{"id":"https://openalex.org/W7126104121","doi":"https://doi.org/10.1109/wpmc67460.2025.11351270","title":"Geometric Feature Extraction for Robust Grasp Modeling in 3D Environments","display_name":"Geometric Feature Extraction for Robust Grasp Modeling in 3D Environments","publication_year":2025,"publication_date":"2025-11-09","ids":{"openalex":"https://openalex.org/W7126104121","doi":"https://doi.org/10.1109/wpmc67460.2025.11351270"},"language":null,"primary_location":{"id":"doi:10.1109/wpmc67460.2025.11351270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wpmc67460.2025.11351270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Symposium on Wireless Personal Multimedia Communications (WPMC)","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/A5124159867","display_name":"Nomane Belouahchi","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I21491767","display_name":"Aix-Marseille Universit\u00e9","ror":"https://ror.org/035xkbk20","country_code":"FR","type":"education","lineage":["https://openalex.org/I21491767"]},{"id":"https://openalex.org/I4210114274","display_name":"Laboratoire d\u2019Informatique et Syst\u00e8mes","ror":"https://ror.org/0257sgk90","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I143002897","https://openalex.org/I21491767","https://openalex.org/I4210114274"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Nomane Belouahchi","raw_affiliation_strings":["Aix Marseille Univ,CNRS, LIS,Marseille,France"],"affiliations":[{"raw_affiliation_string":"Aix Marseille Univ,CNRS, LIS,Marseille,France","institution_ids":["https://openalex.org/I4210114274","https://openalex.org/I1294671590","https://openalex.org/I21491767"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015530120","display_name":"Nicole Christoff","orcid":"https://orcid.org/0000-0001-9815-5470"},"institutions":[{"id":"https://openalex.org/I31151848","display_name":"Technical University of Sofia","ror":"https://ror.org/052prhs50","country_code":"BG","type":"education","lineage":["https://openalex.org/I31151848"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Nicole Christoff","raw_affiliation_strings":["Technical University of Sofia,Faculty of Telecommunications,Sofia,Bulgaria"],"affiliations":[{"raw_affiliation_string":"Technical University of Sofia,Faculty of Telecommunications,Sofia,Bulgaria","institution_ids":["https://openalex.org/I31151848"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016088751","display_name":"Radostina Petkova","orcid":"https://orcid.org/0000-0002-9731-4406"},"institutions":[{"id":"https://openalex.org/I31151848","display_name":"Technical University of Sofia","ror":"https://ror.org/052prhs50","country_code":"BG","type":"education","lineage":["https://openalex.org/I31151848"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Radostina Petkova","raw_affiliation_strings":["Technical University of Sofia,Faculty of Telecommunications,Sofia,Bulgaria"],"affiliations":[{"raw_affiliation_string":"Technical University of Sofia,Faculty of Telecommunications,Sofia,Bulgaria","institution_ids":["https://openalex.org/I31151848"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005044211","display_name":"Krasimir Tonchev","orcid":"https://orcid.org/0000-0002-3332-666X"},"institutions":[{"id":"https://openalex.org/I31151848","display_name":"Technical University of Sofia","ror":"https://ror.org/052prhs50","country_code":"BG","type":"education","lineage":["https://openalex.org/I31151848"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Krasimir Tonchev","raw_affiliation_strings":["Technical University of Sofia,Faculty of Telecommunications,Sofia,Bulgaria"],"affiliations":[{"raw_affiliation_string":"Technical University of Sofia,Faculty of Telecommunications,Sofia,Bulgaria","institution_ids":["https://openalex.org/I31151848"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5124159867"],"corresponding_institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I21491767","https://openalex.org/I4210114274"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.70840195,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.9429000020027161,"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":0.9429000020027161,"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/T10982","display_name":"Motor Control and Adaptation","score":0.014000000432133675,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12290","display_name":"Human Motion and