{"id":"https://openalex.org/W3199814225","doi":"https://doi.org/10.1109/iros51168.2021.9636489","title":"KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation","display_name":"KDFNet: Learning Keypoint Distance Field for 6D Object Pose Estimation","publication_year":2021,"publication_date":"2021-09-27","ids":{"openalex":"https://openalex.org/W3199814225","doi":"https://doi.org/10.1109/iros51168.2021.9636489","mag":"3199814225"},"language":"en","primary_location":{"id":"doi:10.1109/iros51168.2021.9636489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros51168.2021.9636489","pdf_url":null,"source":{"id":"https://openalex.org/S4363607734","display_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":"2021 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/A5101765869","display_name":"Xingyu Liu","orcid":"https://orcid.org/0000-0002-6353-4304"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xingyu Liu","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056342945","display_name":"Shun Iwase","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shun Iwase","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037322163","display_name":"Kris Kitani","orcid":"https://orcid.org/0000-0002-9389-4060"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kris M. Kitani","raw_affiliation_strings":["Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":4.0499,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.95697188,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4631","last_page":"4638"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10653","display_name":"Robot Manipulation and Learning","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10653","display_name":"Robot Manipulation and Learning","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9986000061035156,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9948999881744385,"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":0.7695402503013611},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.7358826994895935},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6326663494110107},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6295310854911804},{"id":"https://openalex.org/keywords/ransac","display_name":"RANSAC","score":0.5396409630775452},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5314936637878418},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.42408865690231323},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4240681529045105},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.42266660928726196},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3987694978713989},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19661852717399597}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7695402503013611},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.7358826994895935},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6326663494110107},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6295310854911804},{"id":"https://openalex.org/C114744707","wikidata":"https://www.wikidata.org/wiki/Q218533","display_name":"RANSAC","level":3,"score":0.5396409630775452},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5314936637878418},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.42408865690231323},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4240681529045105},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.42266660928726196},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3987694978713989},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19661852717399597}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros51168.2021.9636489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros51168.2021.9636489","pdf_url":null,"source":{"id":"https://openalex.org/S4363607734","display_name":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W132147841","https://openalex.org/W639708223","https://openalex.org/W1576725826","https://openalex.org/W1969868017","https://openalex.org/W2107793967","https://openalex.org/W2194775991","https://openalex.org/W2200124539","https://openalex.org/W2488101876","https://openalex.org/W2600447016","https://openalex.org/W2613718673","https://openalex.org/W2797184202","https://openalex.org/W2797394534","https://openalex.org/W2812468425","https://openalex.org/W2886335102","https://openalex.org/W2889600678","https://openalex.org/W2949911710","https://openalex.org/W2952069407","https://openalex.org/W2962771259","https://openalex.org/W2963188159","https://openalex.org/W2963598138","https://openalex.org/W2963756608","https://openalex.org/W2963903710","https://openalex.org/W2964249569","https://openalex.org/W2981378444","https://openalex.org/W2988715931","https://openalex.org/W2989604896","https://openalex.org/W3000681136","https://openalex.org/W3009516594","https://openalex.org/W3034986117","https://openalex.org/W3035235920","https://openalex.org/W3035355652","https://openalex.org/W3100052745","https://openalex.org/W3176888779","https://openalex.org/W6605377543","https://openalex.org/W6620707391","https://openalex.org/W6752936729","https://openalex.org/W6753494528","https://openalex.org/W6754416773"],"related_works":["https://openalex.org/W2131378265","https://openalex.org/W2984240274","https://openalex.org/W4229588126","https://openalex.org/W3171899115","https://openalex.org/W1018308721","https://openalex.org/W3126266918","https://openalex.org/W4229059082","https://openalex.org/W4390872624","https://openalex.org/W4320086129","https://openalex.org/W2950785639"],"abstract_inverted_index":{"We":[0,109,152],"present":[1],"KDFNet,":[2],"a":[3,31,69,85,104,111,136,149],"novel":[4,70],"method":[5,166,201],"for":[6,35,78,120],"6D":[7,208],"object":[8,129],"pose":[9,36,209],"estimation":[10],"from":[11],"RGB":[12],"images.":[13],"To":[14,63],"handle":[15,50],"occlusion,":[16],"many":[17],"recent":[18],"works":[19],"have":[20],"proposed":[21,165,200],"to":[22,116,134,140,204],"localize":[23,141],"2D":[24,80,86,95,107],"keypoints":[25,143],"through":[26],"pixel-wise":[27],"voting":[28,44,138],"and":[29,48,52,103,181,195],"solve":[30],"Perspective-n-Point":[32],"(PnP)":[33],"problem":[34],"estimation,":[37],"which":[38],"achieves":[39,167],"leading":[40],"performance.":[41],"However,":[42],"such":[43],"process":[45],"is":[46,202],"direction-based":[47],"cannot":[49,59],"long":[51],"thin":[53],"objects":[54],"where":[55],"the":[56,91,94,99,118,142,154,199,207],"direction":[57],"intersections":[58,147],"be":[60],"robustly":[61,205],"found.":[62],"address":[64],"this":[65,124],"problem,":[66],"we":[67,132],"propose":[68,133],"continuous":[71],"representation":[72],"called":[73],"Keypoint":[74],"Distance":[75],"Field":[76],"(KDF)":[77],"projected":[79,106,128],"keypoint":[81,130],"locations.":[82],"Formulated":[83],"as":[84],"array,":[87],"each":[88,121],"element":[89],"of":[90,127,157,179,191],"KDF":[92,119,125],"stores":[93],"Euclidean":[96],"distance":[97],"between":[98],"corresponding":[100],"image":[101],"pixel":[102],"specified":[105],"keypoint.":[108,122],"use":[110,135],"fully":[112],"convolutional":[113],"neural":[114],"network":[115],"regress":[117],"Using":[123],"encoding":[126],"locations,":[131],"distance-based":[137],"scheme":[139],"by":[144,160],"calculating":[145],"circle":[146],"in":[148,210],"RANSAC":[150],"fashion.":[151],"validate":[153],"design":[155],"choices":[156],"our":[158],"framework":[159],"extensive":[161],"ablation":[162],"experiments.":[163],"Our":[164],"state-of-the-art":[168],"performance":[169],"on":[170],"Occlusion":[171],"LINEMOD":[172],"dataset":[173,183],"with":[174,186],"an":[175,187],"average":[176,188],"ADD(-S)":[177],"accuracy":[178,190],"50.3%":[180],"TOD":[182],"mug":[184],"subset":[185],"ADD":[189],"75.72%.":[192],"Extensive":[193],"experiments":[194],"visualizations":[196],"demonstrate":[197],"that":[198],"able":[203],"estimate":[206],"challenging":[211],"scenarios":[212],"including":[213],"occlusion.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
