{"id":"https://openalex.org/W2504204199","doi":"https://doi.org/10.48550/arxiv.1607.05695","title":"FusionNet: 3D Object Classification Using Multiple Data Representations","display_name":"FusionNet: 3D Object Classification Using Multiple Data Representations","publication_year":2016,"publication_date":"2016-07-19","ids":{"openalex":"https://openalex.org/W2504204199","doi":"https://doi.org/10.48550/arxiv.1607.05695","mag":"2504204199"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1607.05695","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1607.05695","pdf_url":"https://arxiv.org/pdf/1607.05695","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1607.05695","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031815300","display_name":"Vishakh Hegde","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hegde, Vishakh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110281879","display_name":"Reza Bosagh Zadeh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zadeh, Reza","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":213,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7285000085830688,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.7285000085830688,"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":0.24690000712871552,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.011099999770522118,"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/3d-object-classification","display_name":"3D object classification","score":0.9850000143051147},{"id":"https://openalex.org/keywords/3d-object-recognition","display_name":"3D object recognition","score":0.9430000185966492},{"id":"https://openalex.org/keywords/volumetric-representation","display_name":"volumetric representation","score":0.7229999899864197},{"id":"https://openalex.org/keywords/voxel-data","display_name":"voxel data","score":0.6230000257492065},{"id":"https://openalex.org/keywords/multi-view-projection","display_name":"multi-view projection","score":0.37299999594688416},{"id":"https://openalex.org/keywords/feature-fusion","display_name":"feature fusion","score":0.6100000143051147}],"concepts":[{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5605120062828064},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5535345077514648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45951902866363525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3856450915336609},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3394860625267029}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1607.05695","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1607.05695","pdf_url":"https://arxiv.org/pdf/1607.05695","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1607.05695","doi":"https://doi.org/10.48550/arxiv.1607.05695","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1607.05695","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1607.05695","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1607.05695","pdf_url":"https://arxiv.org/pdf/1607.05695","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2504204199.pdf","grobid_xml":"https://content.openalex.org/works/W2504204199.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1561952261","https://openalex.org/W1623385256","https://openalex.org/W1629010235","https://openalex.org/W1686810756","https://openalex.org/W1799366690","https://openalex.org/W1920022804","https://openalex.org/W2021122545","https://openalex.org/W2022508996","https://openalex.org/W2036163530","https://openalex.org/W2053939793","https://openalex.org/W2059917035","https://openalex.org/W2095705004","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2122585444","https://openalex.org/W2211722331","https://openalex.org/W2216541755","https://openalex.org/W2400418317","https://openalex.org/W2949667497","https://openalex.org/W2950179405","https://openalex.org/W2953360861","https://openalex.org/W7098642242"],"related_works":["https://openalex.org/W2492473091","https://openalex.org/W2505262851","https://openalex.org/W4282933768","https://openalex.org/W2339077268","https://openalex.org/W2855903050","https://openalex.org/W3004899292","https://openalex.org/W3001702258","https://openalex.org/W2700725495","https://openalex.org/W3030141391","https://openalex.org/W2894703649"],"abstract_inverted_index":{"High-quality":[0],"3D":[1,40,62],"object":[2,17,41,63],"recognition":[3,18],"is":[4,42,53,64],"an":[5],"important":[6],"component":[7],"of":[8,69,107,132],"many":[9],"vision":[10],"and":[11,55,95,116],"robotics":[12],"systems.":[13],"We":[14,112],"tackle":[15],"the":[16,29,39,51,61,78,102,105,108,133],"problem":[19],"using":[20,130],"two":[21,34,110],"data":[22],"representations,":[23],"to":[24,77,100,119],"achieve":[25],"leading":[26,75],"results":[27],"on":[28,86],"Princeton":[30],"ModelNet":[31,79],"challenge.":[32],"The":[33],"representations:":[35],"1.":[36],"Volumetric":[37,98,143],"representation:":[38,60],"discretized":[43],"spatially":[44],"as":[45,66],"binary":[46],"voxels":[47],"-":[48],"$1$":[49],"if":[50],"voxel":[52],"occupied":[54],"$0$":[56],"otherwise.":[57],"2.":[58],"Pixel":[59],"represented":[65],"a":[67,125],"set":[68],"projected":[70],"2D":[71],"pixel":[72,87],"images.":[73],"Current":[74],"submissions":[76],"Challenge":[80],"use":[81,97],"Convolutional":[82],"Neural":[83],"Networks":[84],"(CNNs)":[85],"representations.":[88,111],"However,":[89],"we":[90,140],"diverge":[91],"from":[92],"this":[93],"trend":[94],"additionally,":[96],"CNNs":[99],"bridge":[101],"gap":[103],"between":[104],"efficiency":[106],"above":[109],"combine":[113],"both":[114],"representations":[115,134],"exploit":[117],"them":[118],"learn":[120],"new":[121,142],"features,":[122],"which":[123],"yield":[124],"significantly":[126],"better":[127],"classifier":[128],"than":[129],"either":[131],"in":[135],"isolation.":[136],"To":[137],"do":[138],"this,":[139],"introduce":[141],"CNN":[144],"(V-CNN)":[145],"architectures.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":32},{"year":2020,"cited_by_count":36},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":40},{"year":2017,"cited_by_count":24},{"year":2016,"cited_by_count":2}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2016-08-23T00:00:00"}
