{"id":"https://openalex.org/W4410341943","doi":"https://doi.org/10.1109/ncc63735.2025.10983334","title":"OccRobNet: Occlusion Robust Network for Accurate 3D Interacting Hand-Object Pose Estimation","display_name":"OccRobNet: Occlusion Robust Network for Accurate 3D Interacting Hand-Object Pose Estimation","publication_year":2025,"publication_date":"2025-03-06","ids":{"openalex":"https://openalex.org/W4410341943","doi":"https://doi.org/10.1109/ncc63735.2025.10983334"},"language":"en","primary_location":{"id":"doi:10.1109/ncc63735.2025.10983334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc63735.2025.10983334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 National Conference on Communications (NCC)","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/A5031216644","display_name":"Mallika Garg","orcid":"https://orcid.org/0000-0002-6056-6490"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Mallika Garg","raw_affiliation_strings":["IIT,ECE Department,Roorkee,India"],"affiliations":[{"raw_affiliation_string":"IIT,ECE Department,Roorkee,India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030605875","display_name":"Pyari Mohan Pradhan","orcid":"https://orcid.org/0000-0002-6070-5577"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pyari Mohan Pradhan","raw_affiliation_strings":["IIT,ECE Department,Roorkee,India"],"affiliations":[{"raw_affiliation_string":"IIT,ECE Department,Roorkee,India","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100625251","display_name":"Debashis Ghosh","orcid":"https://orcid.org/0000-0001-5672-7645"},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Debashis Ghosh","raw_affiliation_strings":["IIT,ECE Department,Roorkee,India"],"affiliations":[{"raw_affiliation_string":"IIT,ECE Department,Roorkee,India","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031216644"],"corresponding_institution_ids":["https://openalex.org/I154851008"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10862491,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9988999962806702,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9940999746322632,"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/pose","display_name":"Pose","score":0.789017379283905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7461447715759277},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7458664774894714},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.726756751537323},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5699797868728638},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.5347871780395508},{"id":"https://openalex.org/keywords/occlusion","display_name":"Occlusion","score":0.5258013606071472},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08736151456832886}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.789017379283905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7461447715759277},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7458664774894714},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.726756751537323},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5699797868728638},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.5347871780395508},{"id":"https://openalex.org/C2776268601","wikidata":"https://www.wikidata.org/wiki/Q968808","display_name":"Occlusion","level":2,"score":0.5258013606071472},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08736151456832886},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ncc63735.2025.10983334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ncc63735.2025.10983334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 National Conference on Communications (NCC)","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":23,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1967554269","https://openalex.org/W2194775991","https://openalex.org/W2565639579","https://openalex.org/W2724316534","https://openalex.org/W2768683308","https://openalex.org/W2897765997","https://openalex.org/W2964304707","https://openalex.org/W2968722025","https://openalex.org/W2973857456","https://openalex.org/W3034470433","https://openalex.org/W3034479523","https://openalex.org/W3035467087","https://openalex.org/W3096609285","https://openalex.org/W3107167007","https://openalex.org/W3178872387","https://openalex.org/W3183430956","https://openalex.org/W4298014233","https://openalex.org/W4312923690","https://openalex.org/W4385245566","https://openalex.org/W4401536861","https://openalex.org/W6781983457","https://openalex.org/W6784094891"],"related_works":["https://openalex.org/W3089306886","https://openalex.org/W2113785214","https://openalex.org/W3201205132","https://openalex.org/W2798721181","https://openalex.org/W4387967917","https://openalex.org/W4312694060","https://openalex.org/W4386075737","https://openalex.org/W4382141741","https://openalex.org/W1968716783","https://openalex.org/W2736638679"],"abstract_inverted_index":{"Occlusion":[0],"is":[1,51],"one":[2],"of":[3,73,126],"the":[4,29,71,78,87,109,114,119,123,134,138,152,161,164,167,183],"challenging":[5],"issues":[6],"when":[7,17,180],"estimating":[8],"3D":[9,54,74],"hand":[10,18,55,88,115,124,145],"pose.":[11],"This":[12,117],"problem":[13],"becomes":[14,170],"more":[15],"prominent":[16],"interacts":[19],"with":[20,113,144],"an":[21,64],"object":[22],"or":[23],"two":[24],"hands":[25],"are":[26,147],"involved.":[27],"In":[28],"past":[30],"works,":[31],"much":[32],"attention":[33,105,156],"has":[34],"not":[35],"been":[36],"given":[37],"to":[38,121,132,150,172],"these":[39,43,142],"occluded":[40,139,165],"regions.":[41],"But":[42],"regions":[44],"contain":[45],"important":[46],"and":[47,67,95,186],"beneficial":[48],"information":[49],"that":[50],"vital":[52],"for":[53,70],"pose":[56,76,153],"estimation.":[57],"Thus,":[58,158],"in":[59,137,163],"this":[60,175],"paper,":[61],"we":[62],"propose":[63],"occlusion":[65],"robust":[66,171],"accurate":[68],"method":[69,83],"estimation":[72],"hand-object":[75],"from":[77],"input":[79],"RGB":[80],"image.":[81],"Our":[82],"includes":[84],"first":[85],"localising":[86],"joints":[89,111,143,162],"using":[90,154],"a":[91,127],"CNN":[92],"based":[93],"model":[94,120],"then":[96,107,148],"refining":[97],"them":[98],"by":[99,159],"extracting":[100],"contextual":[101],"information.":[102],"The":[103],"self":[104],"transformer":[106],"identifies":[108],"specific":[110],"along":[112],"identity.":[116],"helps":[118,131],"identify":[122],"belongingness":[125],"particular":[128],"joint":[129,135],"which":[130],"detect":[133],"even":[136],"region.":[140],"Further,":[141],"identity":[146],"used":[149],"estimate":[151],"cross":[155],"mechanism.":[157],"identifying":[160],"region,":[166],"obtained":[168],"network":[169,176],"occlusion.":[173],"Hence,":[174],"achieves":[177],"state-of-the-art":[178],"results":[179],"evaluated":[181],"on":[182],"InterHand2.6M,":[184],"H03D":[185],"H<inf":[187],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[188],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</inf>03D":[189],"datasets.":[190]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
