{"id":"https://openalex.org/W4221155806","doi":"https://doi.org/10.1109/icra46639.2022.9812291","title":"NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields","display_name":"NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields","publication_year":2022,"publication_date":"2022-05-23","ids":{"openalex":"https://openalex.org/W4221155806","doi":"https://doi.org/10.1109/icra46639.2022.9812291"},"language":"en","primary_location":{"id":"doi:10.1109/icra46639.2022.9812291","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra46639.2022.9812291","pdf_url":null,"source":{"id":"https://openalex.org/S4363607759","display_name":"2022 International Conference on Robotics and Automation (ICRA)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/1721.1/153644","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101361764","display_name":"Yen-Chen Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Lin Yen-Chen","raw_affiliation_strings":["MIT"],"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021289514","display_name":"Pete Florence","orcid":"https://orcid.org/0000-0002-7148-5645"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pete Florence","raw_affiliation_strings":["Google"],"affiliations":[{"raw_affiliation_string":"Google","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024117395","display_name":"Jonathan T. Barron","orcid":"https://orcid.org/0009-0009-1274-7500"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan T. Barron","raw_affiliation_strings":["Google"],"affiliations":[{"raw_affiliation_string":"Google","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052768778","display_name":"Tsung-Yi Lin","orcid":"https://orcid.org/0000-0003-4819-0627"},"institutions":[{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tsung-Yi Lin","raw_affiliation_strings":["Nvidia"],"affiliations":[{"raw_affiliation_string":"Nvidia","institution_ids":["https://openalex.org/I1304085615"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100657505","display_name":"Alberto Rodr\u00edguez","orcid":"https://orcid.org/0000-0002-9444-3368"},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Alberto Rodriguez","raw_affiliation_strings":["MIT"],"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017456911","display_name":"Phillip Isola","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109586","display_name":"Moscow Institute of Thermal Technology","ror":"https://ror.org/021es5e59","country_code":"RU","type":"facility","lineage":["https://openalex.org/I4210109586"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Phillip Isola","raw_affiliation_strings":["MIT"],"affiliations":[{"raw_affiliation_string":"MIT","institution_ids":["https://openalex.org/I4210109586"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101361764"],"corresponding_institution_ids":["https://openalex.org/I4210109586"],"apc_list":null,"apc_paid":null,"fwci":4.9544,"has_fulltext":false,"cited_by_count":83,"citation_normalized_percentile":{"value":0.96408801,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6496","last_page":"6503"},"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9997000098228455,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9988999962806702,"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":0.7687104940414429},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6818918585777283},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6777089834213257},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5722270011901855},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4987626075744629},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.49775150418281555},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.44911348819732666},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.44864141941070557},{"id":"https://openalex.org/keywords/radiance","display_name":"Radiance","score":0.43301281332969666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3427921533584595}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7687104940414429},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6818918585777283},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6777089834213257},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5722270011901855},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4987626075744629},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.49775150418281555},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.44911348819732666},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.44864141941070557},{"id":"https://openalex.org/C23690007","wikidata":"https://www.wikidata.org/wiki/Q1411145","display_name":"Radiance","level":2,"score":0.43301281332969666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3427921533584595},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra46639.2022.9812291","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra46639.2022.9812291","pdf_url":null,"source":{"id":"https://openalex.org/S4363607759","display_name":"2022 International Conference on Robotics and Automation (ICRA)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:dspace.mit.edu:1721.1/153644","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/153644","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE","raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/153644","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/153644","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE","raw_type":"http://purl.org/eprint/type/ConferencePaper"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8100000023841858,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1555614281","https://openalex.org/W2117228865","https://openalex.org/W2119605622","https://openalex.org/W2122572959","https://openalex.org/W2151103935","https://openalex.org/W2259424905","https://openalex.org/W2295355433","https://openalex.org/W2471962767","https://openalex.org/W2519683295","https://openalex.org/W2558625610","https://openalex.org/W2604233003","https://openalex.org/W2741885505","https://openalex.org/W2811406147","https://openalex.org/W2950974964","https://openalex.org/W2963150697","https://openalex.org/W2991276239","https://openalex.org/W3013674262","https://openalex.org/W3035477606","https://openalex.org/W3036843665","https://openalex.org/W3043075211","https://openalex.org/W3086412568","https://openalex.org/W3090004508","https://openalex.org/W3109585842","https://openalex.org/W3112108866","https://openalex.org/W3167674261","https://openalex.org/W3177583232","https://openalex.org/W3203518786","https://openalex.org/W4214731463","https://openalex.org/W4214768561","https://openalex.org/W4237648096","https://openalex.org/W4287666213","https://openalex.org/W4288414710","https://openalex.org/W4312969460","https://openalex.org/W6622446577","https://openalex.org/W6730413402","https://openalex.org/W6747043499","https://openalex.org/W6752823625","https://openalex.org/W6764386301","https://openalex.org/W6768067148","https://openalex.org/W6774631009","https://openalex.org/W6780179280","https://openalex.org/W6783308292","https://openalex.org/W6791730830"],"related_works":["https://openalex.org/W2896728493","https://openalex.org/W2392142157","https://openalex.org/W2043512367","https://openalex.org/W4321518006","https://openalex.org/W2331836163","https://openalex.org/W2005276308","https://openalex.org/W1970182911","https://openalex.org/W2349443037","https://openalex.org/W1994657804","https://openalex.org/W2347721387"],"abstract_inverted_index":{"Thin,":[0],"reflective":[1,67,220],"objects":[2,42,62],"such":[3],"as":[4,78,128,157],"forks":[5],"and":[6,123,188,215,219],"whisks":[7],"are":[8,16],"common":[9],"in":[10],"our":[11,173,190],"daily":[12],"lives,":[13],"but":[14],"they":[15],"particularly":[17],"chal-lenging":[18],"for":[19,57,84,131],"robot":[20,86],"perception":[21],"because":[22],"it":[23],"is":[24],"hard":[25],"to":[26,53,103,113,147,159,207],"reconstruct":[27],"them":[28],"using":[29,164],"commodity":[30],"RGB-D":[31],"cameras":[32],"or":[33,66],"multi-view":[34,194],"stereo":[35,195],"techniques.":[36],"While":[37],"traditional":[38],"pipelines":[39],"struggle":[40],"with":[41,63,152,172,193],"like":[43],"these,":[44],"Neural":[45],"Radiance":[46],"Fields":[47],"(NeRFs)":[48],"have":[49],"recently":[50],"been":[51],"shown":[52],"be":[54,101],"remarkably":[55],"effective":[56],"performing":[58],"view":[59],"synthesis":[60],"on":[61],"thin":[64,218],"structures":[65],"materials.":[68],"In":[69,89],"this":[70],"paper":[71],"we":[72,91,199],"explore":[73],"the":[74,137,149,160,201],"use":[75,109,125],"of":[76,82,97,120,136,141,163,211,217],"NeRF":[77,95,112],"a":[79,94,98,133,142,153,165],"new":[80],"source":[81],"supervision":[83],"robust":[85],"vision":[87],"systems.":[88],"particular,":[90],"demonstrate":[92,200],"that":[93],"representation":[96,135],"scene":[99],"can":[100],"used":[102],"train":[104],"dense":[105,115,203],"object":[106],"descriptors.":[107],"We":[108],"an":[110,121],"optimized":[111],"extract":[114],"correspondences":[116,127],"between":[117],"multiple":[118],"views":[119],"object,":[122],"then":[124],"these":[126],"training":[129],"data":[130],"learning":[132],"view-invariant":[134],"object.":[138],"NeRF's":[139],"usage":[140],"density":[143],"field":[144],"allows":[145],"us":[146],"reformulate":[148],"correspondence":[150,169],"problem":[151],"novel":[154],"distribution-of-depths":[155],"formulation,":[156],"opposed":[158],"conventional":[161],"approach":[162],"depth":[166],"map.":[167],"Dense":[168],"models":[170],"supervised":[171,192],"method":[174],"significantly":[175],"outperform":[176,189],"off-the-shelf":[177],"learned":[178,202],"descriptors":[179,204],"by":[180,196],"106%":[181],"(PCK@3px":[182],"metric,":[183],"more":[184],"than":[185],"doubling":[186],"performance)":[187],"baseline":[191],"29%.":[197],"Furthermore,":[198],"enable":[205],"robots":[206],"perform":[208],"accurate":[209],"6-degree":[210],"freedom":[212],"(6-DoF)":[213],"pick":[214],"place":[216],"objects.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":34},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
