{"id":"https://openalex.org/W4372346078","doi":"https://doi.org/10.1109/icassp49357.2023.10095208","title":"Graph Wavelet-Based Point Cloud Geometric Denoising with Surface-Consistent Non-Negative Kernel Regression","display_name":"Graph Wavelet-Based Point Cloud Geometric Denoising with Surface-Consistent Non-Negative Kernel Regression","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372346078","doi":"https://doi.org/10.1109/icassp49357.2023.10095208"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10095208","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5060700521","display_name":"Ryosuke Watanabe","orcid":"https://orcid.org/0000-0002-0720-7763"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I2800817003","display_name":"California Southern University","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]},{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Ryosuke Watanabe","raw_affiliation_strings":["University of Southern California","KDDI Research, Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]},{"raw_affiliation_string":"KDDI Research, Inc","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058963825","display_name":"Keisuke Nonaka","orcid":"https://orcid.org/0000-0002-9701-2862"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Keisuke Nonaka","raw_affiliation_strings":["KDDI Research, Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034283584","display_name":"Eduardo Pav\u00e9z","orcid":"https://orcid.org/0000-0001-8985-2872"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I2800817003","display_name":"California Southern University","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eduardo Pavez","raw_affiliation_strings":["University of Southern California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036021612","display_name":"Tatsuya Kobayashi","orcid":"https://orcid.org/0000-0001-5072-6756"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tatsuya Kobayashi","raw_affiliation_strings":["KDDI Research, Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KDDI Research, Inc","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040001106","display_name":"Antonio Ortega","orcid":"https://orcid.org/0000-0001-5403-0940"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I2800817003","display_name":"California Southern University","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonio Ortega","raw_affiliation_strings":["University of Southern California"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3942,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52828423,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9979000091552734,"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":0.9979000091552734,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.996399998664856,"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/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.9750000238418579,"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/point-cloud","display_name":"Point cloud","score":0.8199337720870972},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6500523090362549},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5775219202041626},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5709570646286011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5682618618011475},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4914330244064331},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4842959940433502},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46011075377464294},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4533596932888031},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4314592480659485},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3655458092689514},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12289616465568542},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.08417069911956787}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8199337720870972},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6500523090362549},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5775219202041626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5709570646286011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5682618618011475},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4914330244064331},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4842959940433502},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46011075377464294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4533596932888031},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4314592480659485},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3655458092689514},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12289616465568542},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.08417069911956787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10095208","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10095208","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6100000143051147,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320325628","display_name":"Ministry of Internal Affairs and Communications","ror":"https://ror.org/00vs1pz50"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1753163303","https://openalex.org/W2064855647","https://openalex.org/W2097063526","https://openalex.org/W2101491865","https://openalex.org/W2117007522","https://openalex.org/W2125527601","https://openalex.org/W2148089412","https://openalex.org/W2158787690","https://openalex.org/W2168029744","https://openalex.org/W2404981275","https://openalex.org/W2743427350","https://openalex.org/W2793293836","https://openalex.org/W2890896661","https://openalex.org/W2963026027","https://openalex.org/W2998115525","https://openalex.org/W3015965491","https://openalex.org/W3045749263","https://openalex.org/W3089869138","https://openalex.org/W3099141203","https://openalex.org/W3105126405","https://openalex.org/W3122628962","https://openalex.org/W3133749673","https://openalex.org/W4214806317","https://openalex.org/W4224920101"],"related_works":["https://openalex.org/W4389574804","https://openalex.org/W2382174632","https://openalex.org/W2129959498","https://openalex.org/W3016928466","https://openalex.org/W2784060934","https://openalex.org/W2936725271","https://openalex.org/W2902714807","https://openalex.org/W3150655618","https://openalex.org/W2537489131","https://openalex.org/W2077021924"],"abstract_inverted_index":{"Point":[0],"cloud":[1,15],"applications":[2],"suffer":[3],"from":[4,80],"geometric":[5],"noise":[6,90],"caused":[7],"by":[8,12,98],"measurement":[9],"errors":[10],"induced":[11],"the":[13,54,118],"point":[14,103],"acquisition":[16],"system.":[17],"We":[18],"propose":[19,87],"a":[20,81,88],"novel":[21,89],"graph":[22,43,49],"construction":[23,50],"method,":[24],"surface-consistent":[25],"non-negative":[26],"kernel":[27],"regression":[28],"(SC-NNK),":[29],"that":[30,94,117],"can":[31],"achieve":[32],"more":[33],"accurate":[34],"denoising":[35,66,127],"of":[36],"geometry":[37,65],"information":[38],"in":[39,62,102,106],"combination":[40],"with":[41],"spectral":[42],"wavelet":[44,108],"transforms":[45],"(SGWTs).":[46],"Unlike":[47],"conventional":[48],"methods":[51],"such":[52],"as":[53],"K-nearest":[55],"neighbor":[56],"(KNN),":[57],"which":[58],"have":[59],"been":[60],"adopted":[61],"previous":[63],"SGWT-based":[64],"methods,":[67],"SC-NNK":[68],"graphs":[69],"consider":[70],"geometrical":[71],"and":[72,124],"frequency":[73],"characteristics":[74],"to":[75],"remove":[76],"redundant":[77],"edge":[78],"connections":[79],"KNN":[82],"graph.":[83],"In":[84],"addition,":[85],"we":[86],"level":[91],"estimation":[92],"method":[93,120],"achieves":[95],"improved":[96],"accuracy":[97],"detecting":[99],"flat":[100],"surfaces":[101],"clouds,":[104],"resulting":[105],"better":[107],"shrinkage":[109],"thresholds":[110],"for":[111],"denoising.":[112],"Our":[113],"experimental":[114],"results":[115],"show":[116],"proposed":[119],"outperforms":[121],"recent":[122],"deep-learning-based":[123],"graph-based":[125],"state-of-the-art":[126],"methods.":[128]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}