{"id":"https://openalex.org/W2963045776","doi":"https://doi.org/10.1109/icra.2018.8460184","title":"Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image","display_name":"Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2963045776","doi":"https://doi.org/10.1109/icra.2018.8460184","mag":"2963045776"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2018.8460184","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460184","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 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/A5065452156","display_name":"Fangchang Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]},{"id":"https://openalex.org/I4210143601","display_name":"Decision Systems (United States)","ror":"https://ror.org/0434dpa13","country_code":"US","type":"company","lineage":["https://openalex.org/I4210143601"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Fangchang Ma","raw_affiliation_strings":["The Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"The Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210143601","https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081073767","display_name":"Serta\u00e7 Karaman","orcid":"https://orcid.org/0000-0002-2225-7275"},"institutions":[{"id":"https://openalex.org/I4210143601","display_name":"Decision Systems (United States)","ror":"https://ror.org/0434dpa13","country_code":"US","type":"company","lineage":["https://openalex.org/I4210143601"]},{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sertac Karaman","raw_affiliation_strings":["The Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"The Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210143601","https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5065452156"],"corresponding_institution_ids":["https://openalex.org/I4210143601","https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":32.6571,"has_fulltext":false,"cited_by_count":648,"citation_normalized_percentile":{"value":0.99733238,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4796","last_page":"4803"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","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/T10531","display_name":"Advanced Vision and Imaging","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":0.9983000159263611,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9965999722480774,"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":0.7165034413337708},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6426953077316284},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6315170526504517},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6124076843261719},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43590855598449707},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4245002865791321},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4128755033016205},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35873645544052124},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27582523226737976},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15243694186210632}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7165034413337708},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6426953077316284},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6315170526504517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6124076843261719},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43590855598449707},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4245002865791321},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4128755033016205},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35873645544052124},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27582523226737976},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15243694186210632},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2018.8460184","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460184","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 Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W753012316","https://openalex.org/W1545195129","https://openalex.org/W1612997784","https://openalex.org/W1905829557","https://openalex.org/W1967027087","https://openalex.org/W2005441409","https://openalex.org/W2074254947","https://openalex.org/W2083047701","https://openalex.org/W2095705004","https://openalex.org/W2117539524","https://openalex.org/W2123043875","https://openalex.org/W2125416623","https://openalex.org/W2132947399","https://openalex.org/W2150066425","https://openalex.org/W2158211626","https://openalex.org/W2194775991","https://openalex.org/W2300779272","https://openalex.org/W2410664773","https://openalex.org/W2436453945","https://openalex.org/W2520707372","https://openalex.org/W2554243884","https://openalex.org/W2566430556","https://openalex.org/W2593414960","https://openalex.org/W2593751597","https://openalex.org/W2609883120","https://openalex.org/W2951234442","https://openalex.org/W2963359474","https://openalex.org/W2963591054","https://openalex.org/W4211015534","https://openalex.org/W6622239757","https://openalex.org/W6669028136","https://openalex.org/W6674330103","https://openalex.org/W6678596876","https://openalex.org/W6681934335","https://openalex.org/W6683067110","https://openalex.org/W6685261749","https://openalex.org/W6697658144","https://openalex.org/W6726644806","https://openalex.org/W6734308328","https://openalex.org/W6734560617","https://openalex.org/W6736677999"],"related_works":["https://openalex.org/W2102148524","https://openalex.org/W200819717","https://openalex.org/W2770593030","https://openalex.org/W2032269556","https://openalex.org/W1991834176","https://openalex.org/W2944448661","https://openalex.org/W2064421702","https://openalex.org/W4253756925","https://openalex.org/W2805523177","https://openalex.org/W4391053327"],"abstract_inverted_index":{"We":[0,66,144],"consider":[1],"the":[2,68,80,86,106,115,122,129,141,149],"problem":[3],"of":[4,12,37,70,88,90,108,131,148],"dense":[5,162],"depth":[6,13,21,45,54,91,112],"prediction":[7,94,116,133],"from":[8,23,79,134],"a":[9,16,34,52,71,152],"sparse":[10,44,159],"set":[11],"measurements":[14],"and":[15,30,39,62,84,164,176],"single":[17,72],"RGB":[18,104],"image.":[19],"Since":[20],"estimation":[22],"monocular":[24],"images":[25],"alone":[26],"is":[27],"inherently":[28],"ambiguous":[29],"unreliable,":[31],"to":[32,76,101,137,157,161],"attain":[33],"higher":[35],"level":[36],"robustness":[38],"accuracy,":[40],"we":[41],"introduce":[42],"additional":[43],"samples,":[46],"which":[47],"are":[48,186],"either":[49],"acquired":[50],"with":[51],"low-resolution":[53],"sensor":[55],"or":[56],"computed":[57],"via":[58],"visual":[59],"Simultaneous":[60],"Localization":[61],"Mapping":[63],"(SLAM)":[64],"algorithms.":[65],"propose":[67],"use":[69],"deep":[73],"regression":[74],"network":[75],"learn":[77],"directly":[78],"RGB-D":[81],"raw":[82],"data,":[83],"explore":[85],"impact":[87],"number":[89],"samples":[92,113],"on":[93,121,140],"accuracy.":[95],"Our":[96],"experiments":[97],"show":[98],"that,":[99],"compared":[100],"using":[102],"only":[103],"images,":[105],"addition":[107],"100":[109],"spatially":[110],"random":[111],"reduces":[114],"root-mean-square":[117],"error":[118],"by":[119],"50%":[120],"NYU-Depth-v2":[123],"indoor":[124],"dataset.":[125,143],"It":[126],"also":[127],"boosts":[128],"percentage":[130],"reliable":[132],"59":[135],"%":[136,139],"92":[138],"KITTI":[142],"demonstrate":[145],"two":[146],"applications":[147],"proposed":[150],"algorithm:":[151],"plug-in":[153],"module":[154],"in":[155],"SLAM":[156],"convert":[158],"maps":[160],"maps,":[163],"super-resolution":[165],"for":[166],"LiDARs.":[167],"Software":[168],"<sup":[169,172,179,182],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[170,173,180,183],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[171,174],"https://github.com/fangchangma/sparse-to-dense":[175],"video":[177],"demonstration":[178],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</sup>":[181,184],"https://www.youtube.com/watch?v=vNIIT_M7\u00d77Y":[185],"publicly":[187],"available.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":51},{"year":2024,"cited_by_count":77},{"year":2023,"cited_by_count":94},{"year":2022,"cited_by_count":112},{"year":2021,"cited_by_count":101},{"year":2020,"cited_by_count":93},{"year":2019,"cited_by_count":84},{"year":2018,"cited_by_count":29}],"updated_date":"2026-03-29T08:15:47.926485","created_date":"2025-10-10T00:00:00"}