{"id":"https://openalex.org/W2810477194","doi":"https://doi.org/10.1109/fskd.2017.8393296","title":"Classify 3D voxel based point-cloud using convolutional neural network on a neural compute stick","display_name":"Classify 3D voxel based point-cloud using convolutional neural network on a neural compute stick","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2810477194","doi":"https://doi.org/10.1109/fskd.2017.8393296","mag":"2810477194"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393296","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393296","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5111569783","display_name":"Xiaofan Xu","orcid":"https://orcid.org/0009-0008-5760-731X"},"institutions":[{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]},{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["GB","US"],"is_corresponding":true,"raw_author_name":"Xiaofan Xu","raw_affiliation_strings":["Computer Vision and Machine Learning Group, Movidius/Intel"],"affiliations":[{"raw_affiliation_string":"Computer Vision and Machine Learning Group, Movidius/Intel","institution_ids":["https://openalex.org/I1304085615","https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033009942","display_name":"Jo\u00e3o Amaro","orcid":"https://orcid.org/0000-0001-8723-3744"},"institutions":[{"id":"https://openalex.org/I4210120471","display_name":"Instituto de Telecomunica\u00e7\u00f5es","ror":"https://ror.org/02ht4fk33","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210120471"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Joao Amaro","raw_affiliation_strings":["Instituto de Telecomunicacoes"],"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunicacoes","institution_ids":["https://openalex.org/I4210120471"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042771885","display_name":"Sam Caulfield","orcid":null},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]},{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Sam Caulfield","raw_affiliation_strings":["Computer Vision and Machine Learning Group, Movidius/Intel"],"affiliations":[{"raw_affiliation_string":"Computer Vision and Machine Learning Group, Movidius/Intel","institution_ids":["https://openalex.org/I1304085615","https://openalex.org/I1343180700"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088848663","display_name":"Gabriel Falc\u00e3o","orcid":"https://orcid.org/0000-0001-9805-6747"},"institutions":[{"id":"https://openalex.org/I4210120471","display_name":"Instituto de Telecomunica\u00e7\u00f5es","ror":"https://ror.org/02ht4fk33","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210120471"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Gabriel Falcao","raw_affiliation_strings":["Instituto de Telecomunicacoes"],"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunicacoes","institution_ids":["https://openalex.org/I4210120471"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043631421","display_name":"David Moloney","orcid":"https://orcid.org/0000-0001-7604-370X"},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]},{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"David Moloney","raw_affiliation_strings":["Computer Vision and Machine Learning Group, Movidius/Intel"],"affiliations":[{"raw_affiliation_string":"Computer Vision and Machine Learning Group, Movidius/Intel","institution_ids":["https://openalex.org/I1304085615","https://openalex.org/I1343180700"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5111569783"],"corresponding_institution_ids":["https://openalex.org/I1304085615","https://openalex.org/I1343180700"],"apc_list":null,"apc_paid":null,"fwci":5.9425,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.95657589,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"6939","issue":null,"first_page":"37","last_page":"43"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9991999864578247,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8422824144363403},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7473140954971313},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7137036919593811},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6341150403022766},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5162937045097351},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4939783215522766},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45212793350219727},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4311577379703522},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38009583950042725},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33193933963775635}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8422824144363403},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7473140954971313},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7137036919593811},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6341150403022766},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5162937045097351},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4939783215522766},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45212793350219727},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4311577379703522},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38009583950042725},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33193933963775635},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393296","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393296","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W156975732","https://openalex.org/W317170363","https://openalex.org/W1644641054","https://openalex.org/W1789336918","https://openalex.org/W1905882502","https://openalex.org/W1915761309","https://openalex.org/W1920022804","https://openalex.org/W2071879227","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2119112357","https://openalex.org/W2155893237","https://openalex.org/W2211722331","https://openalex.org/W2271840356","https://openalex.org/W2283234189","https://openalex.org/W2300242332","https://openalex.org/W2492473091","https://openalex.org/W2555254696","https://openalex.org/W2556802233","https://openalex.org/W2593245696","https://openalex.org/W2964228333","https://openalex.org/W6611025721","https://openalex.org/W6640102224","https://openalex.org/W6640300118","https://openalex.org/W6698200048"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4389574804","https://openalex.org/W4375867731","https://openalex.org/W2936725271","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"With":[0],"the":[1,53,82,85,112,119,160,167,174,178,188],"recent":[2],"surge":[3],"in":[4,16,34,115],"popularity":[5],"of":[6,71,81,90,211],"Convolutional":[7],"Neural":[8,169],"Networks":[9],"(CNNs),":[10],"motivated":[11],"by":[12],"their":[13],"significant":[14,148],"performance":[15,89],"many":[17],"classification":[18],"and":[19,41,64,76,87,159],"related":[20],"tasks,":[21],"a":[22,98,139,207],"new":[23],"challenge":[24,50],"now":[25],"needs":[26],"to":[27,31,47,117,166,199,215],"be":[28],"addressed:":[29],"how":[30],"accommodate":[32],"CNNs":[33],"mobile":[35],"devices,":[36],"such":[37],"as":[38],"drones,":[39],"smartphones,":[40],"similar":[42],"low-power":[43],"devices?":[44],"In":[45,93],"order":[46,116],"tackle":[48],"this":[49,94],"we":[51,96],"exploit":[52],"Vision":[54],"Processing":[55],"Unit":[56],"(VPU)":[57],"that":[58],"combines":[59],"dedicated":[60],"CNN":[61,155,176],"hardware":[62],"blocks":[63],"very":[65],"high":[66],"power":[67,209],"efficiency.":[68],"The":[69],"lack":[70],"readily":[72],"available":[73],"training":[74,86,120],"data":[75],"memory":[77,149],"requirements":[78],"are":[79],"two":[80],"factors":[83],"hindering":[84],"accuracy":[88,181],"3D":[91,103,128],"CNNs.":[92,124],"paper,":[95],"propose":[97],"method":[99],"for":[100,122,147,151],"generating":[101],"synthetic":[102],"point-clouds":[104],"from":[105],"realistic":[106],"CAD":[107],"scene":[108],"models":[109,156],"(based":[110],"on":[111,187,202],"ModelNet10":[113],"dataset),":[114],"enrich":[118],"process":[121],"volumetric":[123,129,152],"Furthermore,":[125],"an":[126],"efficient":[127],"object":[130],"representation":[131,144],"(VOLA)":[132],"is":[133,138,183],"employed.":[134],"VOLA":[135],"(Volumetric":[136],"Accelerator)":[137],"sexaquaternary":[140],"(power-of-four":[141],"subdivision)":[142],"tree-based":[143],"which":[145,213],"allows":[146],"saving":[150],"data.":[153],"Multiple":[154],"were":[157],"trained":[158,175],"top":[161],"performing":[162],"model":[163],"was":[164],"ported":[165],"Fathom":[168,189],"Compute":[170],"Stick":[171],"(NCS).":[172],"Among":[173],"models,":[177],"maximum":[179],"test":[180],"achieved":[182],"91.3%.":[184],"After":[185],"deployment":[186],"NCS,":[190],"it":[191],"takes":[192],"11ms":[193],"(~":[194],"90":[195],"frames":[196],"per":[197,218,220],"second)":[198],"perform":[200],"inference":[201,217],"each":[203],"input":[204],"volume,":[205],"with":[206],"reported":[208],"requirement":[210],"1.2W":[212],"leads":[214],"75.75":[216],"second":[219],"Watt.":[221]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}