{"id":"https://openalex.org/W2902846555","doi":"https://doi.org/10.1109/icmew.2018.8551515","title":"2D to 3D Label Propagation For Object Detection In Point Cloud","display_name":"2D to 3D Label Propagation For Object Detection In Point Cloud","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2902846555","doi":"https://doi.org/10.1109/icmew.2018.8551515","mag":"2902846555"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2018.8551515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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/A5083610532","display_name":"Kanokphan Lertniphonphan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kanokphan Lertniphonphan","raw_affiliation_strings":["Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061487218","display_name":"Satoshi Komorita","orcid":"https://orcid.org/0000-0003-1526-5514"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Komorita","raw_affiliation_strings":["Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079581959","display_name":"Kazuyuki Tasaka","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuyuki Tasaka","raw_affiliation_strings":["Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083165705","display_name":"Hiromasa Yanagihara","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiromasa Yanagihara","raw_affiliation_strings":["Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Media Recognition Laboratory, KDDI Research, Inc., Saitama, Japan","institution_ids":["https://openalex.org/I4210164495"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210164495"],"apc_list":null,"apc_paid":null,"fwci":2.6187,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.9058952,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"24","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9991000294685364,"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":0.9991000294685364,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9990000128746033,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/point-cloud","display_name":"Point cloud","score":0.877758800983429},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7405080199241638},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6887246370315552},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6689226627349854},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6299428939819336},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.5958646535873413},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5825269818305969},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5631888508796692},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.522944986820221},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.46033817529678345},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4564391076564789},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4427618980407715},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4396771192550659},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4260070025920868},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.42398083209991455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3669864535331726},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.301535964012146},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.19722512364387512},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11906379461288452},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07030898332595825}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.877758800983429},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7405080199241638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6887246370315552},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6689226627349854},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6299428939819336},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.5958646535873413},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5825269818305969},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5631888508796692},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.522944986820221},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.46033817529678345},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4564391076564789},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4427618980407715},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4396771192550659},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4260070025920868},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.42398083209991455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3669864535331726},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.301535964012146},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.19722512364387512},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11906379461288452},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07030898332595825},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/icmew.2018.8551515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2018.8551515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","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":21,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1861492603","https://openalex.org/W2074658631","https://openalex.org/W2085261163","https://openalex.org/W2115579991","https://openalex.org/W2117539524","https://openalex.org/W2129091674","https://openalex.org/W2149542945","https://openalex.org/W2154458843","https://openalex.org/W2211722331","https://openalex.org/W2481401919","https://openalex.org/W2555618208","https://openalex.org/W2580683366","https://openalex.org/W2613718673","https://openalex.org/W2775701113","https://openalex.org/W2784333415","https://openalex.org/W2964162504","https://openalex.org/W4243493583","https://openalex.org/W6620707391","https://openalex.org/W6639102338","https://openalex.org/W6669223668"],"related_works":["https://openalex.org/W4318751391","https://openalex.org/W4390398949","https://openalex.org/W2364404923","https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W2058718607","https://openalex.org/W2562256921","https://openalex.org/W3102673927","https://openalex.org/W2327954668","https://openalex.org/W2745033168"],"abstract_inverted_index":{"Object":[0],"detection":[1],"and":[2,34,62,97,103,109,124],"classification":[3],"from":[4,29],"LiDAR":[5],"point":[6,30,45,54,84,101,113],"cloud":[7,31,46,55,85,102,114],"is":[8,23,32,56,77],"increasingly":[9],"important":[10],"in":[11],"robotic":[12],"system.":[13],"For":[14],"training":[15,50,127],"a":[16,38,49],"classifier,":[17],"huge":[18],"datasets":[19],"with":[20,93,145],"object":[21,53],"labeling":[22,123],"needed.":[24],"However,":[25],"manually":[26,142],"labeled":[27],"data":[28,128],"time-consuming":[33],"costly.":[35],"We":[36],"present":[37],"framework,":[39],"which":[40],"propagates":[41,81],"image":[42,61],"annotation":[43],"to":[44,82,115,137],"for":[47,64],"making":[48],"data.":[51],"Each":[52],"projected":[57],"on":[58],"the":[59,65,69,74,79,83,131,139],"corresponding":[60],"searches":[63],"overlapping":[66],"area":[67,76],"within":[68],"2D":[70],"bounding":[71],"box.":[72],"If":[73],"occurred":[75],"matched,":[78],"label":[80,125,132],"object.":[86],"While":[87],"previous":[88],"works":[89],"trained":[90,108],"their":[91],"classifier":[92,140],"3D":[94],"ground":[95,143],"truth":[96,144],"tested":[98,110],"by":[99],"combining":[100],"RGB":[104],"image.":[105],"Our":[106],"system":[107],"using":[111,121],"only":[112],"identify":[116],"objects.":[117],"The":[118],"comparison":[119],"between":[120],"manual":[122],"propagation":[126,133],"demonstrate":[129],"that":[130],"can":[134],"be":[135],"used":[136],"train":[138],"without":[141],"80.22%":[146],"mean":[147],"average":[148],"precision.":[149]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}