{"id":"https://openalex.org/W3010454265","doi":"https://doi.org/10.1109/wacv45572.2020.9093276","title":"Frustum VoxNet for 3D object detection from RGB-D or Depth images","display_name":"Frustum VoxNet for 3D object detection from RGB-D or Depth images","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3010454265","doi":"https://doi.org/10.1109/wacv45572.2020.9093276","mag":"3010454265"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093276","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093276","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5010095320","display_name":"Xiaoke Shen","orcid":"https://orcid.org/0000-0001-9674-2436"},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaoke Shen","raw_affiliation_strings":["The Graduate Center, CUNY, New York City, USA"],"affiliations":[{"raw_affiliation_string":"The Graduate Center, CUNY, New York City, USA","institution_ids":["https://openalex.org/I121847817"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079727022","display_name":"Ioannis Stamos","orcid":null},"institutions":[{"id":"https://openalex.org/I121847817","display_name":"The Graduate Center, CUNY","ror":"https://ror.org/00awd9g61","country_code":"US","type":"education","lineage":["https://openalex.org/I121847817"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ioannis Stamos","raw_affiliation_strings":["The Graduate Center, CUNY, New York City, USA"],"affiliations":[{"raw_affiliation_string":"The Graduate Center, CUNY, New York City, USA","institution_ids":["https://openalex.org/I121847817"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5010095320"],"corresponding_institution_ids":["https://openalex.org/I121847817"],"apc_list":null,"apc_paid":null,"fwci":1.8563,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.8761343,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1687","last_page":"1695"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9969000220298767,"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/computer-science","display_name":"Computer science","score":0.824435830116272},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.7741458415985107},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.758521556854248},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6748302578926086},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6486876010894775},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.565830409526825},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5424923896789551},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41407400369644165},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29794609546661377}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.824435830116272},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.7741458415985107},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.758521556854248},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6748302578926086},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6486876010894775},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.565830409526825},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5424923896789551},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41407400369644165},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29794609546661377},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093276","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093276","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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":47,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1565402342","https://openalex.org/W1836465849","https://openalex.org/W1861492603","https://openalex.org/W1903029394","https://openalex.org/W1923184257","https://openalex.org/W1984681615","https://openalex.org/W2037227137","https://openalex.org/W2095705004","https://openalex.org/W2102605133","https://openalex.org/W2186094539","https://openalex.org/W2194775991","https://openalex.org/W2229637417","https://openalex.org/W2463032559","https://openalex.org/W2555618208","https://openalex.org/W2560609797","https://openalex.org/W2561343020","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2613718673","https://openalex.org/W2780829839","https://openalex.org/W2947710136","https://openalex.org/W2949117887","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963400571","https://openalex.org/W2963727135","https://openalex.org/W2964062501","https://openalex.org/W2988715931","https://openalex.org/W6628973269","https://openalex.org/W6633727509","https://openalex.org/W6638667902","https://openalex.org/W6639102338","https://openalex.org/W6640054144","https://openalex.org/W6674330103","https://openalex.org/W6675026286","https://openalex.org/W6687483927","https://openalex.org/W6689579922","https://openalex.org/W6729973049","https://openalex.org/W6730903564","https://openalex.org/W6731892127","https://openalex.org/W6745673378","https://openalex.org/W6747139287","https://openalex.org/W6749954789","https://openalex.org/W6763160220","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W3016928466","https://openalex.org/W4293226380","https://openalex.org/W4389574804","https://openalex.org/W2936725271","https://openalex.org/W3150655618","https://openalex.org/W2295788148","https://openalex.org/W1578717197","https://openalex.org/W3108295644","https://openalex.org/W2969228573","https://openalex.org/W4389251353"],"abstract_inverted_index":{"Recently,":[0],"there":[1],"have":[2,131,188],"been":[3],"a":[4,23,160,191,215,233],"plethora":[5],"of":[6,72,82,95,107,123,182,184,199],"classification":[7],"and":[8,155],"detection":[9,27,151,226],"systems":[10,166],"from":[11,29,48],"RGB":[12],"as":[13,15,87,173],"well":[14],"3D":[16,25,56,60,136,144,150],"images.":[17],"In":[18],"this":[19],"work,":[20],"we":[21],"describe":[22],"new":[24],"object":[26],"system":[28,37,97,129,152],"an":[30,141],"RGB-D":[31,205],"or":[32,45],"depth-only":[33],"point":[34],"cloud.":[35],"Our":[36,149],"first":[38],"detects":[39],"objects":[40,57,122],"in":[41,89],"2D":[42,63],"(either":[43],"RGB,":[44],"pseudo-RGB":[46],"constructed":[47],"depth).":[49],"The":[50,92],"next":[51],"step":[52],"is":[53,67,153,212],"to":[54,99,110,115,130,140,165,228],"detect":[55],"within":[58],"the":[59,73,84,108,121,180,185,197,229],"frustums":[61,74,76,86,109],"these":[62],"detections":[64],"define.":[65],"This":[66],"achieved":[68],"by":[69],"voxelizing":[70],"parts":[71,104],"(since":[75],"can":[77,156,177,218],"be":[78,157],"really":[79],"large),":[80],"instead":[81],"using":[83],"whole":[85],"done":[88],"earlier":[90],"work.":[91],"main":[93],"novelty":[94],"our":[96,128,175,200,209],"has":[98],"do":[100,168],"with":[101,224],"determining":[102],"which":[103,211],"(3D":[105],"proposals)":[106],"voxelize,":[111],"thus":[112],"allowing":[113],"us":[114],"provide":[116],"high":[117],"resolution":[118],"representations":[119],"around":[120],"interest.":[124],"It":[125],"also":[126,189],"allows":[127],"reduced":[132],"memory":[133],"requirements.":[134],"These":[135],"proposals":[137],"are":[138],"fed":[139],"efficient":[142],"ResNet-based":[143],"Fully":[145],"Convolutional":[146],"Network":[147],"(FCN).":[148],"fast,":[154],"integrated":[158],"into":[159],"robotics":[161],"platform.":[162],"With":[163],"respect":[164],"that":[167,194,208],"not":[169],"perform":[170],"voxelization":[171],"(such":[172],"PointNet),":[174],"methods":[176],"operate":[178],"without":[179],"requirement":[181],"subsampling":[183],"datasets.":[186],"We":[187],"introduced":[190],"pipelining":[192],"approach":[193],"further":[195],"improves":[196],"efficiency":[198],"system.":[201],"Results":[202],"on":[203,214],"SUN":[204],"dataset":[206],"show":[207],"system,":[210],"based":[213],"small":[216],"network,":[217],"process":[219],"20":[220],"frames":[221],"per":[222],"second":[223],"comparable":[225],"results":[227],"state-of-the-art":[230],"[16],":[231],"achieving":[232],"2\u00d7":[234],"speedup.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
