{"id":"https://openalex.org/W2963026027","doi":"https://doi.org/10.1109/tsp.2020.2978617","title":"Feature Graph Learning for 3D Point Cloud Denoising","display_name":"Feature Graph Learning for 3D Point Cloud Denoising","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W2963026027","doi":"https://doi.org/10.1109/tsp.2020.2978617","mag":"2963026027"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2020.2978617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.2978617","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1907.09138","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059045087","display_name":"Wei Hu","orcid":"https://orcid.org/0000-0002-9860-0922"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Hu","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9860-0922","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076848346","display_name":"Xiang Gao","orcid":"https://orcid.org/0000-0002-2679-4019"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Gao","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038897476","display_name":"Gene Cheung","orcid":"https://orcid.org/0000-0002-5571-4137"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Gene Cheung","raw_affiliation_strings":["York University, Toronto, Canada"],"raw_orcid":"https://orcid.org/0000-0002-5571-4137","affiliations":[{"raw_affiliation_string":"York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001396675","display_name":"Zongming Guo","orcid":"https://orcid.org/0000-0002-4944-9621"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zongming Guo","raw_affiliation_strings":["Wangxuan Institute of Computer Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4944-9621","affiliations":[{"raw_affiliation_string":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.7931,"has_fulltext":false,"cited_by_count":110,"citation_normalized_percentile":{"value":0.99514108,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"68","issue":null,"first_page":"2841","last_page":"2856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9965999722480774,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9965999722480774,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.9866999983787537,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.6580540537834167},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5611258149147034},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.445748895406723},{"id":"https://openalex.org/keywords/laplacian-smoothing","display_name":"Laplacian smoothing","score":0.44233348965644836},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.440791517496109},{"id":"https://openalex.org/keywords/diagonal","display_name":"Diagonal","score":0.43654417991638184},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39986544847488403},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.35510939359664917},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.20066657662391663},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.11559787392616272}],"concepts":[{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.6580540537834167},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5611258149147034},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.445748895406723},{"id":"https://openalex.org/C100695618","wikidata":"https://www.wikidata.org/wiki/Q6488267","display_name":"Laplacian smoothing","level":4,"score":0.44233348965644836},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.440791517496109},{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.43654417991638184},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39986544847488403},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.35510939359664917},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20066657662391663},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.11559787392616272},{"id":"https://openalex.org/C181145010","wikidata":"https://www.wikidata.org/wiki/Q4418033","display_name":"Mesh generation","level":3,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2020.2978617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2020.2978617","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"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 Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1907.09138","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.09138","pdf_url":"https://arxiv.org/pdf/1907.09138","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1907.09138","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.09138","pdf_url":"https://arxiv.org/pdf/1907.09138","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6100000143051147}],"awards":[{"id":"https://openalex.org/G3604809996","display_name":"\u57fa\u4e8e\u8c31\u56fe\u7406\u8bba\u7684\u591a\u5206\u8fa8\u7387\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7814\u7a76","funder_award_id":"61972009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4203983771","display_name":null,"funder_award_id":"4194080","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"},{"id":"https://openalex.org/G4251215508","display_name":"Graph Spectral Imaging: Sampling, Representation and Restoration","funder_award_id":"rgpas-2019-00110","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"},{"id":"https://openalex.org/G968945561","display_name":"Graph Spectral Imaging: Sampling, Representation and Restoration","funder_award_id":"rgpin-2019-06271","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":115,"referenced_works":["https://openalex.org/W143236119","https://openalex.org/W658512522","https://openalex.org/W940328677","https://openalex.org/W1520053542","https://openalex.org/W1540596182","https://openalex.org/W1544422618","https://openalex.org/W1569530544","https://openalex.org/W1578099820","https://openalex.org/W1753163303","https://openalex.org/W1965106955","https://openalex.org/W1980811840","https://openalex.org/W1987113397","https://openalex.org/W1987985833","https://openalex.org/W1988317275","https://openalex.org/W1989727964","https://openalex.org/W2000081328","https://openalex.org/W2000222149","https://openalex.org/W2004402003","https://openalex.org/W2010473040","https://openalex.org/W2010824638","https://openalex.org/W2016285927","https://openalex.org/W2021982403","https://openalex.org/W2049981393","https://openalex.org/W2050272999","https://openalex.org/W2051142108","https