{"id":"https://openalex.org/W3089979486","doi":"https://doi.org/10.1109/icra40945.2020.9196663","title":"Probabilistic TSDF Fusion Using Bayesian Deep Learning for Dense 3D Reconstruction with a Single RGB Camera","display_name":"Probabilistic TSDF Fusion Using Bayesian Deep Learning for Dense 3D Reconstruction with a Single RGB Camera","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3089979486","doi":"https://doi.org/10.1109/icra40945.2020.9196663","mag":"3089979486"},"language":"en","primary_location":{"id":"doi:10.1109/icra40945.2020.9196663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9196663","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","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/A5002382496","display_name":"Hanjun Kim","orcid":"https://orcid.org/0000-0002-7371-7366"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hanjun Kim","raw_affiliation_strings":["Automation and Systems Research Institute (ASRI), Seoul National University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Automation and Systems Research Institute (ASRI), Seoul National University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103174678","display_name":"Beom-Hee Lee","orcid":"https://orcid.org/0000-0001-9264-8952"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Beomhee Lee","raw_affiliation_strings":["Automation and Systems Research Institute (ASRI), Seoul National University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Automation and Systems Research Institute (ASRI), Seoul National University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002382496"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.1962,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.50662356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"8623","last_page":"8629"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9997000098228455,"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":0.9994000196456909,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7529220581054688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7285393476486206},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7284958362579346},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6702026128768921},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6324543952941895},{"id":"https://openalex.org/keywords/3d-reconstruction","display_name":"3D reconstruction","score":0.5041536092758179},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4819643199443817},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.479521781206131},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4638146758079529},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.4372454285621643},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43562179803848267},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4048318564891815},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36141979694366455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3528524339199066},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15905001759529114},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09836503863334656}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7529220581054688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7285393476486206},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7284958362579346},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6702026128768921},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6324543952941895},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.5041536092758179},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4819643199443817},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.479521781206131},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4638146758079529},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.4372454285621643},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43562179803848267},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4048318564891815},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36141979694366455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3528524339199066},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15905001759529114},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09836503863334656}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra40945.2020.9196663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9196663","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"mag:3217542189","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002273806808107","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W582134693","https://openalex.org/W601603264","https://openalex.org/W1522301498","https://openalex.org/W1579853615","https://openalex.org/W1765589589","https://openalex.org/W1899042909","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1905829557","https://openalex.org/W1915250530","https://openalex.org/W1979266466","https://openalex.org/W1987648924","https://openalex.org/W1992178727","https://openalex.org/W2009422376","https://openalex.org/W2016285927","https://openalex.org/W2058535340","https://openalex.org/W2074254947","https://openalex.org/W2103328396","https://openalex.org/W2111959010","https://openalex.org/W2124907686","https://openalex.org/W2125416623","https://openalex.org/W2171740948","https://openalex.org/W2243794092","https://openalex.org/W2414078574","https://openalex.org/W2436453945","https://openalex.org/W2544243493","https://openalex.org/W2559597482","https://openalex.org/W2582996697","https://openalex.org/W2600383743","https://openalex.org/W2606794968","https://openalex.org/W2621274416","https://openalex.org/W2760112017","https://openalex.org/W2908723075","https://openalex.org/W2951234442","https://openalex.org/W2963045776","https://openalex.org/W2963446712","https://openalex.org/W2963591054","https://openalex.org/W2964059111","https://openalex.org/W2964121744","https://openalex.org/W2964144363","https://openalex.org/W6605121731","https://openalex.org/W6617145748","https://openalex.org/W6618476874","https://openalex.org/W6631190155","https://openalex.org/W6634817459","https://openalex.org/W6639666111","https://openalex.org/W6639824700","https://openalex.org/W6675415620","https://openalex.org/W6678569853","https://openalex.org/W6685261749","https://openalex.org/W6732781371","https://openalex.org/W6735443497"],"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/W2383143032","https://openalex.org/W4206138983"],"abstract_inverted_index":{"In":[0],"this":[1,88],"paper,":[2],"we":[3,62,90,170],"address":[4,87],"a":[5,13,73,92,132],"3D":[6,40,60,120,147,165],"reconstruction":[7,41,121,148,166],"problem":[8],"using":[9,167],"depth":[10,24,49,56,139,162],"prediction":[11,25,57,163],"from":[12,149],"single":[14],"RGB":[15],"image.":[16],"With":[17],"the":[18,32,46,53,124,150,159,177,180],"recent":[19],"advances":[20],"in":[21,108,114],"deep":[22,65,70],"learning,":[23],"shows":[26],"high":[27],"performance.":[28],"However,":[29],"due":[30],"to":[31,45,81,137],"discrepancy":[33],"between":[34],"training":[35],"environment":[36],"and":[37,78,101,144,164,175],"test":[38],"environment,":[39],"can":[42,141],"be":[43,142],"vulnerable":[44],"uncertainty":[47,54,111],"of":[48,55,76,98,126,161,179],"prediction.":[50],"To":[51,86,157],"consider":[52],"for":[58,118],"robust":[59,136],"reconstruction,":[61],"adopt":[63],"Bayesian":[64,69,94],"learning":[66,71],"framework.":[67],"Conventional":[68],"requires":[72],"large":[74],"amount":[75],"time":[77],"GPU":[79],"memory":[80],"perform":[82],"Monte":[83],"Carlo":[84],"sampling.":[85],"problem,":[89],"propose":[91],"lightweight":[93],"neural":[95],"network":[96],"consisting":[97],"U-net":[99],"structure":[100],"summation-based":[102],"skip":[103],"connections,":[104],"which":[105],"is":[106,112,153],"performed":[107],"real-time.":[109],"Estimated":[110],"utilized":[113,171],"probabilistic":[115],"TSDF":[116,127,135,152],"fusion":[117],"dense":[119,146],"by":[122],"maximizing":[123],"posterior":[125],"value":[128],"per":[129],"voxel.":[130],"As":[131],"result,":[133],"global":[134,151],"erroneous":[138],"values":[140],"obtained":[143],"then":[145],"achievable":[154],"more":[155],"accurately.":[156],"evaluate":[158],"performance":[160],"our":[168],"method,":[169],"two":[172],"official":[173],"datasets":[174],"demonstrated":[176],"outperformance":[178],"proposed":[181],"method":[182],"over":[183],"other":[184],"conventional":[185],"methods.":[186]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}