{"id":"https://openalex.org/W7138247505","doi":"https://doi.org/10.1609/aaai.v40i9.37685","title":"PUFM: Efficient Point Cloud Upsampling via Flow Matching","display_name":"PUFM: Efficient Point Cloud Upsampling via Flow Matching","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138247505","doi":"https://doi.org/10.1609/aaai.v40i9.37685"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v40i9.37685","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i9.37685","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i9.37685","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129749156","display_name":"Zhi-Song Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I63548447","display_name":"Lappeenranta-Lahti University of Technology","ror":"https://ror.org/0208vgz68","country_code":"FI","type":"education","lineage":["https://openalex.org/I63548447"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Zhi-Song Liu","raw_affiliation_strings":["Lappeenranta-Lahti University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lappeenranta-Lahti University of Technology","institution_ids":["https://openalex.org/I63548447"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129708126","display_name":"Chenhang He","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chenhang He","raw_affiliation_strings":["The Hong Kong Polytechnic University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086183661","display_name":"Yakun Ju","orcid":"https://orcid.org/0000-0003-4065-4108"},"institutions":[{"id":"https://openalex.org/I153648349","display_name":"University of Leicester","ror":"https://ror.org/04h699437","country_code":"GB","type":"education","lineage":["https://openalex.org/I153648349"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yakun Ju","raw_affiliation_strings":["University of Leicester"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Leicester","institution_ids":["https://openalex.org/I153648349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129680159","display_name":"Lei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["Technical University of Munich\nUniversity of Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich\nUniversity of Virginia","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"9","first_page":"7458","last_page":"7466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.8289999961853027,"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":0.8289999961853027,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.07760000228881836,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.017500000074505806,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.8837000131607056},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7890999913215637},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6129000186920166},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.45899999141693115},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3970000147819519},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.39640000462532043},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3668999969959259},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.36419999599456787},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.36070001125335693}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8837000131607056},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7890999913215637},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6129000186920166},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5465999841690063},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5116000175476074},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.45899999141693115},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.421999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4194999933242798},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3970000147819519},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.36419999599456787},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.36070001125335693},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.350600004196167},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.3418000042438507},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3285999894142151},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.30820000171661377},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C207214200","wikidata":"https://www.wikidata.org/wiki/Q4202129","display_name":"Nearest-neighbor interpolation","level":4,"score":0.2590999901294708},{"id":"https://openalex.org/C203332170","wikidata":"https://www.wikidata.org/wiki/Q6334079","display_name":"Multivariate interpolation","level":3,"score":0.250900000333786},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v40i9.37685","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i9.37685","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/37685","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/37685","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i9.37685","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i9.37685","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"models":[1],"have":[2],"recently":[3],"been":[4],"adopted":[5],"for":[6,107,194],"point":[7,30,67,95,115,188],"cloud":[8],"upsampling":[9,20,163],"due":[10],"to":[11,36,63,80,102,129,183],"their":[12,70],"effectiveness":[13],"in":[14,44,142],"solving":[15],"ill-posed":[16],"problems.":[17],"However,":[18],"existing":[19],"methods":[21],"often":[22],"struggle":[23],"with":[24,166],"inefficiencies,":[25],"as":[26],"they":[27],"generate":[28],"dense":[29,72,94,138],"clouds":[31,68,96],"by":[32],"mapping":[33],"Gaussian":[34],"noise":[35],"data,":[37],"overlooking":[38],"the":[39,82,104,111],"geometric":[40],"information":[41],"already":[42],"present":[43],"sparse":[45,66,83,92,136],"inputs.":[46],"To":[47],"address":[48],"this,":[49],"we":[50,86,117],"propose":[51],"PUFM,":[52],"a":[53,88,99,119,143],"novel":[54],"Point":[55],"Cloud":[56],"Upsampling":[57],"via":[58],"Flow":[59],"Matching,":[60],"which":[61],"learns":[62],"directly":[64],"transform":[65],"into":[69],"high-fidelity":[71],"counterparts.":[73],"Our":[74],"approach":[75,180],"first":[76],"applies":[77],"midpoint":[78],"interpolation":[79,134],"densify":[81],"input.":[84],"Then,":[85],"construct":[87],"continuous":[89],"interpolant":[90],"between":[91,135],"and":[93,97,132,137,146,174,186],"train":[98],"neural":[100],"network":[101],"estimate":[103],"velocity":[105],"field":[106],"flow":[108,151],"matching.":[109,152],"Given":[110],"unordered":[112],"nature":[113],"of":[114],"clouds,":[116,189],"introduce":[118],"pre-alignment":[120],"step":[121],"based":[122],"on":[123,154,172],"Earth":[124],"Mover's":[125],"Distance":[126],"(EMD)":[127],"optimization":[128],"ensure":[130],"coherent":[131],"meaningful":[133],"representations.":[139],"This":[140],"results":[141],"more":[144,192],"stable":[145],"efficient":[147],"learning":[148],"trajectory":[149],"during":[150],"Experiments":[153],"synthetic":[155],"benchmarks":[156],"demonstrate":[157],"that":[158,178],"our":[159,179],"method":[160],"delivers":[161],"superior":[162],"quality":[164],"but":[165],"fewer":[167],"sampling":[168],"steps.":[169],"Further":[170],"experiments":[171],"ScanNet":[173],"KITTI":[175],"also":[176],"show":[177],"generalizes":[181],"well":[182],"real-world":[184,195],"RGB-D":[185],"LiDAR":[187],"making":[190],"it":[191],"practical":[193],"applications.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}
