{"id":"https://openalex.org/W3189759214","doi":"https://doi.org/10.1109/ur52253.2021.9494670","title":"Depth Hole Filling based on Deep Learning for Robust Grasp Detection","display_name":"Depth Hole Filling based on Deep Learning for Robust Grasp Detection","publication_year":2021,"publication_date":"2021-07-12","ids":{"openalex":"https://openalex.org/W3189759214","doi":"https://doi.org/10.1109/ur52253.2021.9494670","mag":"3189759214"},"language":"en","primary_location":{"id":"doi:10.1109/ur52253.2021.9494670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ur52253.2021.9494670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Conference on Ubiquitous Robots (UR)","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/A5101953942","display_name":"Sungwon Seo","orcid":"https://orcid.org/0000-0001-7862-7936"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Sungwon Seo","raw_affiliation_strings":["Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039506859","display_name":"Luong Anh Tuan","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Luong Anh Tuan","raw_affiliation_strings":["Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026173109","display_name":"Eugene Auh","orcid":"https://orcid.org/0000-0001-9349-5046"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Eugene Auh","raw_affiliation_strings":["Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055372260","display_name":"Hyungpil Moon","orcid":"https://orcid.org/0000-0002-1091-0716"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyungpil Moon","raw_affiliation_strings":["Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Faculty of Mechanical Engineering, Sungkyunkwan University, Su-won, Republic of Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101953942"],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":0.412,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6062905,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"194","last_page":"197"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10653","display_name":"Robot Manipulation and Learning","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9793000221252441,"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/grasp","display_name":"GRASP","score":0.8327619433403015},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.8023921251296997},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7734273672103882},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7165048122406006},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6654062867164612},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6578258872032166},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6332155466079712},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5530873537063599},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46576976776123047},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45213478803634644},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4495495557785034},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2880828380584717},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09859868884086609}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.8327619433403015},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.8023921251296997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7734273672103882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7165048122406006},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6654062867164612},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6578258872032166},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6332155466079712},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5530873537063599},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46576976776123047},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45213478803634644},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4495495557785034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2880828380584717},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09859868884086609},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"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/ur52253.2021.9494670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ur52253.2021.9494670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 18th International Conference on Ubiquitous Robots (UR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1751162681","https://openalex.org/W1901129140","https://openalex.org/W2050449584","https://openalex.org/W2165736859","https://openalex.org/W2201912979","https://openalex.org/W2600030077","https://openalex.org/W2793146153","https://openalex.org/W2798365772","https://openalex.org/W2962746398","https://openalex.org/W2963326767","https://openalex.org/W2964204553","https://openalex.org/W2982470081","https://openalex.org/W4243385754","https://openalex.org/W6639824700","https://openalex.org/W6744542307","https://openalex.org/W6749271710"],"related_works":["https://openalex.org/W2163296013","https://openalex.org/W165915117","https://openalex.org/W2326995835","https://openalex.org/W2743859443","https://openalex.org/W2059402478","https://openalex.org/W2123347777","https://openalex.org/W4387804363","https://openalex.org/W2477150073","https://openalex.org/W2019547100","https://openalex.org/W3202440119"],"abstract_inverted_index":{"In":[0,19],"current":[1],"decades,":[2],"object":[3],"grasp":[4,132],"detection":[5,133],"of":[6,10,51,102,127],"a":[7,60,76,82],"diverse":[8],"range":[9],"novel":[11],"objects":[12],"using":[13,135,149],"vision":[14],"systems":[15],"has":[16],"been":[17],"developed.":[18],"order":[20],"to":[21,40,74,112],"achieve":[22],"full":[23],"performance,":[24],"it":[25],"requires":[26],"high-quality":[27],"depth":[28,32,37,64,69,79,85,92,104,110,121,141],"images.":[29],"However,":[30],"commodity":[31,109],"cameras":[33],"often":[34],"offer":[35],"invalid":[36],"pixels":[38],"due":[39],"dark,":[41],"shining":[42],"surfaces":[43],"and":[44,49],"edges":[45],"between":[46],"the":[47,52,89,99,103,108,114,120,124,139,146,150],"foreground":[48],"background":[50],"scene.":[53],"To":[54],"address":[55],"this":[56],"problem,":[57],"we":[58],"propose":[59],"deep":[61],"learning":[62],"based":[63],"hole":[65,70,105,122],"filling":[66,71],"method.":[67],"The":[68,116,131],"network":[72],"learns":[73],"predict":[75],"ground":[77,151],"truth":[78,152],"map":[80],"for":[81],"given":[83],"sparse":[84,91,140],"map.":[86],"We":[87],"generate":[88],"artificial":[90],"images":[93],"from":[94],"Dex-Net":[95],"2.0":[96],"by":[97],"simulating":[98],"common":[100],"situation":[101],"generation":[106],"in":[107],"camera":[111],"train":[113],"network.":[115],"proposed":[117],"model":[118,137],"fills":[119],"with":[123,138,145],"RMSE":[125],"value":[126],"7.1":[128],"\u00b1":[129],"4.1mm.":[130],"performance":[134,147],"our":[136],"image":[142],"is":[143],"comparable":[144],"when":[148],"image.":[153]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
