{"id":"https://openalex.org/W3107378443","doi":"https://doi.org/10.1109/jstsp.2020.3047471","title":"Learning Robust Graph-Convolutional Representations for Point Cloud Denoising","display_name":"Learning Robust Graph-Convolutional Representations for Point Cloud Denoising","publication_year":2020,"publication_date":"2020-12-25","ids":{"openalex":"https://openalex.org/W3107378443","doi":"https://doi.org/10.1109/jstsp.2020.3047471","mag":"3107378443"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2020.3047471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3047471","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-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/A5016675020","display_name":"Francesca Pistilli","orcid":"https://orcid.org/0000-0001-9372-032X"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Francesca Pistilli","raw_affiliation_strings":["Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062328236","display_name":"Giulia Fracastoro","orcid":"https://orcid.org/0000-0001-8495-1097"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giulia Fracastoro","raw_affiliation_strings":["Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058182410","display_name":"Diego Valsesia","orcid":"https://orcid.org/0000-0003-1997-2910"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Diego Valsesia","raw_affiliation_strings":["Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034486931","display_name":"Enrico Magli","orcid":"https://orcid.org/0000-0002-0901-0251"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Politecnico di Torino","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Enrico Magli","raw_affiliation_strings":["Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino - Department of Electronics, and Telecommunications, Torino, TO, Italy","institution_ids":["https://openalex.org/I177477856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5016675020"],"corresponding_institution_ids":["https://openalex.org/I177477856"],"apc_list":null,"apc_paid":null,"fwci":3.4099,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.93457315,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"15","issue":"2","first_page":"402","last_page":"414"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.9972000122070312,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9959999918937683,"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/point-cloud","display_name":"Point cloud","score":0.8682950735092163},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7044883966445923},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.64980149269104},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6109309792518616},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5914796590805054},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5818349719047546},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5137856602668762},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4911961257457733},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4728199243545532},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4681397080421448},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4245193898677826},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23428824543952942},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15660253167152405}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8682950735092163},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7044883966445923},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.64980149269104},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6109309792518616},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5914796590805054},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5818349719047546},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5137856602668762},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4911961257457733},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4728199243545532},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4681397080421448},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4245193898677826},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23428824543952942},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15660253167152405},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstsp.2020.3047471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3047471","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":81,"referenced_works":["https://openalex.org/W156975732","https://openalex.org/W637153065","https://openalex.org/W1019830208","https://openalex.org/W1569530544","https://openalex.org/W1753163303","https://openalex.org/W1987113397","https://openalex.org/W2000081328","https://openalex.org/W2004402003","https://openalex.org/W2010473040","https://openalex.org/W2037642501","https://openalex.org/W2052790699","https://openalex.org/W2072723786","https://openalex.org/W2081194966","https://openalex.org/W2101491865","https://openalex.org/W2137531922","https://openalex.org/W2138821028","https://openalex.org/W2169611956","https://openalex.org/W2190691619","https://openalex.org/W2508457857","https://openalex.org/W2519887557","https://openalex.org/W2546714150","https://openalex.org/W2558460151","https://openalex.org/W2558748708","https://openalex.org/W2560609797","https://openalex.org/W2566809385","https://openalex.org/W2606202972","https://openalex.org/W2617121149","https://openalex.org/W2624431344","https://openalex.org/W2791092480","https://openalex.org/W2793293836","https://openalex.org/W2805163084","https://openalex.org/W2805499196","https://openalex.org/W2904160178","https://openalex.org/W2906788812","https://openalex.org/W2910792243","https://openalex.org/W2940235493","https://openalex.org/W2950642167","https://openalex.org/W2962711740","https://openalex.org/W2962737939","https://openalex.org/W2962767366","https://openalex.org/W2963197375","https://openalex.org/W2963756602","https://openalex.org/W2963858333","https://openalex.org/W2964015378","https://openalex.org/W2964113829","https://openalex.org/W2964253597","https://openalex.org/W2964321699","https://openalex.org/W2970279440","https://openalex.org/W2979750740","https://openalex.org/W2981199548","https://openalex.org/W2981479471","https://openalex.org/W2998456637","https://openalex.org/W3016124664","https://openalex.org/W3045749263","https://openalex.org/W3048092834","https://openalex.org/W3099141203","https://openalex.org/W3102733014","https://openalex.org/W3104591796","https://openalex.org/W3133749673","https://openalex.org/W3137369665","https://openalex.org/W3137466219","https://openalex.org/W4285723986","https://openalex.org/W4289107553","https://openalex.org/W4294558607","https://openalex.org/W4394671432","https://openalex.org/W6620673361","https://openalex.org/W6634138237","https://openalex.org/W6685350579","https://openalex.org/W6687484953","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6731768253","https://openalex.org/W6738964360","https://openalex.org/W6752237900","https://openalex.org/W6754929296","https://openalex.org/W6757069382","https://openalex.org/W6758371058","https://openalex.org/W6761698645","https://openalex.org/W6763422710","https://openalex.org/W6775947557","https://openalex.org/W6780526740"],"related_works":["https://openalex.org/W3006513224","https://openalex.org/W2046456988","https://openalex.org/W4293226380","https://openalex.org/W2357409937","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Point":[0],"clouds":[1],"are":[2,11],"an":[3],"increasingly":[4],"relevant":[5],"geometric":[6],"data":[7],"type":[8],"but":[9],"they":[10],"often":[12],"corrupted":[13],"by":[14,18,82],"noise":[15,113],"and":[16,36,62,75,115],"affected":[17],"the":[19,46,59,63,90,95,108],"presence":[20,117],"of":[21,45,68,80,94,111,118],"outliers.":[22],"We":[23],"propose":[24],"a":[25,33,40,50],"deep":[26],"learning":[27],"method":[28,48],"that":[29],"can":[30,76],"simultaneously":[31],"denoise":[32],"point":[34,69],"cloud":[35],"remove":[37],"outliers":[38],"in":[39,107,116],"single":[41],"model.":[42],"The":[43,71,97],"core":[44],"proposed":[47,98],"is":[49,73],"graph-convolutional":[51],"neural":[52],"network":[53,72],"able":[54],"to":[55],"efficiently":[56],"deal":[57],"with":[58],"irregular":[60],"domain":[61],"permutation":[64],"invariance":[65],"problem":[66],"typical":[67],"clouds.":[70],"fully-convolutional":[74],"build":[77],"complex":[78],"hierarchies":[79],"features":[81],"dynamically":[83],"constructing":[84],"neighborhood":[85],"graphs":[86],"from":[87],"similarity":[88],"among":[89],"high-dimensional":[91],"feature":[92],"representations":[93],"points.":[96],"approach":[99],"outperforms":[100],"state-of-the-art":[101],"denoising":[102],"methods":[103],"showing":[104],"robust":[105],"performance":[106],"challenging":[109],"setup":[110],"high":[112],"levels":[114],"structured":[119],"noise.":[120]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}