{"id":"https://openalex.org/W4416513212","doi":"https://doi.org/10.1109/tvcg.2025.3635138","title":"LaPDA: Latent-Space Point Cloud Denoising With Adaptivity","display_name":"LaPDA: Latent-Space Point Cloud Denoising With Adaptivity","publication_year":2025,"publication_date":"2025-11-21","ids":{"openalex":"https://openalex.org/W4416513212","doi":"https://doi.org/10.1109/tvcg.2025.3635138","pmid":"https://pubmed.ncbi.nlm.nih.gov/41269811"},"language":"en","primary_location":{"id":"doi:10.1109/tvcg.2025.3635138","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvcg.2025.3635138","pdf_url":null,"source":{"id":"https://openalex.org/S84775595","display_name":"IEEE Transactions on Visualization and Computer Graphics","issn_l":"1077-2626","issn":["1077-2626","1941-0506","2160-9306"],"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 Visualization and Computer Graphics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Peng Du","orcid":"https://orcid.org/0009-0007-9016-1842"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Du","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-9016-1842","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024691662","display_name":"Xingce Wang","orcid":"https://orcid.org/0000-0002-3177-8902"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingce Wang","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3177-8902","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101833322","display_name":"Zhongke Wu","orcid":"https://orcid.org/0000-0003-3735-6476"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongke Wu","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3735-6476","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001355713","display_name":"Xudong Ru","orcid":"https://orcid.org/0000-0003-1502-1712"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xudong Ru","raw_affiliation_strings":["School of Artificial Intelligence, Beijing Normal University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1502-1712","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027315622","display_name":"Xavier Granier","orcid":"https://orcid.org/0000-0003-1349-2297"},"institutions":[{"id":"https://openalex.org/I4210091746","display_name":"Institut d\u2019Optique Graduate School","ror":"https://ror.org/00d0rke27","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210091746"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Xavier Granier","raw_affiliation_strings":["Institut d’Optique Graduate School, Paris, France","Institut d'Optique Graduate School, Paris, France"],"raw_orcid":"https://orcid.org/0000-0003-1349-2297","affiliations":[{"raw_affiliation_string":"Institut d’Optique Graduate School, Paris, France","institution_ids":["https://openalex.org/I4210091746"]},{"raw_affiliation_string":"Institut d'Optique Graduate School, Paris, France","institution_ids":["https://openalex.org/I4210091746"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100389169","display_name":"Ying He","orcid":"https://orcid.org/0000-0002-6749-4485"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ying He","raw_affiliation_strings":["College of Computing Science and Data Science, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-6749-4485","affiliations":[{"raw_affiliation_string":"College of Computing Science and Data Science, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6419,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.84116149,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"32","issue":"2","first_page":"1525","last_page":"1539"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.8181999921798706,"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":0.8181999921798706,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.02500000037252903,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.01549999974668026,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.7372000217437744},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6931999921798706},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6238999962806702},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6222000122070312},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.501800000667572},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4681999981403351},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4165000021457672},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.40950000286102295},{"id":"https://openalex.org/keywords/value-noise","display_name":"Value noise","score":0.39820000529289246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7985000014305115},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7372000217437744},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6931999921798706},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6238999962806702},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6222000122070312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6133000254631042},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.501800000667572},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4681999981403351},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4165000021457672},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C182163834","wikidata":"https://www.wikidata.org/wiki/Q2926529","display_name":"Value