{"id":"https://openalex.org/W7124180428","doi":"https://doi.org/10.1109/codit66093.2025.11321591","title":"Evaluation of Dense Differential Filter to Detect Semantic Edges for Estimating 3D Room Structure","display_name":"Evaluation of Dense Differential Filter to Detect Semantic Edges for Estimating 3D Room Structure","publication_year":2025,"publication_date":"2025-07-15","ids":{"openalex":"https://openalex.org/W7124180428","doi":"https://doi.org/10.1109/codit66093.2025.11321591"},"language":null,"primary_location":{"id":"doi:10.1109/codit66093.2025.11321591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit66093.2025.11321591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 11th International Conference on Control, Decision and Information Technologies (CoDIT)","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/A5050918343","display_name":"Marin Wada","orcid":null},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Marin Wada","raw_affiliation_strings":["Meiji University,Graduate School of Science and Technology,Department of Computer Science,Kawasaki,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University,Graduate School of Science and Technology,Department of Computer Science,Kawasaki,Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123006149","display_name":"Kae Nakayama","orcid":null},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kae Nakayama","raw_affiliation_strings":["Meiji University,Graduate School of Science and Technology,Department of Computer Science,Kawasaki,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University,Graduate School of Science and Technology,Department of Computer Science,Kawasaki,Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027856843","display_name":"Junya Morioka","orcid":null},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Junya Morioka","raw_affiliation_strings":["Meiji University,Graduate School of Science and Technology,Department of Computer Science,Kawasaki,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University,Graduate School of Science and Technology,Department of Computer Science,Kawasaki,Japan","institution_ids":["https://openalex.org/I16656306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035420223","display_name":"Ryusuke Miyamoto","orcid":"https://orcid.org/0000-0001-9450-4493"},"institutions":[{"id":"https://openalex.org/I16656306","display_name":"Meiji University","ror":"https://ror.org/02rqvrp93","country_code":"JP","type":"education","lineage":["https://openalex.org/I16656306"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryusuke Miyamoto","raw_affiliation_strings":["Meiji University,School of Science and Technology,Department of Computer Science,Kawasaki,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meiji University,School of Science and Technology,Department of Computer Science,Kawasaki,Japan","institution_ids":["https://openalex.org/I16656306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16656306"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.59787077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2946","last_page":"2951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.2599000036716461,"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":0.2599000036716461,"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":0.24150000512599945,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.11050000041723251,"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/filter","display_name":"Filter (signal processing)","score":0.6664999723434448},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6399000287055969},{"id":"https://openalex.org/keywords/cuboid","display_name":"Cuboid","score":0.5371000170707703},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45320001244544983},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4392000138759613},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4262000024318695},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4250999987125397},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.40529999136924744}],"concepts":[{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.6664999723434448},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6399000287055969},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5529000163078308},{"id":"https://openalex.org/C203527163","wikidata":"https://www.wikidata.org/wiki/Q262959","display_name":"Cuboid","level":2,"score":0.5371000170707703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5195000171661377},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4302000105381012},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4262000024318695},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4250999987125397},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39890000224113464},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3662000000476837},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3626999855041504},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.35600000619888306},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C22597639","wikidata":"https://www.wikidata.org/wiki/Q5449227","display_name":"Filter design","level":3,"score":0.29910001158714294},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2930999994277954},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/codit66093.2025.11321591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit66093.2025.11321591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 11th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6818113923072815,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2022686119","https://openalex.org/W2065301447","https://openalex.org/W2074777933","https://openalex.org/W2081021369","https://openalex.org/W2095844239","https://openalex.org/W2098633863","https://openalex.org/W2099244020","https://openalex.org/W2100470273","https://openalex.org/W2117539524","https://openalex.org/W2145023731","https://openalex.org/W2159386181","https://openalex.org/W2179352600","https://openalex.org/W2560023338","https://openalex.org/W2604146181","https://openalex.org/W2909151280","https://openalex.org/W3094502228","https://openalex.org/W3211490618","https://openalex.org/W4385245566","https://openalex.org/W4386076222","https://openalex.org/W4391095126","https://openalex.org/W4395481595","https://openalex.org/W4401361788","https://openalex.org/W4402727359","https://openalex.org/W4403535007","https://openalex.org/W4413799550","https://openalex.org/W4415798746"],"related_works":[],"abstract_inverted_index":{"The":[0,23],"authors":[1],"attempt":[2],"to":[3,98,103],"actualize":[4],"3D":[5],"reconstruction":[6],"from":[7,88],"a":[8,13,51,57,99,115,120,159],"single":[9],"view":[10],"for":[11,174],"previewing":[12],"room":[14,46,52,70],"in":[15,43,147],"virtual":[16],"space":[17],"using":[18,140,163],"results":[19,139],"of":[20,37,68,79,111,150],"semantic":[21,66,100,116],"segmentation.":[22],"segmentation":[24],"accuracy":[25,149],"has":[26],"been":[27],"drastically":[28],"improved":[29],"by":[30,95],"state-of-the-art":[31],"method,":[32],"which":[33],"enables":[34],"pixel-wise":[35],"classification":[36],"walls,":[38],"floors,":[39],"ceilings,":[40],"and":[41],"objects":[42],"the":[44,69,76,80,84,109,134,148,165,168],"target":[45],"with":[47,158],"sufficient":[48],"accuracy.":[49],"Assuming":[50],"can":[53,61],"be":[54,62],"represented":[55],"as":[56,130],"cuboid,":[58],"its":[59],"parameters":[60],"computed":[63],"analytically":[64],"when":[65,154],"edges":[67,92],"structure":[71],"are":[72,86,93],"accurately":[73],"obtained.":[74],"In":[75],"actual":[77],"process":[78,170],"estimation,":[81],"lines":[82],"constructing":[83],"cuboid":[85],"estimated":[87],"detected":[89],"edges.":[90],"These":[91],"derived":[94],"spatial":[96],"filtering":[97],"map":[101],"corresponding":[102],"an":[104],"input":[105],"image.":[106],"To":[107],"enhance":[108],"effectiveness":[110],"edge":[112],"detection":[113],"on":[114],"map,":[117],"we":[118],"adopted":[119],"simple":[121],"differential":[122],"filter":[123,131,136],"that":[124],"incorporates":[125],"only":[126],"two":[127],"active":[128],"values":[129],"coefficients,":[132],"utilizing":[133],"smallest":[135],"size.":[137],"Experimental":[138],"synthetic":[141],"datasets":[142],"showed":[143],"no":[144],"significant":[145],"difference":[146],"line":[151],"parameter":[152,175],"estimation":[153,176],"comparing":[155],"our":[156],"method":[157],"typical":[160],"filter,":[161],"despite":[162],"half":[164],"samples":[166],"during":[167],"optimization":[169],"though":[171],"sample":[172],"numbers":[173],"became":[177],"smaller":[178],"obviously.":[179]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-01-15T00:00:00"}
