{"id":"https://openalex.org/W7141237060","doi":"https://doi.org/10.48550/arxiv.2603.25165","title":"Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds","display_name":"Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141237060","doi":"https://doi.org/10.48550/arxiv.2603.25165"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25165","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25165","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130814194","display_name":"Bin Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020076634","display_name":"Mohamed Abdelsamad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdelsamad, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130790649","display_name":"Miao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Miao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112353924","display_name":"Alexandru Paul Condurache","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Condurache, Alexandru Paul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.8543999791145325,"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":0.8543999791145325,"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":0.03929999843239784,"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":0.017899999395012856,"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/point-cloud","display_name":"Point cloud","score":0.7541000247001648},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5442000031471252},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5400999784469604},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.5307999849319458},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5045999884605408},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5020999908447266},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4489000141620636},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.37529999017715454},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.3709999918937683}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7541000247001648},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6952000260353088},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5442000031471252},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.534600019454956},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5020999908447266},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.37529999017715454},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32409998774528503},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25165","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25165","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"self-supervised":[3,90],"learning":[4],"(SSL)":[5],"for":[6,49,67,124,176,184,191],"point":[7,94],"clouds":[8],"have":[9],"substantially":[10],"improved":[11],"3D":[12,59,71,80,193],"scene":[13,33],"understanding":[14,111],"without":[15],"human":[16],"annotations.":[17],"Existing":[18],"approaches":[19],"emphasize":[20],"semantic":[21,110],"awareness":[22,53],"by":[23,31,157],"enforcing":[24],"feature":[25],"consistency":[26],"across":[27,166],"augmented":[28],"views":[29],"or":[30],"masked":[32],"modeling.":[34],"However,":[35],"the":[36,189],"resulting":[37],"representations":[38,96],"transfer":[39],"poorly":[40],"to":[41,106],"instance":[42,116,126,178],"localization,":[43],"and":[44,112,128,148,180],"often":[45],"require":[46],"full":[47],"finetuning":[48],"strong":[50],"performance.":[51],"Instance":[52],"is":[54,65],"a":[55],"fundamental":[56],"component":[57],"of":[58],"perception,":[60],"thus":[61],"bridging":[62],"this":[63,83],"gap":[64],"crucial":[66],"progressing":[68],"toward":[69],"true":[70],"foundation":[72,194],"models":[73],"that":[74,92],"support":[75],"all":[76],"downstream":[77],"tasks":[78],"on":[79,171],"data.":[81],"In":[82],"work,":[84],"we":[85],"introduce":[86],"PointINS,":[87],"an":[88,102],"instance-oriented":[89],"framework":[91],"enriches":[93],"cloud":[95],"through":[97],"geometry-aware":[98],"learning.":[99],"PointINS":[100,169],"employs":[101],"orthogonal":[103],"offset":[104],"branch":[105],"jointly":[107],"learn":[108],"high-level":[109],"geometric":[113,146],"reasoning,":[114],"yielding":[115],"awareness.":[117],"We":[118],"identify":[119],"two":[120],"consistent":[121],"properties":[122],"essential":[123],"robust":[125],"localization":[127],"formulate":[129],"them":[130],"as":[131],"complementary":[132],"regularization":[133],"strategies,":[134],"Offset":[135],"Distribution":[136],"Regularization":[137,151],"(ODR),":[138],"which":[139,153],"aligns":[140],"predicted":[141],"offsets":[142,159],"with":[143,160],"empirically":[144],"observed":[145],"priors,":[147],"Spatial":[149],"Clustering":[150],"(SCR),":[152],"enforces":[154],"local":[155],"coherence":[156],"regularizing":[158],"pseudo-instance":[161],"masks.":[162],"Through":[163],"extensive":[164],"experiments":[165],"five":[167],"datasets,":[168],"achieves":[170],"average":[172],"+3.5%":[173],"mAP":[174],"improvement":[175],"indoor":[177],"segmentation":[179],"+4.1%":[181],"PQ":[182],"gain":[183],"outdoor":[185],"panoptic":[186],"segmentation,":[187],"paving":[188],"way":[190],"scalable":[192],"models.":[195]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-28T00:00:00"}