{"id":"https://openalex.org/W4402916424","doi":"https://doi.org/10.1109/icip51287.2024.10647694","title":"FAWN: Floor-and-Walls Normal Regularization for Direct Neural TSDF Reconstruction","display_name":"FAWN: Floor-and-Walls Normal Regularization for Direct Neural TSDF Reconstruction","publication_year":2024,"publication_date":"2024-09-27","ids":{"openalex":"https://openalex.org/W4402916424","doi":"https://doi.org/10.1109/icip51287.2024.10647694"},"language":"en","primary_location":{"id":"doi:10.1109/icip51287.2024.10647694","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10647694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","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/A5103051562","display_name":"Anna Sokolova","orcid":"https://orcid.org/0000-0001-8777-2035"},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anna Sokolova","raw_affiliation_strings":["Samsung Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062803007","display_name":"Anna Vorontsova","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anna Vorontsova","raw_affiliation_strings":["Samsung Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086074077","display_name":"B. M. Gabdullin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bulat Gabdullin","raw_affiliation_strings":["Samsung Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025510210","display_name":"Alexander Limonov","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Limonov","raw_affiliation_strings":["Samsung Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung Research","institution_ids":["https://openalex.org/I4210101778"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210101778"],"apc_list":null,"apc_paid":null,"fwci":0.9935,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77653069,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2215","last_page":"2221"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.7634000182151794,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.7634000182151794,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.6754000186920166,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10776","display_name":"Spinal Fractures and Fixation Techniques","score":0.646399974822998,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5668616890907288},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48212409019470215},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4294980764389038},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35035061836242676},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.32907015085220337}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5668616890907288},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48212409019470215},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4294980764389038},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35035061836242676},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.32907015085220337}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip51287.2024.10647694","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icip51287.2024.10647694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1989476314","https://openalex.org/W2021851106","https://openalex.org/W2058535340","https://openalex.org/W2594519801","https://openalex.org/W2903435684","https://openalex.org/W2980925180","https://openalex.org/W2987505621","https://openalex.org/W2989645276","https://openalex.org/W3109428934","https://openalex.org/W3136301378","https://openalex.org/W3172610252","https://openalex.org/W4226416753","https://openalex.org/W4311801991","https://openalex.org/W4312539440","https://openalex.org/W4313186498","https://openalex.org/W4319299971","https://openalex.org/W4386076194","https://openalex.org/W6774418182","https://openalex.org/W6797770180","https://openalex.org/W6798033281","https://openalex.org/W6798491912","https://openalex.org/W6839182500"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2324615561","https://openalex.org/W2086120259","https://openalex.org/W2390279801","https://openalex.org/W2245170124","https://openalex.org/W2076393078","https://openalex.org/W2358668433","https://openalex.org/W3186982001","https://openalex.org/W1984402782"],"abstract_inverted_index":{"Leveraging":[0],"3D":[1,5,89,107],"semantics":[2,108,126],"for":[3,78,110],"direct":[4],"reconstruction":[6,58,141],"has":[7],"a":[8,22,50,70,88,145],"great":[9],"potential":[10],"yet":[11],"unleashed.":[12],"For":[13],"instance,":[14],"by":[15,64],"assuming":[16],"that":[17,101,122],"walls":[18,66],"are":[19,118],"vertical,":[20],"and":[21,26,34,42,67,72,83,143,153],"floor":[23,68],"is":[24],"planar":[25],"horizontal,":[27],"we":[28,47,134],"can":[29,94],"correct":[30],"distorted":[31],"room":[32],"shapes":[33],"eliminate":[35],"local":[36],"artifacts":[37],"such":[38],"as":[39,87],"holes,":[40],"pits,":[41],"hills.":[43],"In":[44],"this":[45],"paper,":[46],"propose":[48],"FAWN,":[49],"modification":[51,137],"of":[52],"truncated":[53],"signed":[54],"distance":[55],"function":[56],"(TSDF)":[57],"methods,":[59,142],"which":[60],"considers":[61],"scene":[62],"structure":[63],"detecting":[65],"in":[69,148],"scene,":[71],"penalizing":[73],"the":[74,81],"corresponding":[75],"surface":[76],"normals":[77],"deviating":[79],"from":[80],"horizontal":[82],"vertical":[84],"directions.":[85],"Implemented":[86],"sparse":[90],"convolutional":[91],"module,":[92],"FAWN":[93,105],"be":[95],"incorporated":[96],"into":[97],"any":[98],"trainable":[99],"pipeline":[100],"predicts":[102],"TSDF.":[103],"Since":[104],"requires":[106],"only":[109],"training,":[111],"no":[112],"additional":[113],"limitations":[114],"on":[115],"further":[116],"use":[117,125],"imposed.":[119],"We":[120],"demonstrate,":[121],"FAWN-modified":[123],"methods":[124],"more":[127],"effectively,":[128],"than":[129],"existing":[130],"semantic-based":[131],"approaches.":[132],"Besides,":[133],"apply":[135],"our":[136],"to":[138],"state-of-the-art":[139],"TSDF":[140],"demonstrate":[144],"quality":[146],"gain":[147],"SCANNET,":[149],"ICL-NUIM,":[150],"TUM":[151],"RGBD,":[152],"7SCENES":[154],"benchmarks.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}