{"id":"https://openalex.org/W4415707470","doi":"https://doi.org/10.1109/lra.2025.3627067","title":"PartPose: Attentive 6D Pose Estimation by Focusing on Graspable Parts of Multi-Part Deformable Objects","display_name":"PartPose: Attentive 6D Pose Estimation by Focusing on Graspable Parts of Multi-Part Deformable Objects","publication_year":2025,"publication_date":"2025-10-30","ids":{"openalex":"https://openalex.org/W4415707470","doi":"https://doi.org/10.1109/lra.2025.3627067"},"language":null,"primary_location":{"id":"doi:10.1109/lra.2025.3627067","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lra.2025.3627067","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1109/lra.2025.3627067","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109594509","display_name":"Ryo Okumura","orcid":null},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryo Okumura","raw_affiliation_strings":["Panasonic Holdings Corporation, Osaka, Japan","Panasonic Holdings Corp., Osaka, Japan"],"raw_orcid":"https://orcid.org/0000-0003-4370-4860","affiliations":[{"raw_affiliation_string":"Panasonic Holdings Corporation, Osaka, Japan","institution_ids":["https://openalex.org/I1283155146"]},{"raw_affiliation_string":"Panasonic Holdings Corp., Osaka, Japan","institution_ids":["https://openalex.org/I1283155146"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023160093","display_name":"Tadahiro Taniguchi","orcid":"https://orcid.org/0000-0002-5682-2076"},"institutions":[{"id":"https://openalex.org/I1283155146","display_name":"Panasonic (Japan)","ror":"https://ror.org/011tm7n37","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283155146"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tadahiro Taniguchi","raw_affiliation_strings":["Panasonic Holdings Corporation, Osaka, Japan","Panasonic Holdings Corp., Osaka, Japan"],"raw_orcid":"https://orcid.org/0000-0002-5682-2076","affiliations":[{"raw_affiliation_string":"Panasonic Holdings Corporation, Osaka, Japan","institution_ids":["https://openalex.org/I1283155146"]},{"raw_affiliation_string":"Panasonic Holdings Corp., Osaka, Japan","institution_ids":["https://openalex.org/I1283155146"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5109594509"],"corresponding_institution_ids":["https://openalex.org/I1283155146"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38372511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"12","first_page":"13082","last_page":"13089"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9409000277519226,"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":0.9409000277519226,"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/T10868","display_name":"Soft Robotics and Applications","score":0.014000000432133675,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.006899999920278788,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.8105999827384949},{"id":"https://openalex.org/keywords/articulated-body-pose-estimation","display_name":"Articulated body pose estimation","score":0.6699000000953674},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.49549999833106995},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.447299987077713},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.36550000309944153},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.3165000081062317}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.8105999827384949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.763700008392334},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7049000263214111},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777999997138977},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.6699000000953674},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.49549999833106995},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.2596000134944916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2025.3627067","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lra.2025.3627067","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/lra.2025.3627067","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lra.2025.3627067","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W132147841","https://openalex.org/W1526868886","https://openalex.org/W1817644897","https://openalex.org/W2016959837","https://openalex.org/W2083624955","https://openalex.org/W2574567538","https://openalex.org/W2580726517","https://openalex.org/W2771008497","https://openalex.org/W2888752296","https://openalex.org/W2949676527","https://openalex.org/W2963150697","https://openalex.org/W2963188159","https://openalex.org/W3011972190","https://openalex.org/W3035131925","https://openalex.org/W3159947841","https://openalex.org/W4224598146","https://openalex.org/W4312956471","https://openalex.org/W4390190260","https://openalex.org/W4390874575","https://openalex.org/W4399213366","https://openalex.org/W4402727436","https://openalex.org/W4402754209","https://openalex.org/W4403941282","https://openalex.org/W4404612908","https://openalex.org/W4408357973","https://openalex.org/W4413147748","https://openalex.org/W4413917067"],"related_works":[],"abstract_inverted_index":{"This":[0,173],"study":[1],"tackles":[2],"robotic":[3],"picking":[4,93],"of":[5,57,77,136],"multi-part":[6,59,98],"deformable":[7,27,60,99],"objects\u2014common":[8],"in":[9,13],"warehouses":[10],"yet":[11],"underexplored":[12],"the":[14,54,58,65,108,118,160],"literature\u2014such":[15],"as":[16,40,105],"cable-attached":[17],"appliances":[18],"and":[19,26,80,92,117,141,147,184],"pouch":[20],"drinks,":[21],"which":[22,52],"comprise":[23],"both":[24],"rigid":[25,44,66],"components.":[28,67,172],"Their":[29],"deformability":[30],"poses":[31],"a":[32,86,102,130],"challenge":[33],"to":[34,72,156],"model-based":[35],"6D":[36,55],"pose":[37,56,84,103],"estimators,":[38],"such":[39],"FoundationPose,":[41],"that":[42,181],"assume":[43],"bodies.":[45],"To":[46],"address":[47],"this,":[48],"we":[49],"present":[50],"PartPose,":[51],"estimates":[53,82],"objects":[61],"by":[62],"focusing":[63],"on":[64,96],"PartPose":[68,127,151],"uses":[69],"Bayesian":[70],"optimization":[71],"select":[73],"an":[74],"appropriate":[75],"region":[76],"interest":[78],"(ROI)":[79],"then":[81],"its":[83],"with":[85],"render-and-compare":[87],"pipeline.":[88],"We":[89],"evaluate":[90],"pose-estimation":[91],"success":[94,134],"rates":[95,135],"nine":[97],"objects,":[100],"counting":[101],"estimate":[104],"successful":[106],"if":[107],"translational":[109],"error":[110,120],"is":[111,121,175],"<inline-formula":[112,122],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[113,123],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math":[114,124],"notation=\"LaTeX\">$\\leq$</tex-math></inline-formula>30":[115],"mm":[116],"rotational":[119],"notation=\"LaTeX\">$\\leq$</tex-math></inline-formula>0.3":[125],"radians.":[126],"significantly":[128],"outperforms":[129],"FoundationPose":[131],"baseline,":[132],"achieving":[133],"98.2%":[137],"(translational),":[138],"96.4%":[139],"(rotational),":[140],"87.2%":[142],"(picking),":[143],"versus":[144],"47.9%,":[145],"35.9%,":[146],"22.8%,":[148],"respectively.":[149],"Moreover,":[150],"generalizes":[152],"category-level":[153],"semantic":[154],"knowledge":[155],"new":[157],"instances":[158,168],"within":[159],"same":[161],"category":[162],"without":[163],"performance":[164],"degradation":[165],"when":[166],"those":[167],"have":[169],"semantically":[170],"similar":[171],"capability":[174],"crucial":[176],"for":[177],"large":[178],"logistics":[179],"centers":[180],"handle":[182],"diverse":[183],"novel":[185],"objects.":[186]},"counts_by_year":[],"updated_date":"2025-11-07T23:20:04.922697","created_date":"2025-10-30T00:00:00"}