{"id":"https://openalex.org/W2903548913","doi":"https://doi.org/10.1109/icpr.2018.8545566","title":"Multi-View Classification and 3D Bounding Box Regression Networks","display_name":"Multi-View Classification and 3D Bounding Box Regression Networks","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903548913","doi":"https://doi.org/10.1109/icpr.2018.8545566","mag":"2903548913"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5048269567","display_name":"Christopher Pramerdorfer","orcid":null},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Christopher Pramerdorfer","raw_affiliation_strings":["Cogvis & TUWien, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cogvis & TUWien, Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081324708","display_name":"Martin Kampel","orcid":"https://orcid.org/0000-0002-5217-2854"},"institutions":[{"id":"https://openalex.org/I145847075","display_name":"TU Wien","ror":"https://ror.org/04d836q62","country_code":"AT","type":"education","lineage":["https://openalex.org/I145847075"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Martin Kampel","raw_affiliation_strings":["TU Wien, Vienna, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Wien, Vienna, Austria","institution_ids":["https://openalex.org/I145847075"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040604298","display_name":"Mark Van Loock","orcid":"https://orcid.org/0000-0001-9811-7060"},"institutions":[{"id":"https://openalex.org/I4210120547","display_name":"Toyota Motor Corporation (Belgium)","ror":"https://ror.org/023g86t37","country_code":"BE","type":"company","lineage":["https://openalex.org/I4210120547","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Mark Van Loock","raw_affiliation_strings":["Toyota Motor Europe, Brussels, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Motor Europe, Brussels, Belgium","institution_ids":["https://openalex.org/I4210120547"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"734","last_page":"739"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9995999932289124,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9950000047683716,"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":0.9914000034332275,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.8736968040466309},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.7778221368789673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7544281482696533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7045636773109436},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6926750540733337},{"id":"https://openalex.org/keywords/amodal-perception","display_name":"Amodal perception","score":0.6734849214553833},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.617553174495697},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.588123083114624},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5219385027885437},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46734505891799927},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4465814530849457},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4404309391975403},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41257408261299133},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20142650604248047},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15357369184494019},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1177593469619751}],"concepts":[{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.8736968040466309},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.7778221368789673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544281482696533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7045636773109436},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6926750540733337},{"id":"https://openalex.org/C174478892","wikidata":"https://www.wikidata.org/wiki/Q4747455","display_name":"Amodal perception","level":3,"score":0.6734849214553833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.617553174495697},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.588123083114624},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5219385027885437},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46734505891799927},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4465814530849457},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4404309391975403},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41257408261299133},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20142650604248047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15357369184494019},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1177593469619751},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1565402342","https://openalex.org/W1644641054","https://openalex.org/W1920022804","https://openalex.org/W2058535340","https://openalex.org/W2076068958","https://openalex.org/W2190691619","https://openalex.org/W2229637417","https://openalex.org/W2556802233","https://openalex.org/W2557465155","https://openalex.org/W2560609797","https://openalex.org/W2749208254","https://openalex.org/W2962731536","https://openalex.org/W4394671432","https://openalex.org/W6633727509","https://openalex.org/W6640300118","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W3192357901","https://openalex.org/W3036286480","https://openalex.org/W2387360586","https://openalex.org/W4287027631","https://openalex.org/W4237171675","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W2962677013"],"abstract_inverted_index":{"We":[0,76],"present":[1],"a":[2,22,35,89,112],"method":[3,31,82,103],"for":[4,45],"jointly":[5],"classifying":[6],"objects":[7],"in":[8,21,69],"depth":[9],"maps":[10],"and":[11,58,61,65,111,127],"regressing":[12],"amodal":[13],"(extending":[14],"beyond":[15],"occluded":[16],"parts)":[17],"3D":[18,97],"bounding":[19,66],"boxes":[20],"way":[23],"that":[24,54],"is":[25,32],"highly":[26],"robust":[27],"to":[28,123],"occlusions.":[29],"Our":[30],"based":[33],"on":[34],"novel":[36],"multi-view":[37],"convolutional":[38],"neural":[39],"network":[40,51],"architecture":[41],"with":[42],"shared":[43],"layers":[44],"both":[46],"tasks,":[47],"improving":[48],"efficiency.":[49],"The":[50,125],"processes":[52],"views":[53],"encode":[55],"object":[56],"geometry":[57],"occlusion":[59,119],"information":[60],"outputs":[62],"class":[63],"scores":[64],"box":[67],"coordinates":[68],"world":[70],"coordinates,":[71],"requiring":[72],"no":[73],"post-processing":[74],"steps.":[75],"demonstrate":[77],"the":[78],"effectiveness":[79],"of":[80,85,92,121],"our":[81,102],"by":[83],"example":[84],"fall":[86],"detection,":[87],"presenting":[88],"new":[90],"dataset":[91,126],"40k":[93],"samples":[94],"rendered":[95],"from":[96],"models.":[98],"On":[99],"this":[100],"dataset,":[101],"achieves":[104],"an":[105],"average":[106],"classification":[107],"accuracy":[108],"above":[109],"97%":[110],"regression":[113],"error":[114],"below":[115],"10":[116],"cm":[117],"at":[118],"ratios":[120],"up":[122],"90%.":[124],"trained":[128],"models":[129],"are":[130],"publicly":[131],"available.":[132]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
