{"id":"https://openalex.org/W4206814852","doi":"https://doi.org/10.1109/m2vip49856.2021.9665046","title":"Room Layout Estimation by Learning Depth Maps of Planes from 2D Layout Labels","display_name":"Room Layout Estimation by Learning Depth Maps of Planes from 2D Layout Labels","publication_year":2021,"publication_date":"2021-11-26","ids":{"openalex":"https://openalex.org/W4206814852","doi":"https://doi.org/10.1109/m2vip49856.2021.9665046"},"language":"en","primary_location":{"id":"doi:10.1109/m2vip49856.2021.9665046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip49856.2021.9665046","pdf_url":null,"source":{"id":"https://openalex.org/S4363608277","display_name":"2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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/A5100428755","display_name":"Weidong Zhang","orcid":"https://orcid.org/0000-0003-0081-930X"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Weidong Zhang","raw_affiliation_strings":["School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100414124","display_name":"Ying Liu","orcid":"https://orcid.org/0000-0002-0337-277X"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Liu","raw_affiliation_strings":["School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100428755"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.0653,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38335253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"777","last_page":"782"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.998199999332428,"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":0.998199999332428,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9907000064849854,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7424685955047607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7403705716133118},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6036895513534546},{"id":"https://openalex.org/keywords/ground-plane","display_name":"Ground plane","score":0.5937875509262085},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5675216317176819},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5577726364135742},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5438653826713562},{"id":"https://openalex.org/keywords/cuboid","display_name":"Cuboid","score":0.5065022706985474},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.4938817620277405},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.48978352546691895},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.46452581882476807},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.45656925439834595},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3544612228870392},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.12093663215637207},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11763417720794678},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0842958390712738}],"concepts":[{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7424685955047607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7403705716133118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6036895513534546},{"id":"https://openalex.org/C88764893","wikidata":"https://www.wikidata.org/wiki/Q1547722","display_name":"Ground plane","level":3,"score":0.5937875509262085},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5675216317176819},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5577726364135742},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5438653826713562},{"id":"https://openalex.org/C203527163","wikidata":"https://www.wikidata.org/wiki/Q262959","display_name":"Cuboid","level":2,"score":0.5065022706985474},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.4938817620277405},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.48978352546691895},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.46452581882476807},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.45656925439834595},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3544612228870392},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.12093663215637207},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11763417720794678},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0842958390712738},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C21822782","wikidata":"https://www.wikidata.org/wiki/Q131214","display_name":"Antenna (radio)","level":2,"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/m2vip49856.2021.9665046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip49856.2021.9665046","pdf_url":null,"source":{"id":"https://openalex.org/S4363608277","display_name":"2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W29210613","https://openalex.org/W1898543992","https://openalex.org/W1965834447","https://openalex.org/W1991817626","https://openalex.org/W2007492000","https://openalex.org/W2011857665","https://openalex.org/W2068397385","https://openalex.org/W2072736233","https://openalex.org/W2126836862","https://openalex.org/W2152380633","https://openalex.org/W2156802865","https://openalex.org/W2168439642","https://openalex.org/W2174266739","https://openalex.org/W2199215860","https://openalex.org/W2298113822","https://openalex.org/W2395611524","https://openalex.org/W2439650069","https://openalex.org/W2534523274","https://openalex.org/W2545706078","https://openalex.org/W2598108580","https://openalex.org/W2744404335","https://openalex.org/W2789326270","https://openalex.org/W2899771611","https://openalex.org/W2917562521","https://openalex.org/W2962717701","https://openalex.org/W2962966483","https://openalex.org/W2963591013","https://openalex.org/W2963911235","https://openalex.org/W2964030239","https://openalex.org/W3000471755","https://openalex.org/W3004335748","https://openalex.org/W3007533900","https://openalex.org/W3041751223","https://openalex.org/W3097432995","https://openalex.org/W3099126293","https://openalex.org/W3112777757","https://openalex.org/W3125097483","https://openalex.org/W4250952223","https://openalex.org/W6639895262","https://openalex.org/W6682761374","https://openalex.org/W6685556374","https://openalex.org/W6721025048","https://openalex.org/W6756040250","https://openalex.org/W6757540954","https://openalex.org/W6763016451","https://openalex.org/W6773713915","https://openalex.org/W6786313952"],"related_works":["https://openalex.org/W2387231455","https://openalex.org/W2036512149","https://openalex.org/W2329792127","https://openalex.org/W2388061575","https://openalex.org/W3099045389","https://openalex.org/W2374882918","https://openalex.org/W1991123685","https://openalex.org/W3102673927","https://openalex.org/W2327954668","https://openalex.org/W2003805688"],"abstract_inverted_index":{"Room":[0],"layout":[1,24,67,104,132,144,152,158,172,185],"estimation":[2,25],"is":[3,26,48,68,77],"a":[4,14,113],"basic":[5],"mission":[6],"in":[7,74],"understanding":[8],"the":[9,20,46,51,54,60,66,85,97,118,142,149,161,167],"indoor":[10,30,56],"scene":[11],"and":[12,40,88,131,157,171,187],"has":[13],"wide":[15],"range":[16],"of":[17,22,53,65,100,129],"applications.":[18],"Specifically,":[19],"task":[21],"room":[23],"to":[27,49,71,116,125,145],"segment":[28],"an":[29,90],"RGB":[31],"image":[32,162],"with":[33,148],"semantic":[34],"surface":[35,119,169],"labels,":[36],"i.e.,":[37],"ceiling,":[38],"floor,":[39],"walls.":[41],"A":[42],"straightforward":[43],"solution":[44],"for":[45],"problem":[47],"estimate":[50],"geometry":[52],"dominant":[55],"planes.":[57],"However,":[58],"since":[59],"ground":[61,107,150],"truth":[62,108,151],"depth":[63,98,109,127,189],"maps":[64,99,128,190],"not":[69],"easy":[70],"obtain,":[72],"research":[73],"this":[75,80],"direction":[76],"rare.":[78],"In":[79],"paper,":[81],"we":[82,135],"focus":[83],"on":[84,160,191],"cuboid":[86],"rooms":[87],"propose":[89,136],"effective":[91],"learning":[92],"framework":[93],"that":[94,140,166,179],"can":[95,122,182],"learn":[96,117],"planes":[101,130],"from":[102],"2D":[103],"labels":[105],"without":[106],"maps.":[110],"We":[111],"employ":[112],"deep":[114],"network":[115],"parameters,":[120],"which":[121],"be":[123,146],"used":[124],"produce":[126,183],"results.":[133],"Then":[134],"stereo":[137],"supervision":[138],"mechanism":[139],"encourages":[141],"generated":[143],"consistent":[147],"segmentation":[153],"along":[154],"Z":[155],"axis":[156],"edge":[159],"plane":[163],"simultaneously,":[164],"so":[165],"learned":[168],"parameters":[170],"results":[173,177],"are":[174],"reasonable.":[175],"Experimental":[176],"show":[178],"our":[180],"method":[181],"high-quality":[184],"estimates":[186],"corresponding":[188],"benchmark":[192],"datasets.":[193]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}