{"id":"https://openalex.org/W4387967917","doi":"https://doi.org/10.1145/3581783.3612089","title":"LocalPose: Object Pose Estimation with Local Geometry Guidance","display_name":"LocalPose: Object Pose Estimation with Local Geometry Guidance","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387967917","doi":"https://doi.org/10.1145/3581783.3612089"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5101910179","display_name":"Yang Xiao","orcid":"https://orcid.org/0009-0009-7215-9073"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Xiao","raw_affiliation_strings":["Huawei Technologies Ltd, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-7215-9073","affiliations":[{"raw_affiliation_string":"Huawei Technologies Ltd, Beijing, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087352522","display_name":"Bo Duan","orcid":"https://orcid.org/0009-0002-3871-081X"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Duan","raw_affiliation_strings":["Huawei Technologies Ltd, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0002-3871-081X","affiliations":[{"raw_affiliation_string":"Huawei Technologies Ltd, Wuhan, China","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101843589","display_name":"Mingwei Sun","orcid":"https://orcid.org/0000-0003-2006-4645"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]},{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingwei Sun","raw_affiliation_strings":["Huawei Technologies Ltd, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-2006-4645","affiliations":[{"raw_affiliation_string":"Huawei Technologies Ltd, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I2250955327","https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101529572","display_name":"Jingwei Huang","orcid":"https://orcid.org/0000-0003-0356-6002"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingwei Huang","raw_affiliation_strings":["Huawei Technologies Ltd, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-0356-6002","affiliations":[{"raw_affiliation_string":"Huawei Technologies Ltd, Shenzhen, China","institution_ids":["https://openalex.org/I2250955327"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101910179"],"corresponding_institution_ids":["https://openalex.org/I2250955327"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16302199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8176","last_page":"8184"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9983999729156494,"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":0.9983999729156494,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9883999824523926,"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/pose","display_name":"Pose","score":0.9543954133987427},{"id":"https://openalex.org/keywords/3d-pose-estimation","display_name":"3D pose estimation","score":0.798815131187439},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7210769653320312},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6795580387115479},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6662155985832214},{"id":"https://openalex.org/keywords/articulated-body-pose-estimation","display_name":"Articulated body pose estimation","score":0.6602010726928711},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.6261121034622192},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5904971957206726},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.578954815864563},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43546003103256226},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4353397488594055},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43160220980644226},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.4256865978240967},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4243849515914917},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40062659978866577}],"concepts":[{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.9543954133987427},{"id":"https://openalex.org/C36613465","wikidata":"https://www.wikidata.org/wiki/Q4636322","display_name":"3D pose estimation","level":3,"score":0.798815131187439},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7210769653320312},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6795580387115479},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6662155985832214},{"id":"https://openalex.org/C22100474","wikidata":"https://www.wikidata.org/wiki/Q4800952","display_name":"Articulated body pose estimation","level":4,"score":0.6602010726928711},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6261121034622192},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5904971957206726},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.578954815864563},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43546003103256226},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4353397488594055},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43160220980644226},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.4256865978240967},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4243849515914917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40062659978866577},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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:10.1145/3581783.3612089","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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":30,"referenced_works":["https://openalex.org/W1991544872","https://openalex.org/W2057069782","https://openalex.org/W2119605622","https://openalex.org/W2124386111","https://openalex.org/W2128019145","https://openalex.org/W2161168419","https://openalex.org/W2604236302","https://openalex.org/W2768879211","https://openalex.org/W2963756608","https://openalex.org/W2964249569","https://openalex.org/W2981378444","https://openalex.org/W2989915422","https://openalex.org/W3009516594","https://openalex.org/W3034597466","https://openalex.org/W3096754345","https://openalex.org/W3107992529","https://openalex.org/W3163945288","https://openalex.org/W3202459445","https://openalex.org/W3202538459","https://openalex.org/W3215207332","https://openalex.org/W4214526701","https://openalex.org/W4225435562","https://openalex.org/W4226321491","https://openalex.org/W4286696412","https://openalex.org/W4297683907","https://openalex.org/W4298014068","https://openalex.org/W4312296985","https://openalex.org/W4312604533","https://openalex.org/W4312982499","https://openalex.org/W4386075656"],"related_works":["https://openalex.org/W2113785214","https://openalex.org/W2946083937","https://openalex.org/W2798721181","https://openalex.org/W4299867837","https://openalex.org/W4386075737","https://openalex.org/W2951583186","https://openalex.org/W1974260915","https://openalex.org/W4382141741","https://openalex.org/W2088028039","https://openalex.org/W3165753266"],"abstract_inverted_index":{"We":[0,35,94],"present":[1],"LocalPose,":[2],"a":[3,30,108,143],"novel":[4,92],"method":[5,157],"for":[6,126],"9":[7,113],"DoF":[8,114],"object":[9,13,40,128],"pose":[10,19,41,66,80,99,103,115,118,137,146,165,175],"estimation":[11],"from":[12,21],"point":[14,70],"clouds.":[15],"Existing":[16],"works":[17],"regress":[18],"directly":[20],"the":[22,38,75,87,121,127,150,159],"global":[23,39,76],"shape":[24],"embedding":[25],"and":[26,71,89,120,169,179],"are":[27,82],"limited":[28],"to":[29,45,60,84,91,111,141,148],"fixed":[31],"set":[32],"of":[33,98,123,145,163],"shapes.":[34,93],"identify":[36],"that":[37,101,155],"is":[42,59],"closely":[43],"related":[44],"local":[46,62,79],"geometry":[47,63],"properties":[48,64],"like":[49],"surface":[50,133],"orientations":[51],"at":[52,68],"representative":[53],"regions.":[54],"Therefore,":[55],"our":[56,156],"key":[57],"idea":[58],"summarize":[61],"as":[65,117,135,177],"signatures":[67,81,100,116,176],"each":[69],"aggregate":[72],"them":[73,125],"into":[74],"pose,":[77],"where":[78],"easier":[83],"learn":[85,107],"by":[86,173],"network":[88,110],"generalize":[90],"find":[95],"two":[96],"types":[97],"benefit":[102],"estimation.":[104],"First,":[105],"we":[106,131],"neural":[109],"predict":[112],"candidates,":[119],"process":[122],"voting":[124],"pose.":[129],"Second,":[130],"treat":[132],"normals":[134],"direct":[136],"regulators":[138],"who":[139],"help":[140],"select":[142],"subset":[144],"candidates":[147,178],"achieve":[149],"best":[151],"accuracy.":[152],"Experiments":[153],"show":[154],"outperforms":[158],"state-of-the-art":[160],"in":[161],"terms":[162],"fine-grained":[164],"accuracy":[166],"on":[167],"synthetic":[168],"real":[170],"datasets,":[171],"contributed":[172],"both":[174],"regulators.":[180]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}