noise","level":5,"score":0.39820000529289246},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.365200012922287},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C200378446","wikidata":"https://www.wikidata.org/wiki/Q4147391","display_name":"Gradient noise","level":5,"score":0.3197000026702881},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3183000087738037},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.31610000133514404},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2921999990940094},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.2856000065803528},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C127372701","wikidata":"https://www.wikidata.org/wiki/Q16979398","display_name":"Impulse noise","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.2752000093460083},{"id":"https://openalex.org/C35772409","wikidata":"https://www.wikidata.org/wiki/Q1323086","display_name":"Image noise","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2648000121116638}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tvcg.2025.3635138","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvcg.2025.3635138","pdf_url":null,"source":{"id":"https://openalex.org/S84775595","display_name":"IEEE Transactions on Visualization and Computer Graphics","issn_l":"1077-2626","issn":["1077-2626","1941-0506","2160-9306"],"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 Visualization and Computer Graphics","raw_type":"journal-article"},{"id":"pmid:41269811","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41269811","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on visualization and computer graphics","raw_type":null},{"id":"pmh:oai:HAL:hal-05402439v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05402439","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Visualization and Computer Graphics, 2025, pp.1-15. ⟨10.1109/TVCG.2025.3635138⟩","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1478828925","display_name":"\u57fa\u4e8e\u7403B\u6837\u6761\u4e0e\u7a7a\u95f4\u56fe\u6df1\u5ea6\u5b66\u4e60\u7684\u4e09\u7ef4\u9885\u5185\u52a8\u8109\u7624\u81ea\u52a8\u68c0\u6d4b\u7b97\u6cd5\u7814\u7a76","funder_award_id":"62072045","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320320751","display_name":"Ministry of Education - Singapore","ror":"https://ror.org/01kcva023"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325902","display_name":"Beijing Municipal Science and Technology Commission","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Point":[0,57],"cloud":[1,58],"denoising":[2,73],"is":[3],"a":[4,62,127],"fundamental":[5],"yet":[6],"challenging":[7],"task":[8],"in":[9,86],"computer":[10],"graphics.":[11],"Existing":[12],"solutions":[13],"typically":[14],"rely":[15],"on":[16,19,33,116,146],"supervised":[17],"training":[18,97],"synthesized":[20],"noise.":[21],"However,":[22],"real-world":[23],"noise":[24,35,47,69,85,92,106,119,129],"often":[25],"exhibits":[26],"greater":[27],"complexity,":[28],"causing":[29],"learning-based":[30],"methods":[31],"trained":[32],"synthetic":[34,118,148],"to":[36,45,67,161],"struggle":[37],"when":[38],"encountering":[39],"unseen":[40,91],"noise-a":[41],"phenomenon":[42],"we":[43,53,82,125],"refer":[44],"as":[46],"misalignment.":[48],"To":[49],"address":[50],"this":[51,111],"challenge,":[52],"propose":[54],"LaPDA":[55,75,154],"(Latent-space":[56],"Denoising":[59],"with":[60,94,104,120],"Adaptivity),":[61],"neural":[63],"network":[64],"explicitly":[65],"designed":[66],"mitigate":[68],"misalignment":[70],"and":[71,149,158],"enhance":[72],"robustness.":[74],"consists":[76],"of":[77,137],"two":[78],"key":[79],"stages.":[80],"First,":[81],"adaptively":[83,139],"model":[84],"the":[87,95,134,138],"latent":[88],"space,":[89],"aligning":[90],"distributions":[93,98,103],"known":[96],"or":[99],"adjusting":[100],"them":[101],"toward":[102],"lower":[105],"scales.":[107],"Training":[108],"objectives":[109],"at":[110],"stage":[112],"are":[113],"formulated":[114],"based":[115],"controlled":[117],"varying":[121],"intensity":[122],"levels.":[123],"Second,":[124],"introduce":[126],"gradual":[128],"removal":[130],"module":[131],"that":[132,153],"optimizes":[133],"spatial":[135],"distribution":[136],"adjusted":[140],"noisy":[141],"points.":[142],"Extensive":[143],"experiments":[144],"conducted":[145],"both":[147],"scanned":[150],"datasets":[151],"demonstrate":[152],"achieves":[155],"enhanced":[156],"accuracy":[157],"robustness":[159],"compared":[160],"state-of-the-art":[162],"methods.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-02-09T05:59:30.833894","created_date":"2025-11-23T00:00:00"}