{"id":"https://openalex.org/W2773456899","doi":"https://doi.org/10.1109/iros.2017.8202207","title":"3D object instance recognition and pose estimation using triplet loss with dynamic margin","display_name":"3D object instance recognition and pose estimation using triplet loss with dynamic margin","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2773456899","doi":"https://doi.org/10.1109/iros.2017.8202207","mag":"2773456899"},"language":"en","primary_location":{"id":"doi:10.1109/iros.2017.8202207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2017.8202207","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1904.04854","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102739061","display_name":"Sergey Zakharov","orcid":"https://orcid.org/0000-0002-6231-6137"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]},{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sergey Zakharov","raw_affiliation_strings":["Siemens AG, Munich, Germany","Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens AG, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]},{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000146074","display_name":"Wadim Kehl","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Wadim Kehl","raw_affiliation_strings":["Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068780385","display_name":"Benjamin Planche","orcid":"https://orcid.org/0000-0002-6110-6437"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Benjamin Planche","raw_affiliation_strings":["Siemens AG, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens AG, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018221262","display_name":"Andreas Hutter","orcid":"https://orcid.org/0000-0002-5682-2009"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Hutter","raw_affiliation_strings":["Siemens AG, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens AG, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045382199","display_name":"Slobodan Ili\u0107","orcid":"https://orcid.org/0000-0002-3413-1936"},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]},{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Slobodan Ilic","raw_affiliation_strings":["Siemens AG, Munich, Germany","Technical University of Munich, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens AG, Munich, Germany","institution_ids":["https://openalex.org/I1325886976"]},{"raw_affiliation_string":"Technical University of Munich, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":73.3729,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.99756524,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"552","last_page":"559"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9988999962806702,"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":0.9986000061035156,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9986000061035156,"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/computer-science","display_name":"Computer science","score":0.7924526333808899},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.7346848249435425},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7221869230270386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.707517147064209},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5611314177513123},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5158756971359253},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4979062080383301},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4909849464893341},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4904806315898895},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45857009291648865},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.4511609673500061},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4353821873664856},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.431968629360199},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38284310698509216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3204536437988281},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3093031942844391}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7924526333808899},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.7346848249435425},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7221869230270386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.707517147064209},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5611314177513123},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5158756971359253},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4979062080383301},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4909849464893341},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4904806315898895},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45857009291648865},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.4511609673500061},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4353821873664856},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.431968629360199},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38284310698509216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3204536437988281},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3093031942844391},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/iros.2017.8202207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros.2017.8202207","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1904.04854","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.04854","pdf_url":"https://arxiv.org/pdf/1904.04854","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:mediatum.ub.tum.de:node/1631154","is_oa":false,"landing_page_url":"https://mediatum.ub.tum.de/1631154","pdf_url":null,"source":{"id":"https://openalex.org/S4306400453","display_name":"mediaTUM \u2013 the media and publications repository of the Technical University Munich (Technical University Munich)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I62916508","host_organization_name":"Technical University of Munich","host_organization_lineage":["https://openalex.org/I62916508"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1904.04854","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.04854","pdf_url":"https://arxiv.org/pdf/1904.04854","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1482825550","https://openalex.org/W1526868886","https://openalex.org/W1591870335","https://openalex.org/W1644641054","https://openalex.org/W1909903157","https://openalex.org/W1920022804","https://openalex.org/W2005756025","https://openalex.org/W2009178416","https://openalex.org/W2025064394","https://openalex.org/W2087193308","https://openalex.org/W2101025736","https://openalex.org/W2101199297","https://openalex.org/W2102605133","https://openalex.org/W2138621090","https://openalex.org/W2163605009","https://openalex.org/W2211722331","https://openalex.org/W2504204199","https://openalex.org/W2520005737","https://openalex.org/W2963775347","https://openalex.org/W3103919331","https://openalex.org/W6628991564","https://openalex.org/W6631711059","https://openalex.org/W6640300118","https://openalex.org/W6684191040","https://openalex.org/W6724310229"],"related_works":["https://openalex.org/W2130674020","https://openalex.org/W2093748878","https://openalex.org/W2333771223","https://openalex.org/W2120056845","https://openalex.org/W1981531423","https://openalex.org/W4394861761","https://openalex.org/W2035264131","https://openalex.org/W1679012645","https://openalex.org/W1925461966","https://openalex.org/W3036468168"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,36,101,184],"address":[4],"the":[5,27,42,46,84,96,103,110,114,168,178,186,189,192,203,208],"problem":[6],"of":[7,15,31,61,95,180,188,202,210],"3D":[8],"object":[9,145],"instance":[10],"recognition":[11],"and":[12,92,128,154],"pose":[13],"estimation":[14],"localized":[16],"objects":[17,63],"in":[18,45],"cluttered":[19],"environments":[20],"using":[21],"convolutional":[22],"neural":[23],"networks.":[24],"Inspired":[25],"by":[26,109],"descriptor":[28,71],"learning":[29,48],"approach":[30],"Wohlhart":[32],"et":[33],"al.":[34],"[1],":[35],"propose":[37],"a":[38,53,68],"method":[39],"that":[40,121,162],"introduces":[41],"dynamic":[43,85],"margin":[44,86],"manifold":[47],"triplet":[49],"loss":[50,54],"function.":[51],"Such":[52],"function":[55],"is":[56],"designed":[57],"to":[58,67,113,123,133,148,176,197,207],"map":[59],"images":[60],"different":[62,65],"under":[64],"poses":[66],"lower-dimensional,":[69],"similarity-preserving":[70],"space":[72],"on":[73,191],"which":[74],"efficient":[75,158],"nearest":[76],"neighbor":[77],"search":[78],"algorithms":[79],"can":[80],"be":[81],"applied.":[82],"Introducing":[83],"allows":[87,163],"for":[88,164],"faster":[89],"training":[90,169],"times":[91],"better":[93,124,149,165],"accuracy":[94,130],"resulting":[97],"low-dimensional":[98],"manifolds.":[99],"Furthermore,":[100],"contribute":[102],"following:":[104],"adding":[105,135],"in-plane":[106],"rotations":[107],"(ignored":[108],"baseline":[111],"method)":[112],"training,":[115],"proposing":[116],"new":[117],"background":[118],"noise":[119],"types":[120],"help":[122],"mimic":[125],"realistic":[126],"scenarios":[127],"improve":[129],"with":[131,205],"respect":[132,206],"clutter,":[134],"surface":[136,146],"normals":[137],"as":[138],"another":[139],"powerful":[140],"image":[141],"modality":[142],"representing":[143],"an":[144,157,173],"leading":[147],"performance":[150,187],"than":[151],"merely":[152],"depth,":[153],"finally":[155],"implementing":[156],"online":[159],"batch":[160],"generation":[161],"variability":[166],"during":[167],"phase.":[170],"We":[171],"perform":[172],"exhaustive":[174],"evaluation":[175],"demonstrate":[177,198],"effects":[179],"our":[181],"contributions.":[182],"Additionally,":[183],"assess":[185],"algorithm":[190],"large":[193],"BigBIRD":[194],"dataset":[195],"[2]":[196],"good":[199],"scalability":[200],"properties":[201],"pipeline":[204],"number":[209],"models.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":2}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