://openalex.org/W2055588122","https://openalex.org/W2056370875","https://openalex.org/W2057926617","https://openalex.org/W2081194966","https://openalex.org/W2083624955","https://openalex.org/W2097073572","https://openalex.org/W2097581234","https://openalex.org/W2097897435","https://openalex.org/W2099244020","https://openalex.org/W2101491865","https://openalex.org/W2105760337","https://openalex.org/W2121886361","https://openalex.org/W2123820077","https://openalex.org/W2128012088","https://openalex.org/W2129107602","https://openalex.org/W2132555912","https://openalex.org/W2133396774","https://openalex.org/W2137221838","https://openalex.org/W2142224912","https://openalex.org/W2144392304","https://openalex.org/W2151128232","https://openalex.org/W2158361880","https://openalex.org/W2169611956","https://openalex.org/W2171265988","https://openalex.org/W2182544397","https://openalex.org/W2184634347","https://openalex.org/W2508691887","https://openalex.org/W2546714150","https://openalex.org/W2553125747","https://openalex.org/W2563408008","https://openalex.org/W2571662414","https://openalex.org/W2610919036","https://openalex.org/W2615556757","https://openalex.org/W2622735597","https://openalex.org/W2623075562","https://openalex.org/W2763081248","https://openalex.org/W2788790154","https://openalex.org/W2796431263","https://openalex.org/W2796728297","https://openalex.org/W2798585159","https://openalex.org/W2811354074","https://openalex.org/W2894310056","https://openalex.org/W2894849414","https://openalex.org/W2897735964","https://openalex.org/W2904160178","https://openalex.org/W2913535645","https://openalex.org/W2919619854","https://openalex.org/W2948729509","https://openalex.org/W2950215113","https://openalex.org/W2959406683","https://openalex.org/W2962759781","https://openalex.org/W2963384510","https://openalex.org/W2963702033","https://openalex.org/W2963756602","https://openalex.org/W2964012239","https://openalex.org/W2964228184","https://openalex.org/W2981341885","https://openalex.org/W2994097903","https://openalex.org/W2998456637","https://openalex.org/W3003775208","https://openalex.org/W3015904785","https://openalex.org/W3091278150","https://openalex.org/W3098834468","https://openalex.org/W3100282875","https://openalex.org/W3102733014","https://openalex.org/W3105198140","https://openalex.org/W3106135392","https://openalex.org/W3137466219","https://openalex.org/W3146210588","https://openalex.org/W4230938240","https://openalex.org/W4238138432","https://openalex.org/W4238253035","https://openalex.org/W4250374778","https://openalex.org/W4252908580","https://openalex.org/W4289107553","https://openalex.org/W4297689879","https://openalex.org/W4297792526","https://openalex.org/W6632267817","https://openalex.org/W6634138237","https://openalex.org/W6676051941","https://openalex.org/W6679719908","https://openalex.org/W6680153307","https://openalex.org/W6682227116","https://openalex.org/W6685999146","https://openalex.org/W6686534882","https://openalex.org/W6689213722","https://openalex.org/W6738675208","https://openalex.org/W6739017038","https://openalex.org/W6739593606","https://openalex.org/W6758920291"],"related_works":["https://openalex.org/W2621954801","https://openalex.org/W1603134999","https://openalex.org/W2365723198","https://openalex.org/W2021824521","https://openalex.org/W4293577752","https://openalex.org/W2962795393","https://openalex.org/W2598517965","https://openalex.org/W4294959758","https://openalex.org/W4390045270","https://openalex.org/W2016843610"],"abstract_inverted_index":{"Identifying":[0],"an":[1,78],"appropriate":[2],"underlying":[3],"graph":[4,15,28,40,81,106,217],"kernel":[5],"that":[6,160],"reflects":[7],"pairwise":[8],"similarities":[9],"is":[10,114],"critical":[11],"in":[12,229,236],"many":[13],"recent":[14],"spectral":[16],"signal":[17,50],"restoration":[18],"schemes,":[19],"including":[20],"image":[21],"denoising,":[22,227],"dequantization,":[23],"and":[24,94,165],"contrast":[25],"enhancement.":[26],"Existing":[27],"learning":[29,218],"algorithms":[30],"compute":[31,77],"the":[32,63,92,105,146,175,221],"most":[33],"likely":[34],"entries":[35,96,127],"of":[36,49,65,84,97,167,178,194,223],"a":[37,46,54,66,85,98,120,156],"properly":[38],"defined":[39],"Laplacian":[41,107],"matrix":[42,101],"L,":[43],"but":[44],"require":[45],"large":[47],"number":[48],"observations":[51],"z's":[52],"for":[53],"stable":[55],"estimate.":[56],"In":[57],"this":[58],"work,":[59],"we":[60,76,89,133,154,173,213],"assume":[61],"instead":[62],"availability":[64],"relevant":[67],"feature":[68,80,86,203,216],"vector":[69,204],"fi":[70,205],"per":[71],"node":[72],"i,":[73],"from":[74,145],"which":[75],"optimal":[79],"via":[82,128,141,187],"optimization":[83],"metric.":[87],"Specifically,":[88],"alternately":[90],"optimize":[91,125,151],"diagonal":[93,126],"off-diagonal":[95,152],"Mahalanobis":[99],"distance":[100],"M":[102,135,171,181],"by":[103],"minimizing":[104],"regularizer":[108],"(GLR)":[109],"zTLz,":[110],"where":[111,132],"edge":[112],"weight":[113],"wi,j=":[115],"exp{-(fi-":[116],"fj)TM(fi-":[117],"fj)},":[118],"given":[119],"single":[121],"observation":[122],"z.":[123],"We":[124],"proximal":[129],"gradient":[130],"(PG),":[131],"constrain":[134,174],"to":[136,182,220,233],"be":[137,183],"positive":[138],"definite":[139],"(PD)":[140],"linear":[142],"inequalities":[143],"derived":[144],"Gershgorin":[147],"circle":[148],"theorem.":[149],"To":[150,169,209],"entries,":[153],"design":[155],"block":[157],"descent":[158],"algorithm":[159,190,219],"iteratively":[161],"optimizes":[162],"one":[163],"row":[164],"column":[166],"M.":[168],"keep":[170],"PD,":[172],"Schur":[176],"complement":[177],"sub-matrix":[179],"M2,2of":[180],"PD":[184],"when":[185,202],"optimizing":[186],"PG.":[188],"Our":[189],"mitigates":[191],"full":[192],"eigen-decomposition":[193],"M,":[195],"thus":[196],"ensuring":[197],"fast":[198],"computation":[199],"speed":[200],"even":[201],"has":[206],"high":[207],"dimension.":[208],"validate":[210],"its":[211],"usefulness,":[212],"apply":[214],"our":[215],"problem":[222],"3D":[224],"point":[225],"cloud":[226],"resulting":[228],"state-of-the-art":[230],"performance":[231],"compared":[232],"competing":[234],"schemes":[235],"extensive":[237],"experiments.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":20},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}