{"id":"https://openalex.org/W2033979122","doi":"https://doi.org/10.1109/icra.2014.6907236","title":"Dense 3D semantic mapping of indoor scenes from RGB-D images","display_name":"Dense 3D semantic mapping of indoor scenes from RGB-D images","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W2033979122","doi":"https://doi.org/10.1109/icra.2014.6907236","mag":"2033979122"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2014.6907236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2014.6907236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Robotics and Automation (ICRA)","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/A5071563379","display_name":"Alexander Hermans","orcid":"https://orcid.org/0000-0003-2127-0782"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander Hermans","raw_affiliation_strings":["Computer Vision Group, RWTH Aachen University","[Computer Vision Group, RWTH Aachen University]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision Group, RWTH Aachen University","institution_ids":["https://openalex.org/I887968799"]},{"raw_affiliation_string":"[Computer Vision Group, RWTH Aachen University]","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Georgios Floros","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Georgios Floros","raw_affiliation_strings":["Computer Vision Group, RWTH Aachen University","[Computer Vision Group, RWTH Aachen University]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision Group, RWTH Aachen University","institution_ids":["https://openalex.org/I887968799"]},{"raw_affiliation_string":"[Computer Vision Group, RWTH Aachen University]","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071006649","display_name":"Bastian Leibe","orcid":"https://orcid.org/0000-0003-4225-0051"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bastian Leibe","raw_affiliation_strings":["Computer Vision Group, RWTH Aachen University","[Computer Vision Group, RWTH Aachen University]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Vision Group, RWTH Aachen University","institution_ids":["https://openalex.org/I887968799"]},{"raw_affiliation_string":"[Computer Vision Group, RWTH Aachen University]","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":272,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2631","last_page":"2638"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9997000098228455,"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":0.9997000098228455,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.9986000061035156,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8249129056930542},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7780110836029053},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6815270185470581},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.6758381128311157},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5348317623138428},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5250765681266785},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4792916178703308},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45360904932022095},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4503898322582245},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.44532105326652527},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.41555255651474},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4146330952644348},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.382251113653183}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8249129056930542},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7780110836029053},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6815270185470581},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.6758381128311157},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5348317623138428},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5250765681266785},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4792916178703308},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45360904932022095},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4503898322582245},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.44532105326652527},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.41555255651474},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4146330952644348},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.382251113653183},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2014.6907236","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2014.6907236","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.rwth-aachen.de:443315","is_oa":false,"landing_page_url":"https://publications.rwth-aachen.de/record/443315","pdf_url":null,"source":{"id":"https://openalex.org/S4306401362","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2014 IEEE International Conference on Robotics and Automation IEEE ICRA 2014 : Conference digest. Conference digest / 2014 IEEE International Conference on Robotics and Automation IEEE ICRA 2014 : Hong Kong, China, 31 May - 7 June, 2014<br/>2014 IEEE International Conference on Robotics and Automation IEEE, ICRA 2014, Hong Kong, Hong Kong, 2014-05-31 - 2014-06-07","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W170770739","https://openalex.org/W1501192708","https://openalex.org/W1565409700","https://openalex.org/W1755850324","https://openalex.org/W1964502729","https://openalex.org/W1997538891","https://openalex.org/W2000063120","https://openalex.org/W2021851106","https://openalex.org/W2022153165","https://openalex.org/W2045587041","https://openalex.org/W2047639605","https://openalex.org/W2054279472","https://openalex.org/W2056132907","https://openalex.org/W2060280062","https://openalex.org/W2066813062","https://openalex.org/W2067912884","https://openalex.org/W2073660060","https://openalex.org/W2085411191","https://openalex.org/W2099405522","https://openalex.org/W2100588357","https://openalex.org/W2125337786","https://openalex.org/W2161236525","https://openalex.org/W2172156083","https://openalex.org/W2401617459","https://openalex.org/W2509493982","https://openalex.org/W2535516436","https://openalex.org/W2952793010","https://openalex.org/W2963038646","https://openalex.org/W4294240437","https://openalex.org/W6605121731","https://openalex.org/W6606812440","https://openalex.org/W6629098493","https://openalex.org/W6633668584","https://openalex.org/W6638172780","https://openalex.org/W6666899075","https://openalex.org/W6675120363","https://openalex.org/W6724925041"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2093471820","https://openalex.org/W50079190","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2487162673","https://openalex.org/W2942366970","https://openalex.org/W2793211469","https://openalex.org/W2805329439","https://openalex.org/W2963336968"],"abstract_inverted_index":{"Dense":[0],"semantic":[1,16,34,51,82,89,103,122,135],"segmentation":[2,17,35,52,104,123],"of":[3,26,32,40,91],"3D":[4,50,71,88,136],"point":[5],"clouds":[6],"is":[7,116],"a":[8,38,59,86,100,121,128,153],"challenging":[9],"task.":[10],"Many":[11],"approaches":[12],"deal":[13,48],"with":[14,49],"2D":[15,81,102],"and":[18,68,147],"can":[19,151],"obtain":[20,120,152],"impressive":[21],"results.":[22],"With":[23],"the":[24,30,54],"availability":[25],"cheap":[27],"RGB-D":[28],"sensors":[29],"field":[31],"indoor":[33,92],"has":[36],"seen":[37],"lot":[39],"progress.":[41],"Still":[42],"it":[43,115],"remains":[44],"unclear":[45],"how":[46],"to":[47,79,84,119,132,157],"in":[53,127,130],"best":[55],"way.":[56],"We":[57,138],"propose":[58,99],"novel":[60],"2D-3D":[61],"label":[62],"transfer":[63],"based":[64,106],"on":[65,107,142],"Bayesian":[66],"updates":[67],"dense":[69],"pairwise":[70],"Conditional":[72],"Random":[73],"Fields.":[74],"This":[75],"approach":[76,105,141],"allows":[77],"us":[78],"use":[80],"segmentations":[83],"create":[85,133],"consistent":[87],"reconstruction":[90],"scenes.":[93],"To":[94],"this":[95],"end,":[96],"we":[97,112,150],"also":[98],"fast":[101],"Randomized":[108],"Decision":[109],"Forests.":[110],"Furthermore,":[111],"show":[113,148],"that":[114,149],"not":[117],"needed":[118],"for":[124],"every":[125],"frame":[126],"sequence":[129],"order":[131],"accurate":[134],"reconstructions.":[137],"evaluate":[139],"our":[140],"both":[143],"NYU":[144],"Depth":[145],"datasets":[146],"significant":[154],"speed-up":[155],"compared":[156],"other":[158],"methods.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":29},{"year":2019,"cited_by_count":40},{"year":2018,"cited_by_count":35},{"year":2017,"cited_by_count":42},{"year":2016,"cited_by_count":29},{"year":2015,"cited_by_count":18},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
