{"id":"https://openalex.org/W3091410168","doi":"https://doi.org/10.1109/icra40945.2020.9197333","title":"High Accuracy and Efficiency Grasp Pose Detection Scheme with Dense Predictions","display_name":"High Accuracy and Efficiency Grasp Pose Detection Scheme with Dense Predictions","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3091410168","doi":"https://doi.org/10.1109/icra40945.2020.9197333","mag":"3091410168"},"language":"en","primary_location":{"id":"doi:10.1109/icra40945.2020.9197333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9197333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","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/A5080724728","display_name":"Hu Cheng","orcid":"https://orcid.org/0000-0002-2090-1362"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hu Cheng","raw_affiliation_strings":["Robotics, Perception and Artificial Intelligence Laboratory, The Chinese University of Hong Kong, N.T. Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Robotics, Perception and Artificial Intelligence Laboratory, The Chinese University of Hong Kong, N.T. Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072824702","display_name":"Danny Ho","orcid":"https://orcid.org/0000-0002-3748-6345"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danny Ho","raw_affiliation_strings":["Robotics, Perception and Artificial Intelligence Laboratory, The Chinese University of Hong Kong, N.T. Hong Kong SAR, China"],"affiliations":[{"raw_affiliation_string":"Robotics, Perception and Artificial Intelligence Laboratory, The Chinese University of Hong Kong, N.T. Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021531143","display_name":"Max Q.\u2010H. Meng","orcid":"https://orcid.org/0000-0002-5255-5898"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Max Q.-H. Meng","raw_affiliation_strings":["Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5080724728"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":1.7654,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.84822209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3604","last_page":"3610"},"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":0.9916999936103821,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9657999873161316,"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":0.9635053873062134},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7850183844566345},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7801798582077026},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6829705238342285},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5963312387466431},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5849990248680115},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5341612100601196},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4908769726753235},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.4354771375656128}],"concepts":[{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.9635053873062134},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7850183844566345},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7801798582077026},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6829705238342285},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5963312387466431},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5849990248680115},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5341612100601196},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4908769726753235},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.4354771375656128},{"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/icra40945.2020.9197333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9197333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","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":43,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1892339738","https://openalex.org/W1978131245","https://openalex.org/W1984237968","https://openalex.org/W1999156278","https://openalex.org/W2041376653","https://openalex.org/W2080279876","https://openalex.org/W2089630413","https://openalex.org/W2089806275","https://openalex.org/W2123435073","https://openalex.org/W2130732670","https://openalex.org/W2163212345","https://openalex.org/W2194775991","https://openalex.org/W2207670488","https://openalex.org/W2221752211","https://openalex.org/W2341294985","https://openalex.org/W2414685554","https://openalex.org/W2415335881","https://openalex.org/W2415378145","https://openalex.org/W2572996265","https://openalex.org/W2587007321","https://openalex.org/W2600030077","https://openalex.org/W2605982830","https://openalex.org/W2738156096","https://openalex.org/W2805051407","https://openalex.org/W2808521881","https://openalex.org/W2824754393","https://openalex.org/W2898913080","https://openalex.org/W2922340928","https://openalex.org/W2962737955","https://openalex.org/W2962914239","https://openalex.org/W2963033241","https://openalex.org/W2963037989","https://openalex.org/W2963326767","https://openalex.org/W2963678509","https://openalex.org/W2963849966","https://openalex.org/W2963956866","https://openalex.org/W2990747716","https://openalex.org/W3098609708","https://openalex.org/W3121991631","https://openalex.org/W6639569854","https://openalex.org/W6715972168","https://openalex.org/W6770858630"],"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/W3021551196"],"abstract_inverted_index":{"Learning-based":[0],"grasp":[1,41,47,64,99,150],"pose":[2,48,65,151],"detection":[3,24,49,117],"algorithms":[4],"have":[5],"boosted":[6],"the":[7,21,54,58,62,71,74,94,103,112,122,135,143,190],"performance":[8],"of":[9,70,115,137,165],"robot":[10,177],"grasping,":[11],"but":[12],"they":[13],"usually":[14],"need":[15],"manually":[16],"fine-tuning":[17],"steps":[18],"to":[19],"find":[20],"balance":[22],"between":[23],"accuracy":[25,159],"and":[26,39,43,60,86,106,175],"efficient.":[27,107],"In":[28],"this":[29],"paper,":[30],"we":[31],"discard":[32],"these":[33],"intermediate":[34],"procedures,":[35],"like":[36],"sampling":[37],"grasps":[38,76,114],"generating":[40],"proposals,":[42],"propose":[44],"an":[45],"end-to-end":[46],"model.":[50],"Our":[51],"model":[52,118,141,156],"uses":[53],"RGB":[55,169],"image":[56,170],"as":[57],"input":[59],"predicts":[61],"single":[63],"in":[66,152,171,189],"each":[67,127],"small":[68],"grid":[69],"image.":[72],"Furthermore,":[73],"best":[75,136],"are":[77,89,119],"found":[78],"by":[79,121],"non-maximum":[80],"suppression":[81],"(NMS)":[82],"strategy.":[83],"The":[84,155],"clustering":[85],"ranking":[87],"procedures":[88],"left":[90],"for":[91,163,181],"NMS":[92],"while":[93],"network":[95,104,146],"only":[96],"generates":[97],"dense":[98,110],"predictions,":[100,111],"which":[101,160],"keeps":[102],"simple":[105],"To":[108,134],"achieve":[109],"predicted":[113],"our":[116,138,140],"represented":[120],"6":[123],"channels":[124],"images":[125],"with":[126,184],"pixel":[128,153],"location":[129],"representing":[130],"a":[131,149,166,185],"rated":[132],"grasp.":[133],"knowledge,":[139],"is":[142],"first":[144],"neural":[145],"that":[147],"attaches":[148],"level.":[154],"achieves":[157],"96.5%":[158],"costs":[161],"14ms":[162],"prediction":[164],"480\u00d7360":[167],"resolution":[168],"Cornell":[172],"Grasp":[173],"Dataset,":[174],"90.4%":[176],"grasping":[178],"success":[179],"rate":[180],"unknown":[182],"objects":[183],"parallel":[186],"plate":[187],"gripper":[188],"real":[191],"environment.":[192]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
