{"id":"https://openalex.org/W3113317635","doi":"https://doi.org/10.1109/tip.2022.3182266","title":"SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction With Self-Projection Optimization","display_name":"SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction With Self-Projection Optimization","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W3113317635","doi":"https://doi.org/10.1109/tip.2022.3182266","mag":"3113317635","pmid":"https://pubmed.ncbi.nlm.nih.gov/35696479"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2022.3182266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2022.3182266","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","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/A5007736125","display_name":"Xinhai Liu","orcid":"https://orcid.org/0000-0003-4200-4862"},"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":"Xinhai 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":"middle","author":{"id":"https://openalex.org/A5030704926","display_name":"Xinchen Liu","orcid":"https://orcid.org/0000-0003-4931-8821"},"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":"Xinchen 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":"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/A5007736125"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":12.2412,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.99633666,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"31","issue":null,"first_page":"4213","last_page":"4226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9998000264167786,"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":0.9998000264167786,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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":0.823630690574646},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.8035322427749634},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6235792636871338},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6006594300270081},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48240816593170166},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.47638505697250366},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46880629658699036},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4428372085094452},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.4342373013496399},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.391954243183136},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3220038414001465},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17521685361862183}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.823630690574646},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8035322427749634},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6235792636871338},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6006594300270081},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48240816593170166},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.47638505697250366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46880629658699036},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4428372085094452},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.4342373013496399},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.391954243183136},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3220038414001465},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17521685361862183},{"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":2,"locations":[{"id":"doi:10.1109/tip.2022.3182266","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2022.3182266","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:35696479","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35696479","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 image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.4000000059604645}],"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/G6179711750","display_name":null,"funder_award_id":"2020YFF0304100","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1988317275","https://openalex.org/W2025973801","https://openalex.org/W2058776828","https://openalex.org/W2081194966","https://openalex.org/W2115579991","https://openalex.org/W2119693472","https://openalex.org/W2137531922","https://openalex.org/W2169611956","https://openalex.org/W2172246656","https://openalex.org/W2190691619","https://openalex.org/W2594519801","https://openalex.org/W2784996692","https://openalex.org/W2796426482","https://openalex.org/W2884154111","https://openalex.org/W2962903125","https://openalex.org/W2963121255","https://openalex.org/W2963123724","https://openalex.org/W2963390820","https://openalex.org/W2963509914","https://openalex.org/W2963680153","https://openalex.org/W2964121744","https://openalex.org/W2979750740","https://openalex.org/W2982041717","https://openalex.org/W2982683655","https://openalex.org/W2991485494","https://openalex.org/W2997337685","https://openalex.org/W3008105217","https://openalex.org/W3009750677","https://openalex.org/W3012494314","https://openalex.org/W3013245634","https://openalex.org/W3031112623","https://openalex.org/W3034429258","https://openalex.org/W3034486798","https://openalex.org/W3034493208","https://openalex.org/W3035014292","https://openalex.org/W3035534438","https://openalex.org/W3082170814","https://openalex.org/W3083861253","https://openalex.org/W3092946149","https://openalex.org/W3103396201","https://openalex.org/W3106699132","https://openalex.org/W3118644257","https://openalex.org/W3118881191","https://openalex.org/W3137466219","https://openalex.org/W3167071962","https://openalex.org/W3170469318","https://openalex.org/W3170754649","https://openalex.org/W3175676582","https://openalex.org/W3191573718","https://openalex.org/W4288092002","https://openalex.org/W4394671432","https://openalex.org/W6631190155","https://openalex.org/W6687484953","https://openalex.org/W6739778489","https://openalex.org/W6748208425","https://openalex.org/W6763103765","https://openalex.org/W6783272290","https://openalex.org/W6786963090"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W1983892167","https://openalex.org/W2281134365","https://openalex.org/W4310746709","https://openalex.org/W3176213335","https://openalex.org/W2562256921","https://openalex.org/W4212888438"],"abstract_inverted_index":{"The":[0],"task":[1],"of":[2,71,102],"point":[3,12,18,35,74,90,122,126,132,158,171,187,209],"cloud":[4,91,210],"upsampling":[5,92,100],"aims":[6],"to":[7,44,55,67,96,146,168,178,205,231],"acquire":[8],"dense":[9,34],"and":[10,16,52,65,125,151,198,219,222],"uniform":[11,197],"sets":[13,36,75,172],"from":[14,78],"sparse":[15,59,80],"irregular":[17],"sets.":[19],"Although":[20],"significant":[21],"progress":[22],"has":[23],"been":[24],"made":[25],"with":[26,140,173,196],"deep":[27],"learning":[28],"models,":[29],"state-of-the-art":[30,232],"methods":[31],"require":[32],"ground-truth":[33],"as":[37,76,201],"the":[38,98,106,131,137,141,157,181,185,207,223],"supervision,":[39],"which":[40,117],"makes":[41],"them":[42],"limited":[43],"be":[45,56],"trained":[46],"under":[47,57],"synthetic":[48,218],"paired":[49,72],"training":[50],"data":[51],"not":[53],"suitable":[54],"real-scanned":[58,79,220],"data.":[60,81],"However,":[61],"it":[62],"is":[63],"expensive":[64],"tedious":[66],"obtain":[68],"large":[69],"numbers":[70],"sparse-dense":[73],"supervision":[77],"To":[82],"address":[83],"this":[84],"problem,":[85],"we":[86,111,135,161,189,227],"propose":[87,112,190],"a":[88,113,163,191,202],"self-supervised":[89,208],"network,":[93],"named":[94],"SPU-Net,":[95],"capture":[97,147],"inherent":[99],"patterns":[101],"points":[103,183],"lying":[104],"on":[105,216],"underlying":[107],"object":[108],"surface.":[109],"Specifically,":[110],"coarse-to-fine":[114],"reconstruction":[115,199],"framework,":[116],"contains":[118],"two":[119],"main":[120],"components:":[121],"feature":[123,127,133,159],"extraction":[124],"expansion,":[128,160],"respectively.":[129],"In":[130,156],"extraction,":[134],"integrate":[136],"self-attention":[138],"module":[139],"graph":[142],"convolution":[143],"network":[144],"(GCN)":[145],"context":[148],"information":[149],"inside":[150],"among":[152],"local":[153],"regions":[154],"simultaneously.":[155],"introduce":[162],"hierarchically":[164],"learnable":[165,174],"folding":[166],"strategy":[167],"generate":[169],"upsampled":[170],"2D":[175],"grids.":[176],"Moreover,":[177],"further":[179],"optimize":[180],"noisy":[182],"in":[184],"generated":[186],"sets,":[188],"novel":[192],"self-projection":[193],"optimization":[194],"associated":[195],"terms":[200],"joint":[203],"loss":[204],"facilitate":[206],"upsampling.":[211],"We":[212],"conduct":[213],"various":[214],"experiments":[215],"both":[217],"datasets,":[221],"results":[224],"demonstrate":[225],"that":[226],"achieve":[228],"comparable":[229],"performances":[230],"supervised":[233],"methods.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":28},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
