{"id":"https://openalex.org/W3133018862","doi":"https://doi.org/10.1109/iros45743.2020.9341769","title":"Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP","display_name":"Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP","publication_year":2020,"publication_date":"2020-10-24","ids":{"openalex":"https://openalex.org/W3133018862","doi":"https://doi.org/10.1109/iros45743.2020.9341769","mag":"3133018862"},"language":"en","primary_location":{"id":"doi:10.1109/iros45743.2020.9341769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341769","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":"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/A5049607975","display_name":"Sungho Yoon","orcid":"https://orcid.org/0000-0002-5801-9279"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungho Yoon","raw_affiliation_strings":["Robotics Program, KAIST, Daejeon, S. Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Program, KAIST, Daejeon, S. Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740100","display_name":"Ayoung Kim","orcid":"https://orcid.org/0000-0001-9829-2408"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ayoung Kim","raw_affiliation_strings":["Department of Civil and Environmental Engineering, KAIST, Daejeon, S. Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, KAIST, Daejeon, S. Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7831,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.75397981,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"10468","last_page":"10475"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.998199999332428,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.756500244140625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6849673986434937},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.6014095544815063},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5256287455558777},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5206515192985535},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5103904604911804},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49909472465515137},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.4678661823272705},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4663239121437073},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.464111864566803},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4481743574142456},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.4307599365711212},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3834906816482544},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19904333353042603},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.1762685477733612},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.122953861951828}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.756500244140625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6849673986434937},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.6014095544815063},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5256287455558777},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5206515192985535},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5103904604911804},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49909472465515137},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.4678661823272705},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4663239121437073},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.464111864566803},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4481743574142456},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.4307599365711212},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3834906816482544},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19904333353042603},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.1762685477733612},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.122953861951828},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros45743.2020.9341769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros45743.2020.9341769","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1702387805","https://openalex.org/W1992178727","https://openalex.org/W2047736581","https://openalex.org/W2125310925","https://openalex.org/W2520707372","https://openalex.org/W2593414960","https://openalex.org/W2593751597","https://openalex.org/W2606794968","https://openalex.org/W2788817933","https://openalex.org/W2830339951","https://openalex.org/W2885093229","https://openalex.org/W2893995718","https://openalex.org/W2897203992","https://openalex.org/W2899479761","https://openalex.org/W2948647700","https://openalex.org/W2949644579","https://openalex.org/W2961926014","https://openalex.org/W2963045776","https://openalex.org/W2963190151","https://openalex.org/W2963316641","https://openalex.org/W2963488291","https://openalex.org/W2963835689","https://openalex.org/W2963867516","https://openalex.org/W2964254721","https://openalex.org/W2964326562","https://openalex.org/W2968288611","https://openalex.org/W2969202876","https://openalex.org/W2970971581","https://openalex.org/W2985775862","https://openalex.org/W2990946490","https://openalex.org/W2998031326","https://openalex.org/W4295312788","https://openalex.org/W6637338928","https://openalex.org/W6734308328","https://openalex.org/W6745263924","https://openalex.org/W6748325268","https://openalex.org/W6752497527","https://openalex.org/W6752865273","https://openalex.org/W6752951365","https://openalex.org/W6753806659","https://openalex.org/W6755011199","https://openalex.org/W6755463424","https://openalex.org/W6755647638","https://openalex.org/W6755789610","https://openalex.org/W6756174191","https://openalex.org/W6766978945","https://openalex.org/W6769663443"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W4285326088","https://openalex.org/W1993996173","https://openalex.org/W2055080101","https://openalex.org/W3200148366","https://openalex.org/W4286247444"],"abstract_inverted_index":{"Estimating":[0],"a":[1,29,50,104,133],"dense":[2,88],"and":[3,14,68,89,116,123,167,186,197],"accurate":[4,58,90],"depth":[5,23,39,81,91,105,114,117,147],"map":[6,82,92],"is":[7],"the":[8,76,85,97,111,120,154,169,184],"key":[9],"requirement":[10],"for":[11,163,195],"autonomous":[12],"driving":[13],"robotics.":[15],"Recent":[16],"advances":[17],"in":[18,25],"deep":[19],"learning":[20],"have":[21,43],"allowed":[22],"estimation":[24,40,52],"full":[26,79],"resolution":[27,80],"from":[28,65,172],"single":[30],"image.":[31],"Despite":[32],"this":[33,94],"impressive":[34],"result,":[35],"many":[36,55],"deep-learning-based":[37],"monocular":[38],"(MDE)":[41],"algorithms":[42],"failed":[44],"to":[45,178],"keep":[46],"their":[47],"accuracy":[48,185],"yielding":[49],"meter-level":[51],"error.":[53],"In":[54],"robotics":[56],"applications,":[57],"but":[59],"sparse":[60,149,196],"measurements":[61],"are":[62,73],"readily":[63],"available":[64],"Light":[66],"Detection":[67],"Ranging":[69],"(LiDAR).":[70],"Although":[71],"they":[72],"highly":[74],"accurate,":[75],"sparsity":[77],"limits":[78],"reconstruction.":[83],"Targeting":[84],"problem":[86,108],"of":[87,99,113,157,188],"recovery,":[93],"paper":[95],"introduces":[96],"fusion":[98],"these":[100],"two":[101],"modalities":[102],"as":[103],"completion":[106],"(DC)":[107],"by":[109,144],"dividing":[110],"role":[112],"inference":[115],"regression.":[118],"Utilizing":[119],"state-of-the-art":[121,192],"MDE":[122,142],"our":[124,189],"Gaussian":[125],"process":[126],"(GP)":[127],"based":[128],"depth-regression":[129],"method,":[130],"we":[131,159],"propose":[132],"general":[134],"solution":[135],"that":[136,183],"can":[137],"flexibly":[138],"work":[139],"with":[140,148],"various":[141],"modules":[143],"enhancing":[145],"its":[146],"range":[150],"measurements.":[151,199],"To":[152],"overcome":[153],"major":[155],"limitation":[156],"GP,":[158],"adopt":[160],"Kernel":[161],"Interpolation":[162],"Scalable":[164],"Structured":[165],"(KISS)-GP":[166],"mitigate":[168],"computational":[170],"complexity":[171],"O(N":[173],"<sup":[174],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[175],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</sup>":[176],")":[177],"O(N).":[179],"Our":[180],"experiments":[181],"demonstrate":[182],"robustness":[187],"method":[190],"outperform":[191],"unsupervised":[193],"methods":[194],"biased":[198]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}