{"id":"https://openalex.org/W3131191845","doi":"https://doi.org/10.1109/iros45743.2020.9341549","title":"SpoxelNet: Spherical Voxel-based Deep Place Recognition for 3D Point Clouds of Crowded Indoor Spaces","display_name":"SpoxelNet: Spherical Voxel-based Deep Place Recognition for 3D Point Clouds of Crowded Indoor Spaces","publication_year":2020,"publication_date":"2020-10-24","ids":{"openalex":"https://openalex.org/W3131191845","doi":"https://doi.org/10.1109/iros45743.2020.9341549","mag":"3131191845"},"language":"en","primary_location":{"id":"doi:10.1109/iros45743.2020.9341549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5109857455","display_name":"Min Young Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]},{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Min Young Chang","raw_affiliation_strings":["Columbia University","Research Intern of NAVER LABS, Seongnam-si, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Research Intern of NAVER LABS, Seongnam-si, South Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026059665","display_name":"Suyong Yeon","orcid":null},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Suyong Yeon","raw_affiliation_strings":["Researchers of NAVER LABS, Seongnam-si, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Researchers of NAVER LABS, Seongnam-si, South Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111160373","display_name":"Soohyun Ryu","orcid":null},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Soohyun Ryu","raw_affiliation_strings":["Researchers of NAVER LABS, Seongnam-si, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Researchers of NAVER LABS, Seongnam-si, South Korea","institution_ids":["https://openalex.org/I60922564"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100654311","display_name":"Donghwan Lee","orcid":"https://orcid.org/0000-0001-6809-4371"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Donghwan Lee","raw_affiliation_strings":["Researchers of NAVER LABS, Seongnam-si, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Researchers of NAVER LABS, Seongnam-si, South Korea","institution_ids":["https://openalex.org/I60922564"]}]}],"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":29,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8564","last_page":"8570"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9990000128746033,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.8756048083305359},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7920548915863037},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7013164758682251},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6795341372489929},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5486016869544983},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5130811929702759},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.501880407333374},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48268771171569824},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44995787739753723},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4415231943130493},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.43579578399658203},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4269927740097046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1300923228263855},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10896605253219604}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8756048083305359},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7920548915863037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7013164758682251},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6795341372489929},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5486016869544983},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5130811929702759},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.501880407333374},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48268771171569824},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44995787739753723},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4415231943130493},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.43579578399658203},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4269927740097046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1300923228263855},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10896605253219604},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros45743.2020.9341549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5799999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1591449811","https://openalex.org/W1988322066","https://openalex.org/W2028000700","https://openalex.org/W2041469568","https://openalex.org/W2045162826","https://openalex.org/W2131821152","https://openalex.org/W2150066425","https://openalex.org/W2211722331","https://openalex.org/W2213518912","https://openalex.org/W2284029970","https://openalex.org/W2527172881","https://openalex.org/W2558027072","https://openalex.org/W2560609797","https://openalex.org/W2564922936","https://openalex.org/W2565998037","https://openalex.org/W2620629206","https://openalex.org/W2790141132","https://openalex.org/W2798278946","https://openalex.org/W2902610326","https://openalex.org/W2910489334","https://openalex.org/W2914921243","https://openalex.org/W2951019013","https://openalex.org/W2963046720","https://openalex.org/W2963708168","https://openalex.org/W2963727135","https://openalex.org/W2973871604","https://openalex.org/W2976327573","https://openalex.org/W2986519585","https://openalex.org/W3003651427","https://openalex.org/W3099455272","https://openalex.org/W6695582405","https://openalex.org/W6763422710","https://openalex.org/W6768569881"],"related_works":["https://openalex.org/W3027020613","https://openalex.org/W2016533837","https://openalex.org/W3167885074","https://openalex.org/W2892386716","https://openalex.org/W4306164210","https://openalex.org/W1998563493","https://openalex.org/W4313316311","https://openalex.org/W4362608745","https://openalex.org/W2082728368","https://openalex.org/W2383143032"],"abstract_inverted_index":{"With":[0],"its":[1,129],"essential":[2],"role":[3],"in":[4,38,48,86,96,120,131,145],"achieving":[5],"full":[6],"autonomy":[7],"of":[8,52,56,82,93],"robot":[9],"navigation,":[10],"place":[11,72],"recognition":[12,73],"has":[13],"been":[14,30],"widely":[15],"studied":[16],"with":[17,25,32,153],"various":[18,146],"approaches.":[19],"Recently,":[20],"numerous":[21],"point":[22,70,84],"cloud-based":[23,71],"methods":[24,152],"deep":[26],"learning":[27],"implementation":[28],"have":[29],"proposed":[31],"promising":[33,47],"results":[34],"for":[35,75],"their":[36,42],"application":[37],"outdoor":[39],"environments.":[40],"However,":[41],"performances":[43],"are":[44],"not":[45],"as":[46],"indoor":[49,77,147],"spaces":[50],"because":[51],"the":[53,91,117],"high":[54],"level":[55],"occlusion":[57],"caused":[58],"by":[59,115,134],"structures":[60],"and":[61,89,123,149],"moving":[62],"objects.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67],"propose":[68],"a":[69,103,154],"method":[74,80,142],"crowded":[76],"spaces.":[78],"The":[79],"consists":[81],"voxelizing":[83],"clouds":[85],"spherical":[87],"coordinates":[88],"defining":[90],"occupancy":[92],"each":[94],"voxel":[95],"ternary":[97],"values.":[98],"We":[99],"also":[100,127],"present":[101],"SpoxelNet,":[102],"neural":[104],"network":[105],"architecture":[106],"that":[107],"encodes":[108],"input":[109],"voxels":[110],"into":[111],"global":[112],"descriptor":[113],"vectors":[114,137],"extracting":[116],"structural":[118],"features":[119],"both":[121],"fine":[122],"coarse":[124],"scales.":[125],"It":[126],"reinforces":[128],"performance":[130],"occluded":[132],"places":[133],"concatenating":[135],"feature":[136],"from":[138],"multiple":[139],"directions.":[140],"Our":[141],"is":[143],"evaluated":[144],"datasets":[148],"outperforms":[150],"existing":[151],"large":[155],"margin.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}