{"id":"https://openalex.org/W4416017029","doi":"https://doi.org/10.1145/3746252.3761017","title":"Point-DMAE: Point Cloud Self-supervised Learning via Density-directed Masked Autoencoders","display_name":"Point-DMAE: Point Cloud Self-supervised Learning via Density-directed Masked Autoencoders","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416017029","doi":"https://doi.org/10.1145/3746252.3761017"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761017","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","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/A5112101805","display_name":"Xianglong Jin","orcid":"https://orcid.org/0009-0005-8397-9078"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianglong Jin","raw_affiliation_strings":["School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401102","display_name":"Zheng Wang","orcid":"https://orcid.org/0000-0002-4814-1115"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Wang","raw_affiliation_strings":["School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109059341","display_name":"Wenjie Zheng","orcid":"https://orcid.org/0009-0002-2965-9012"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjie Zheng","raw_affiliation_strings":["School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003222421","display_name":"Feiping Nie","orcid":"https://orcid.org/0000-0002-0871-6519"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiping Nie","raw_affiliation_strings":["School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5112101805"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36746331,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1231","last_page":"1238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.7006999850273132,"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":0.7006999850273132,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.06499999761581421,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.029600000008940697,"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/point-cloud","display_name":"Point cloud","score":0.7856000065803528},{"id":"https://openalex.org/keywords/point-process","display_name":"Point process","score":0.6341000199317932},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.6144000291824341},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5690000057220459},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4578000009059906},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4438999891281128},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4219000041484833}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7856000065803528},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7627000212669373},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.6341000199317932},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.6144000291824341},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5884000062942505},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5690000057220459},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4578000009059906},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4438999891281128},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.35530000925064087},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3222000002861023},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3190999925136566},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2671999931335449}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761017","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2144199676","https://openalex.org/W2553307952","https://openalex.org/W2560722161","https://openalex.org/W2981440248","https://openalex.org/W3176287975","https://openalex.org/W3195533065","https://openalex.org/W3202611145","https://openalex.org/W3204568647","https://openalex.org/W4312270234","https://openalex.org/W4312317653","https://openalex.org/W4312788538","https://openalex.org/W4312980726","https://openalex.org/W4313156423","https://openalex.org/W4361802179","https://openalex.org/W4386075705","https://openalex.org/W4390873184","https://openalex.org/W4393147949","https://openalex.org/W4393148022","https://openalex.org/W4402816865"],"related_works":[],"abstract_inverted_index":{"Masked":[0,112],"autoencoders":[1],"have":[2],"been":[3],"extensively":[4],"utilized":[5],"in":[6,63],"3D":[7,168],"point":[8,22,43,51,92,124,146],"cloud":[9,23,44,93,125],"self-supervised":[10],"learning,":[11],"where":[12],"the":[13,21,38,42,47,58,74,173],"fundamental":[14],"approach":[15],"involves":[16],"masking":[17,78,130],"a":[18,107,128,136],"portion":[19],"of":[20,41,76],"and":[24,65,84,144,150,161,183],"subsequently":[25],"reconstructing":[26],"it.":[27],"This":[28,69],"process":[29],"is":[30,53],"hypothesized":[31],"to":[32,97,140],"enhance":[33],"model":[34],"learning":[35,99],"by":[36,176],"leveraging":[37],"inherent":[39],"structure":[40],"data.":[45,68],"However,":[46],"information":[48],"density":[49],"within":[50,172],"clouds":[52],"inherently":[54],"uneven,":[55],"contrasting":[56],"with":[57],"more":[59],"uniform":[60],"distributions":[61],"found":[62],"language":[64],"2D":[66,85],"image":[67],"uneven":[70],"distribution":[71],"suggests":[72],"that":[73],"application":[75],"random":[77],"strategies,":[79],"commonly":[80],"adopted":[81],"from":[82],"NLP":[83],"vision,":[86],"may":[87],"not":[88],"be":[89],"optimal":[90],"for":[91,114],"data,":[94],"potentially":[95],"leading":[96],"suboptimal":[98],"outcomes.":[100],"Based":[101],"on":[102,167,178,181,185],"this":[103],"observation,":[104],"we":[105],"propose":[106],"simple":[108],"yet":[109],"effective":[110],"Density-directed":[111],"Autoencoders":[113],"Point":[115],"Cloud":[116],"Self-supervised":[117],"Learning":[118],"(Point-DMAE),":[119],"which":[120],"learns":[121],"latent":[122],"semantic":[123],"features":[126,147],"using":[127],"density-directed":[129,153],"strategy.":[131],"Specifically,":[132],"our":[133,164],"method":[134],"employs":[135],"dual-branch":[137],"Transformer":[138],"architecture":[139],"extract":[141],"both":[142],"high-level":[143],"fine-grained":[145],"through":[148],"global":[149],"local":[151],"block":[152],"masking,":[154],"respectively.":[155],"Point-DMAE":[156],"demonstrates":[157],"high":[158],"pre-training":[159],"efficiency":[160],"significantly":[162],"outperforms":[163],"baseline":[165],"(Point-MAE)":[166],"object":[169],"classification":[170],"tasks":[171],"ScanObjectNN":[174],"dataset":[175],"4.13%":[177],"OBJ-BG,":[179],"5.17%":[180],"OBJ-ONLY,":[182],"4.17%":[184],"PB-T50-RS.":[186],"Codes":[187],"are":[188],"available":[189],"at":[190],"https://github.com/jinxianglong10/Point-DMAE.":[191]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-08T00:00:00"}