{"id":"https://openalex.org/W4415966842","doi":"https://doi.org/10.48550/arxiv.2510.17875","title":"3D Weakly Supervised Semantic Segmentation via Class-Aware and Geometry-Guided Pseudo-Label Refinement","display_name":"3D Weakly Supervised Semantic Segmentation via Class-Aware and Geometry-Guided Pseudo-Label Refinement","publication_year":2025,"publication_date":"2025-10-17","ids":{"openalex":"https://openalex.org/W4415966842","doi":"https://doi.org/10.48550/arxiv.2510.17875"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2510.17875","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.17875","pdf_url":"https://arxiv.org/pdf/2510.17875","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.17875","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100591441","display_name":"Xiaoxu Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xu, Xiaoxu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Liu, Xuexun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xuexun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100454990","display_name":"Jinlong Li","orcid":"https://orcid.org/0000-0003-2279-9506"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinlong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011133696","display_name":"Yitian Yuan","orcid":"https://orcid.org/0000-0001-8701-7689"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Yitian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055435177","display_name":"Qiudan Zhang","orcid":"https://orcid.org/0000-0001-6067-8188"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qiudan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018662596","display_name":"Lin Ma","orcid":"https://orcid.org/0000-0003-1684-9714"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027171279","display_name":"Nicu Sebe","orcid":"https://orcid.org/0000-0002-6597-7248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sebe, Nicu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100407860","display_name":"Xu Wang","orcid":"https://orcid.org/0000-0002-2948-6468"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100591441"],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6083999872207642,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6083999872207642,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.26510000228881836,"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":0.025800000876188278,"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/segmentation","display_name":"Segmentation","score":0.685699999332428},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6419000029563904},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.586899995803833},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5170999765396118},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.42669999599456787},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.41510000824928284},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3984000086784363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7874000072479248},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.685699999332428},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6419000029563904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.61080002784729},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.586899995803833},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5170999765396118},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.41510000824928284},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.39750000834465027},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3749000132083893},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2669000029563904}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.17875","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.17875","pdf_url":"https://arxiv.org/pdf/2510.17875","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.17875","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.17875","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.17875","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.17875","pdf_url":"https://arxiv.org/pdf/2510.17875","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"3D":[0,52,63,75,83,141,199],"weakly":[1,76],"supervised":[2,77],"semantic":[3,10,78,115],"segmentation":[4,11,79],"(3D":[5],"WSSS)":[6],"aims":[7],"to":[8,37,91,106,144,153,159,176],"achieve":[9],"by":[12],"leveraging":[13],"sparse":[14],"or":[15,33],"low-cost":[16],"annotated":[17],"data,":[18],"significantly":[19],"reducing":[20],"reliance":[21],"on":[22,122,213],"dense":[23],"point-wise":[24],"annotations.":[25],"Previous":[26],"works":[27],"mainly":[28],"employ":[29],"class":[30],"activation":[31],"maps":[32],"pre-trained":[34],"vision-language":[35],"models":[36],"address":[38,160],"this":[39,67],"challenge.":[40],"However,":[41],"the":[42,48,130,161,195],"low":[43],"quality":[44,125,188],"of":[45,51,163,197],"pseudo-labels":[46],"and":[47,110,216],"insufficient":[49],"exploitation":[50],"geometric":[53,84,142,156],"priors":[54,85],"jointly":[55],"create":[56],"significant":[57],"technical":[58],"bottlenecks":[59],"in":[60,224],"developing":[61],"high-performance":[62,198],"WSSS":[64,200],"models.":[65,201],"In":[66],"paper,":[68],"we":[69,167],"propose":[70,168],"a":[71,87,169],"simple":[72],"yet":[73],"effective":[74],"method":[80],"that":[81,151,173,206],"integrates":[82,139,174],"into":[86,179],"class-aware":[88],"guidance":[89],"mechanism":[90],"generate":[92,107],"high-fidelity":[93],"pseudo":[94,112,149],"labels.":[95],"Concretely,":[96],"our":[97,207],"designed":[98],"methodology":[99,209],"first":[100],"employs":[101],"Class-Aware":[102],"Label":[103,132,170],"Refinement":[104,133],"module":[105],"more":[108],"balanced":[109],"accurate":[111],"labels":[113,150,178],"for":[114],"categrories.":[116],"This":[117,182],"initial":[118],"refinement":[119],"stage":[120],"focuses":[121],"enhancing":[123],"label":[124,191],"through":[126,230],"category-specific":[127],"optimization.":[128],"Subsequently,":[129],"Geometry-Aware":[131],"component":[134],"is":[135],"developed,":[136],"which":[137],"strategically":[138],"implicit":[140],"constraints":[143],"effectively":[145],"filter":[146],"out":[147],"low-confidence":[148],"fail":[152],"comply":[154],"with":[155],"plausibility.":[157],"Moreover,":[158],"challenge":[162],"extensive":[164],"unlabeled":[165],"regions,":[166],"Update":[171],"strategy":[172],"Self-Training":[175],"propagate":[177],"these":[180],"areas.":[181],"iterative":[183],"process":[184],"continuously":[185],"enhances":[186],"pseudo-label":[187],"while":[189,219],"expanding":[190],"coverage,":[192],"ultimately":[193],"fostering":[194],"development":[196],"Comprehensive":[202],"experimental":[203],"validation":[204],"reveals":[205],"proposed":[208],"achieves":[210],"state-of-the-art":[211],"performance":[212],"both":[214],"ScanNet":[215],"S3DIS":[217],"benchmarks":[218],"demonstrating":[220],"remarkable":[221],"generalization":[222],"capability":[223],"unsupervised":[225],"settings,":[226],"maintaining":[227],"competitive":[228],"accuracy":[229],"its":[231],"robust":[232],"design.":[233]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-24T00:00:00"}