{"id":"https://openalex.org/W4399768814","doi":"https://doi.org/10.1109/tpami.2024.3416068","title":"Fast Learning of Signed Distance Functions From Noisy Point Clouds via Noise to Noise Mapping","display_name":"Fast Learning of Signed Distance Functions From Noisy Point Clouds via Noise to Noise Mapping","publication_year":2024,"publication_date":"2024-06-18","ids":{"openalex":"https://openalex.org/W4399768814","doi":"https://doi.org/10.1109/tpami.2024.3416068","pmid":"https://pubmed.ncbi.nlm.nih.gov/38889034"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2024.3416068","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3416068","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","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":"https://openalex.org/A5060588302","display_name":"Junsheng Zhou","orcid":"https://orcid.org/0000-0002-1919-8227"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junsheng Zhou","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034970671","display_name":"Baorui Ma","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baorui Ma","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101691399","display_name":"Yu-Shen Liu","orcid":"https://orcid.org/0000-0001-7305-1915"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu-Shen Liu","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068597652","display_name":"Zhizhong Han","orcid":"https://orcid.org/0000-0001-9540-9973"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhizhong Han","raw_affiliation_strings":["Department of Computer Science, Wayne State University, Detroit, MI, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Wayne State University, Detroit, MI, USA","institution_ids":["https://openalex.org/I185443292"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5060588302"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.0897,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.92094434,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"46","issue":"12","first_page":"8936","last_page":"8953"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9729999899864197,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9729999899864197,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9725000262260437,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9661999940872192,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6934650540351868},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6323262453079224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6029285788536072},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5555760264396667},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5203901529312134},{"id":"https://openalex.org/keywords/signed-distance-function","display_name":"Signed distance function","score":0.5180166363716125},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.5103561282157898},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.43679752945899963},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37535154819488525},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.37151622772216797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18798106908798218},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11368173360824585}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6934650540351868},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6323262453079224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6029285788536072},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5555760264396667},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5203901529312134},{"id":"https://openalex.org/C71169176","wikidata":"https://www.wikidata.org/wiki/Q7512907","display_name":"Signed distance function","level":2,"score":0.5180166363716125},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.5103561282157898},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.43679752945899963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37535154819488525},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.37151622772216797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18798106908798218},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11368173360824585},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2024.3416068","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2024.3416068","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:38889034","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38889034","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 pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.4699999988079071,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G3174516060","display_name":null,"funder_award_id":"62072268","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4136897696","display_name":null,"funder_award_id":"62272263","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":125,"referenced_works":["https://openalex.org/W1987985833","https://openalex.org/W1992642990","https://openalex.org/W2000760692","https://openalex.org/W2052790699","https://openalex.org/W2085905957","https://openalex.org/W2342277278","https://openalex.org/W2471962767","https://openalex.org/W2529260170","https://openalex.org/W2546714150","https://openalex.org/W2560722161","https://openalex.org/W2746892480","https://openalex.org/W2905288042","https://openalex.org/W2906788812","https://openalex.org/W2944579304","https://openalex.org/W2945957791","https://openalex.org/W2962849139","https://openalex.org/W2963627347","https://openalex.org/W2963680153","https://openalex.org/W2963926543","https://openalex.org/W2973014570","https://openalex.org/W2981479471","https://openalex.org/W2981657250","https://openalex.org/W2998100778","https://openalex.org/W2998456637","https://openalex.org/W2998590421","https://openalex.org/W3028314732","https://openalex.org/W3034259269","https://openalex.org/W3034395814","https://openalex.org/W3034964128","https://openalex.org/W3034968345","https://