Animation","score":0.009800000116229057,"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/curvature","display_name":"Curvature","score":0.5760999917984009},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5615000128746033},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5224000215530396},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48750001192092896},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48260000348091125},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4749000072479248},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.47290000319480896},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4519999921321869},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4205999970436096}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6765000224113464},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6025000214576721},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.5760999917984009},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5615000128746033},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.545199990272522},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5224000215530396},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48750001192092896},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48260000348091125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4519999921321869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C2778775528","wikidata":"https://www.wikidata.org/wiki/Q5135432","display_name":"Closing (real estate)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.35929998755455017},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C7305733","wikidata":"https://www.wikidata.org/wiki/Q207961","display_name":"Geometric shape","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.2987000048160553},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.25850000977516174},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wpmc67460.2025.11351270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wpmc67460.2025.11351270","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Symposium on Wireless Personal Multimedia Communications (WPMC)","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":17,"referenced_works":["https://openalex.org/W2736534894","https://openalex.org/W2962737955","https://openalex.org/W3089966216","https://openalex.org/W3170924787","https://openalex.org/W3205146762","https://openalex.org/W3207888596","https://openalex.org/W3211712154","https://openalex.org/W4285505176","https://openalex.org/W4288889651","https://openalex.org/W4293722105","https://openalex.org/W4362561002","https://openalex.org/W4380450805","https://openalex.org/W4382657775","https://openalex.org/W4383109347","https://openalex.org/W4386065366","https://openalex.org/W4391513053","https://openalex.org/W4402753537"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,6,12,16,19,33,38,45,49,52,122,164],"development":[2],"of":[3,35,55,126],"technology":[4],"and":[5,24,57,150],"increasing":[7],"desire":[8],"for":[9,21],"transition":[10],"from":[11,163],"real":[13],"world":[14],"to":[15,47,72,100,153],"virtual":[17,39],"environments,":[18],"need":[20,46],"more":[22],"realistic":[23],"accurate":[25],"humanobject":[26],"interactions":[27],"emerges.":[28],"One":[29],"such":[30],"process":[31],"is":[32],"embedding":[34],"objects":[36,162],"in":[37,42,160,171],"world,":[40],"which":[41],"turn":[43],"requires":[44],"bridge":[48],"gap":[50],"between":[51],"analytical":[53],"synthesis":[54],"grasping":[56,118],"data-driven":[58,96],"approaches.":[59],"Although":[60],"geometric":[61],"descriptors":[62],"capture":[63],"fundamental":[64],"shape":[65],"properties,":[66],"they":[67],"are":[68],"often":[69],"not":[70],"adaptive":[71],"real-world":[73],"uncertainties,":[74],"where":[75],"learning-based":[76],"methods":[77],"excel.":[78],"In":[79],"this":[80],"article,":[81],"we":[82,115],"propose":[83],"a":[84,132,168],"hybrid":[85],"approach":[86,159],"that":[87,146],"combines":[88],"classical":[89],"feature":[90],"extraction":[91],"based":[92],"on":[93,105,136],"curvature":[94,125],"with":[95],"neural":[97],"network":[98],"refinement":[99],"compute":[101],"stable":[102],"contact":[103],"points":[104],"3D":[106],"object":[107],"networks":[108],"under":[109],"partial":[110],"or":[111],"noisy":[112],"observations.":[113],"First,":[114],"identify":[116],"candidate":[117],"locations":[119],"by":[120],"analyzing":[121],"local":[123],"surface":[124],"uniformly":[127],"sampled":[128],"point":[129],"clouds.":[130],"Then,":[131],"multilayer":[133],"perceptron":[134],"trained":[135],"synthetically":[137],"perturbed":[138],"ground":[139],"contacts":[140],"predicts":[141],"small,":[142],"geometrically":[143],"tailored":[144],"corrections":[145],"increase":[147],"force":[148],"closure":[149],"reduce":[151],"sensitivity":[152],"sampling":[154],"error.":[155],"We":[156],"validate":[157],"our":[158],"household":[161],"YCB":[165],"dataset,":[166],"demonstrating":[167],"20-35%":[169],"improvement":[170],"grip":[172],"stability.":[173]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}