{"id":"https://openalex.org/W3116788653","doi":"https://doi.org/10.1109/icess49830.2020.9301562","title":"3D Hand Pose Estimation from Single Depth Images with Label Distribution Learning","display_name":"3D Hand Pose Estimation from Single Depth Images with Label Distribution Learning","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3116788653","doi":"https://doi.org/10.1109/icess49830.2020.9301562","mag":"3116788653"},"language":"en","primary_location":{"id":"doi:10.1109/icess49830.2020.9301562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icess49830.2020.9301562","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 Embedded Software and Systems (ICESS)","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/A5102210039","display_name":"Yuanfei Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuanfei Xu","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science And Technology, Mianyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science And Technology, Mianyang, China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018927991","display_name":"Xupeng Wang","orcid":"https://orcid.org/0000-0002-4160-8552"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xupeng Wang","raw_affiliation_strings":["School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102210039"],"corresponding_institution_ids":["https://openalex.org/I1297991670"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.43044633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"abs 1609 9698","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","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/T10812","display_name":"Human Pose and Action Recognition","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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9990000128746033,"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/T12290","display_name":"Human Motion and Animation","score":0.9876999855041504,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7876805663108826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7786706686019897},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7699562311172485},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.611644983291626},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5801331996917725},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5258772373199463},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5169726610183716},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5164383053779602},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5046719312667847},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4842779040336609},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4842221736907959},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4655706584453583},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4562171697616577},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.41167089343070984},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3749614357948303},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12852385640144348}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7876805663108826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7786706686019897},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7699562311172485},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.611644983291626},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5801331996917725},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5258772373199463},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5169726610183716},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5164383053779602},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5046719312667847},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4842779040336609},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4842221736907959},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4655706584453583},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4562171697616577},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.41167089343070984},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3749614357948303},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12852385640144348},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icess49830.2020.9301562","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icess49830.2020.9301562","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 Embedded Software and Systems (ICESS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6899999976158142,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1702419847","https://openalex.org/W1928739709","https://openalex.org/W2022566595","https://openalex.org/W2075156252","https://openalex.org/W2156094778","https://openalex.org/W2210697964","https://openalex.org/W2560609797","https://openalex.org/W2737305288","https://openalex.org/W2799191197","https://openalex.org/W2892644985","https://openalex.org/W2962811204","https://openalex.org/W2962878605","https://openalex.org/W2962926199","https://openalex.org/W2963119249","https://openalex.org/W2963121255","https://openalex.org/W2963377353","https://openalex.org/W2963508807","https://openalex.org/W2963637380","https://openalex.org/W6637606113","https://openalex.org/W6688275570","https://openalex.org/W6739778489","https://openalex.org/W6746154080","https://openalex.org/W6763422710","https://openalex.org/W6765820408"],"related_works":["https://openalex.org/W4320086129","https://openalex.org/W4253893311","https://openalex.org/W3089306886","https://openalex.org/W2113785214","https://openalex.org/W2798721181","https://openalex.org/W3201205132","https://openalex.org/W4387967917","https://openalex.org/W4312694060","https://openalex.org/W4386075737","https://openalex.org/W4393563475"],"abstract_inverted_index":{"Reliable":[0],"hand":[1,24,61,89,123],"pose":[2,25],"estimation":[3],"enriches":[4],"the":[5,19,23,46,55,60,65,77,88,93,106,117,122,128,137,144,151],"way":[6],"of":[7,21,48,140],"human-computer":[8],"interaction,":[9],"such":[10],"as":[11],"sign":[12],"language":[13],"recognition":[14],"and":[15,44,64,96],"virtual":[16],"reality.":[17],"However,":[18],"task":[20],"estimating":[22],"faces":[26],"two":[27],"severe":[28],"challenges.":[29],"To":[30,74],"be":[31,72],"specific,":[32],"it":[33,69,113],"is":[34,114],"difficult":[35],"to":[36,71,105,120,131,143],"learn":[37],"spatial":[38,111,124],"information":[39],"from":[40,92],"a":[41,49,82,98],"2D":[42],"image":[43],"regress":[45],"location":[47,67],"point":[50,94,107],"in":[51],"3D":[52],"space.":[53],"And":[54],"highly":[56],"non-linear":[57],"correlation":[58],"between":[59],"feature":[62,90],"space":[63,91],"joint":[66],"makes":[68],"hard":[70],"modeled.":[73],"deal":[75],"with":[76],"above":[78],"problems,":[79],"we":[80],"propose":[81],"deep":[83,129],"regression":[84],"network,":[85],"which":[86],"learns":[87],"cloud":[95,108],"includes":[97],"specific":[99],"label":[100,133],"distribution":[101],"learning":[102,134],"network.":[103],"Due":[104],"contains":[109],"more":[110],"information,":[112],"beneficial":[115],"for":[116],"neural":[118],"network":[119,130,149],"extract":[121],"geometric":[125],"features.":[126],"Utilizing":[127],"guide":[132],"actively":[135],"reduces":[136],"negative":[138],"effects":[139],"nonlinearity.":[141],"According":[142],"experimental":[145],"results,":[146],"our":[147],"proposed":[148],"achieves":[150],"state-of-the-art":[152],"performance":[153],"on":[154],"MSRA":[155],"dataset.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
