Sakazawa","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeyuki Sakazawa","raw_affiliation_strings":["KDDI R&D Laboratories, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI R&D Laboratories, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103552031","display_name":"Shin\u2010Ichi Sato","orcid":null},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shin'ichi Sato","raw_affiliation_strings":["National Institute of Informatics, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Informatics, Tokyo, Japan","institution_ids":["https://openalex.org/I184597095"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.338,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.69954581,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"27","issue":null,"first_page":"2338","last_page":"2343"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998000264167786,"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":0.9984999895095825,"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"}},{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9976000189781189,"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":0.9819554090499878},{"id":"https://openalex.org/keywords/ransac","display_name":"RANSAC","score":0.8002848625183105},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.6242964267730713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6160668134689331},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6064741015434265},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5117978453636169},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.4974038898944855},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4784289002418518},{"id":"https://openalex.org/keywords/geometric-modeling","display_name":"Geometric modeling","score":0.47003409266471863},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46542543172836304},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4519592523574829},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44660502672195435},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.15107840299606323}],"concepts":[{"id":"https://openalex.org/C23379248","wikidata":"https://www.wikidata.org/wiki/Q200904","display_name":"Epipolar geometry","level":3,"score":0.9819554090499878},{"id":"https://openalex.org/C114744707","wikidata":"https://www.wikidata.org/wiki/Q218533","display_name":"RANSAC","level":3,"score":0.8002848625183105},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.6242964267730713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6160668134689331},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6064741015434265},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5117978453636169},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.4974038898944855},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4784289002418518},{"id":"https://openalex.org/C104065381","wikidata":"https://www.wikidata.org/wiki/Q1002535","display_name":"Geometric modeling","level":2,"score":0.47003409266471863},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46542543172836304},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4519592523574829},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44660502672195435},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.15107840299606323},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2016.7899985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1556531089","https://openalex.org/W1575387439","https://openalex.org/W2085261163","https://openalex.org/W2099927035","https://openalex.org/W2106199912","https://openalex.org/W2109635530","https://openalex.org/W2117228865","https://openalex.org/W2130017587","https://openalex.org/W2131846894","https://openalex.org/W2138620157","https://openalex.org/W2141362318","https://openalex.org/W2151103935","https://openalex.org/W2162137128","https://openalex.org/W2165584048","https://openalex.org/W2200154090","https://openalex.org/W2533007775","https://openalex.org/W3021282624","https://openalex.org/W6679467917","https://openalex.org/W7033090389","https://openalex.org/W7046283970"],"related_works":["https://openalex.org/W2082446201","https://openalex.org/W2111113322","https://openalex.org/W4312286391","https://openalex.org/W2113845741","https://openalex.org/W2737053732","https://openalex.org/W2362714107","https://openalex.org/W4251504644","https://openalex.org/W2099927035","https://openalex.org/W2532401163","https://openalex.org/W2139770562"],"abstract_inverted_index":{"Geometric":[0],"verification":[1,154],"with":[2],"epipolar":[3,35],"geometry":[4,36],"often":[5],"results":[6],"in":[7,19,33,71],"a":[8,28,38,45,48,65,67,72,157],"high":[9,29],"score":[10],"for":[11,151],"an":[12,98,109,162],"incorrect":[13,163],"image":[14,110,159,164],"pair":[15,111,160],"due":[16],"to":[17,52,58],"ambiguity":[18,24],"its":[20],"geometric":[21,74,85,153],"constraints.":[22],"The":[23],"is":[25,87,104,144],"caused":[26],"by":[27],"degree":[30],"of":[31,100,113,155],"freedom":[32],"the":[34,42,54,77,101,122,131,135,140,152],"and":[37,47,69,146,161],"weak":[39],"constraint":[40,86],"from":[41],"fitting":[43],"between":[44],"point":[46],"line.":[49],"In":[50],"order":[51],"mitigate":[53],"ambiguity,":[55],"we":[56,79],"propose":[57],"filter":[59],"geometrically":[60],"inconsistent":[61],"components,":[62],"namely":[63],"correspondences,":[64],"sample,":[66],"model,":[68],"inliers":[70],"RANSAC-based":[73],"verification.":[75],"For":[76],"filtering,":[78],"introduce":[80],"novel":[81],"semi-2D":[82],"constraints":[83],"whose":[84],"weaker":[88],"than":[89,94],"full-2D":[90],"constraint,":[91],"but":[92],"stronger":[93],"pure-epipolar":[95],"constraint.":[96],"Additionally,":[97],"advantage":[99],"proposed":[102,132],"approach":[103,133],"that":[105,130],"it":[106],"requires":[107],"only":[108],"instead":[112],"neither":[114],"additional":[115],"information":[116],"nor":[117],"prior":[118],"learning.":[119],"Experiments":[120],"on":[121],"public":[123],"dataset":[124],"containing":[125],"3D":[126],"object":[127],"images":[128],"show":[129],"improves":[134],"true":[136],"positive":[137,142],"rate":[138,143],"when":[139],"false":[141],"low,":[145],"greatly":[147],"reduces":[148],"computational":[149],"time":[150],"both":[156],"correct":[158],"pair.":[165]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
