{"id":"https://openalex.org/W4414360035","doi":"https://doi.org/10.24963/ijcai.2025/261","title":"Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding","display_name":"Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360035","doi":"https://doi.org/10.24963/ijcai.2025/261"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/261","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/261","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","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/A5038981631","display_name":"Yixin Zha","orcid":null},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixin Zha","raw_affiliation_strings":["University of Science and Technology of China / Deep Space Exploration Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China / Deep Space Exploration Lab","institution_ids":["https://openalex.org/I4210148107"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007421671","display_name":"Chuxin Wang","orcid":"https://orcid.org/0000-0003-1431-7677"},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuxin Wang","raw_affiliation_strings":["University of Science and Technology of China / Deep Space Exploration Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China / Deep Space Exploration Lab","institution_ids":["https://openalex.org/I4210148107"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054606798","display_name":"Wenfei Yang","orcid":"https://orcid.org/0000-0003-3599-7659"},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenfei Yang","raw_affiliation_strings":["University of Science and Technology of China / Deep Space Exploration Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China / Deep Space Exploration Lab","institution_ids":["https://openalex.org/I4210148107"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100648981","display_name":"Tianzhu Zhang","orcid":"https://orcid.org/0000-0003-1856-9564"},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianzhu Zhang","raw_affiliation_strings":["University of Science and Technology of China / Deep Space Exploration Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China / Deep Space Exploration Lab","institution_ids":["https://openalex.org/I4210148107"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210148107"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2341","last_page":"2349"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9965999722480774,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.9800999760627747,"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/component","display_name":"Component (thermodynamics)","score":0.8112999796867371},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.7296000123023987},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6568999886512756},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5200999975204468},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5098000168800354},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5080999732017517},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.45890000462532043},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.444599986076355}],"concepts":[{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.8112999796867371},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7465000152587891},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.7296000123023987},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6568999886512756},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5080999732017517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46889999508857727},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.444599986076355},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.42649999260902405},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3555999994277954},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2815999984741211},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2667999863624573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/261","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/261","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"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":{"Point":[0],"cloud":[1,46],"understanding":[2],"aims":[3],"to":[4,35,50,102,154],"acquire":[5],"robust":[6],"and":[7,82,173],"general":[8],"feature":[9],"representations":[10],"from":[11,116],"unlabeled":[12],"data.":[13],"Masked":[14,69],"point":[15,40,45],"modeling-based":[16],"methods":[17,29],"have":[18],"recently":[19],"shown":[20],"significant":[21],"performance":[22,157],"across":[23],"various":[24],"downstream":[25,162],"tasks.":[26,163],"These":[27],"pre-training":[28],"rely":[30],"on":[31,167],"random":[32,133],"masking":[33,86,126,134],"strategies":[34],"establish":[36],"the":[37,51,59,90,111,130,156,176],"perception":[38],"of":[39,53,106,113,132,158,178],"clouds":[41],"by":[42,58],"restoring":[43],"corrupted":[44],"inputs,":[47],"which":[48,71,149],"leads":[49],"failure":[52],"capturing":[54,110],"reasonable":[55],"semantic":[56,79,92,99],"relationships":[57],"self-supervised":[60],"models.":[61],"To":[62],"address":[63],"this":[64],"issue,":[65],"we":[66,95,121,142],"propose":[67],"Semantic":[68],"Autoencoder,":[70],"comprises":[72],"two":[73],"main":[74],"components:":[75],"a":[76,83,97,104,123,144],"prototype-based":[77],"component":[78,84,91,98,124,139,145],"modeling":[80,93],"module":[81],"semantic-enhanced":[85,125,146],"strategy.":[87],"Specifically,":[88],"in":[89,109,135,161],"module,":[94],"design":[96],"guidance":[100],"mechanism":[101],"direct":[103],"set":[105],"learnable":[107],"prototypes":[108,153],"semantics":[112],"different":[114],"components":[115],"objects.":[117],"Leveraging":[118],"these":[119,152],"prototypes,":[120],"develop":[122],"strategy":[127],"that":[128],"addresses":[129],"limitations":[131],"effectively":[136],"covering":[137],"complete":[138],"structures.":[140],"Furthermore,":[141],"introduce":[143],"prompt-tuning":[147],"strategy,":[148],"further":[150],"leverages":[151],"improve":[155],"pre-trained":[159],"models":[160],"Extensive":[164],"experiments":[165],"conducted":[166],"datasets":[168],"such":[169],"as":[170],"ScanObjectNN,":[171],"ModelNet40,":[172],"ShapeNetPart":[174],"demonstrate":[175],"effectiveness":[177],"our":[179],"proposed":[180],"modules.":[181]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