openalex.org/W3035292206","https://openalex.org/W3035507572","https://openalex.org/W3035515538","https://openalex.org/W3035591705","https://openalex.org/W3037930702","https://openalex.org/W3044134703","https://openalex.org/W3044532657","https://openalex.org/W3045749263","https://openalex.org/W3049750358","https://openalex.org/W3095682719","https://openalex.org/W3107341168","https://openalex.org/W3108645709","https://openalex.org/W3109585842","https://openalex.org/W3110132843","https://openalex.org/W3117476483","https://openalex.org/W3133749673","https://openalex.org/W3174178900","https://openalex.org/W3176222228","https://openalex.org/W3180248828","https://openalex.org/W3180799285","https://openalex.org/W3184957317","https://openalex.org/W3187580431","https://openalex.org/W3191573718","https://openalex.org/W3192873895","https://openalex.org/W3197617697","https://openalex.org/W3202037070","https://openalex.org/W4214721726","https://openalex.org/W4220890232","https://openalex.org/W4221151978","https://openalex.org/W4224434530","https://openalex.org/W4233857083","https://openalex.org/W4286611259","https://openalex.org/W4307339700","https://openalex.org/W4310443815","https://openalex.org/W4311805952","https://openalex.org/W4312261929","https://openalex.org/W4312473078","https://openalex.org/W4312539440","https://openalex.org/W4312597904","https://openalex.org/W4312598811","https://openalex.org/W4312784800","https://openalex.org/W4312907000","https://openalex.org/W4312950511","https://openalex.org/W4313156707","https://openalex.org/W4375928841","https://openalex.org/W4379930424","https://openalex.org/W4381051063","https://openalex.org/W4382465742","https://openalex.org/W4386066503","https://openalex.org/W4386066608","https://openalex.org/W4386066812","https://openalex.org/W4386071856","https://openalex.org/W4386075857","https://openalex.org/W4386076147","https://openalex.org/W4386076153","https://openalex.org/W4386076223","https://openalex.org/W4386076272","https://openalex.org/W4390871701","https://openalex.org/W4390874254","https://openalex.org/W4390874418","https://openalex.org/W4393148951","https://openalex.org/W4393149117","https://openalex.org/W4393159039","https://openalex.org/W4394598105","https://openalex.org/W4394994992","https://openalex.org/W4401414285","https://openalex.org/W4402660137","https://openalex.org/W6687484953","https://openalex.org/W6749271710","https://openalex.org/W6759118810","https://openalex.org/W6763480078","https://openalex.org/W6774418182","https://openalex.org/W6779689363","https://openalex.org/W6779923959","https://openalex.org/W6784339035","https://openalex.org/W6784729325","https://openalex.org/W6784862248","https://openalex.org/W6784966207","https://openalex.org/W6786905144","https://openalex.org/W6786963090","https://openalex.org/W6790649466","https://openalex.org/W6796823060","https://openalex.org/W6797770180","https://openalex.org/W6798033281","https://openalex.org/W6810838294","https://openalex.org/W6838746019","https://openalex.org/W6839182500","https://openalex.org/W6843126166","https://openalex.org/W6843265085","https://openalex.org/W6846189886","https://openalex.org/W6851126938","https://openalex.org/W6853442197","https://openalex.org/W6857525286","https://openalex.org/W6859130970","https://openalex.org/W6859295814"],"related_works":["https://openalex.org/W3034789145","https://openalex.org/W4367628250","https://openalex.org/W4390189952","https://openalex.org/W2327107878","https://openalex.org/W1526760723","https://openalex.org/W2171117985","https://openalex.org/W2012356576","https://openalex.org/W2126659863","https://openalex.org/W3108403339","https://openalex.org/W3112120395"],"abstract_inverted_index":{"Learning":[0],"signed":[1,20],"distance":[2],"functions":[3],"(SDFs)":[4],"from":[5,32,35,88,188],"point":[6,22,26,37,60,108,115,123,189,194],"clouds":[7,109,116,190],"is":[8],"an":[9],"important":[10],"task":[11],"in":[12,69,137,139,185],"3D":[13],"computer":[14],"vision.":[15],"However,":[16],"without":[17],"ground":[18,63],"truth":[19,64],"distances,":[21],"normals":[23],"or":[24,62,86,91,191],"clean":[25,59],"clouds,":[27],"current":[28],"methods":[29,184],"still":[30],"struggle":[31],"learning":[33],"SDFs":[34,47,169],"noisy":[36,94,126],"clouds.":[38],"To":[39,128],"overcome":[40],"this":[41,98],"challenge,":[42],"we":[43,131],"propose":[44],"to":[45,51,72,163],"learn":[46],"via":[48],"a":[49,78,83,100,148,160,171],"noise":[50,52,71,73],"mapping,":[53],"which":[54,75,103,142],"does":[55],"not":[56],"require":[57],"any":[58],"cloud":[61,195],"supervision.":[65],"Our":[66,173],"novelty":[67],"lies":[68],"the":[70,182],"mapping":[74],"can":[76],"infer":[77],"highly":[79],"accurate":[80],"SDF":[81],"of":[82,150],"single":[84,93],"object":[85],"scene":[87],"its":[89],"multiple":[90],"even":[92],"observations.":[95,127],"We":[96,157],"achieve":[97],"by":[99,147,167],"novel":[101,161],"loss":[102],"enables":[104],"statistical":[105],"reasoning":[106],"on":[107],"and":[110,120,197],"maintains":[111],"geometric":[112],"consistency":[113],"although":[114],"are":[117],"irregular,":[118],"unordered":[119],"have":[121],"no":[122],"correspondence":[124],"among":[125],"accelerate":[129],"training,":[130],"use":[132],"multi-resolution":[133],"hash":[134],"encodings":[135],"implemented":[136],"CUDA":[138],"our":[140,144,179],"framework,":[141],"reduces":[143],"training":[145],"time":[146],"factor":[149],"ten,":[151],"achieving":[152],"convergence":[153],"within":[154],"one":[155],"minute.":[156],"further":[158],"introduce":[159],"schema":[162],"improve":[164],"multi-view":[165,192],"reconstruction":[166,187],"estimating":[168],"as":[170],"prior.":[172],"evaluations":[174],"under":[175],"widely-used":[176],"benchmarks":[177],"demonstrate":[178],"superiority":[180],"over":[181],"state-of-the-art":[183],"surface":[186],"images,":[193],"denoising":[196],"upsampling.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
