{"id":"https://openalex.org/W2525904748","doi":"https://doi.org/10.1109/icmew.2016.7574711","title":"A hybrid metric for camera pose estimation in RGB-D reconstruction","display_name":"A hybrid metric for camera pose estimation in RGB-D reconstruction","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2525904748","doi":"https://doi.org/10.1109/icmew.2016.7574711","mag":"2525904748"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2016.7574711","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2016.7574711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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/A5091149931","display_name":"Fei Guo","orcid":"https://orcid.org/0000-0001-8845-4995"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fei Guo","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, CA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, CA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461963","display_name":"Yifeng He","orcid":"https://orcid.org/0000-0002-1700-3166"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yifeng He","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, CA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, CA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101405633","display_name":"Ling Guan","orcid":"https://orcid.org/0000-0002-3306-5551"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ling Guan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, CA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, CA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5091149931"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05182421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998000264167786,"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/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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.7705636024475098},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7467160820960999},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6590362191200256},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6446723937988281},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.587758481502533},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.49439382553100586},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.49311205744743347},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4806581735610962},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3971291780471802},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3608701825141907}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7705636024475098},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7467160820960999},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6590362191200256},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6446723937988281},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.587758481502533},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.49439382553100586},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.49311205744743347},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4806581735610962},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3971291780471802},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3608701825141907},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2016.7574711","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2016.7574711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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":14,"referenced_works":["https://openalex.org/W181493107","https://openalex.org/W612478963","https://openalex.org/W1987648924","https://openalex.org/W2021851106","https://openalex.org/W2035379092","https://openalex.org/W2058535340","https://openalex.org/W2118104180","https://openalex.org/W2130502925","https://openalex.org/W2130960731","https://openalex.org/W2152671441","https://openalex.org/W2159029220","https://openalex.org/W2168676389","https://openalex.org/W2168722300","https://openalex.org/W2243794092"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2123263858","https://openalex.org/W2046456988","https://openalex.org/W2357409937","https://openalex.org/W3135697610","https://openalex.org/W2510582230","https://openalex.org/W3127959533","https://openalex.org/W2978674666","https://openalex.org/W2074430941","https://openalex.org/W2085033728"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,105],"propose":[4,106],"a":[5,68,83,117],"hybrid":[6,63,93,103,132],"metric":[7,64,94,133,138,142],"for":[8,111,121],"measuring":[9],"the":[10,13,27,30,34,42,51,56,72,76,92,96,101,113,123,130,135,146],"accuracy":[11,147],"of":[12,26,85,145,148],"estimated":[14],"camera.":[15],"Existing":[16],"camera":[17,124,152],"pose":[18,114,118,153],"evaluation":[19],"methods":[20],"are":[21],"mainly":[22],"built":[23],"upon":[24],"one":[25],"two":[28,97],"metrics:":[29],"geometric":[31,38,73,136],"measurement":[32,74],"or":[33],"photometric":[35,52,77,140],"measurement.":[36,78],"The":[37,61,79],"based":[39,53,137,141],"method":[40,54],"inspects":[41],"relative":[43],"position":[44],"between":[45,82,95],"source":[46],"and":[47,75,116,139,151],"target":[48],"points,":[49],"while":[50],"utilizes":[55],"pixel":[57],"intensity":[58],"as":[59],"assessment.":[60],"proposed":[62,102,131],"is":[65],"represented":[66],"by":[67,90],"vector":[69],"containing":[70],"both":[71],"optimal":[80],"transform":[81],"pair":[84],"frames":[86],"can":[87],"be":[88],"found":[89],"minimizing":[91],"frames.":[98],"Based":[99],"on":[100],"metric,":[104],"an":[107],"outlier":[108,149],"removal":[109],"algorithm":[110,120],"finding":[112],"outliers,":[115],"estimation":[119],"estimating":[122],"pose.":[125],"Experimental":[126],"results":[127],"demonstrated":[128],"that":[129],"outperforms":[134],"in":[143],"terms":[144],"identification":[150],"estimation.":[154]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}