{"id":"https://openalex.org/W7111148419","doi":"https://doi.org/10.48550/arxiv.2512.06058","title":"Representation Learning for Point Cloud Understanding","display_name":"Representation Learning for Point Cloud Understanding","publication_year":2025,"publication_date":"2025-12-05","ids":{"openalex":"https://openalex.org/W7111148419","doi":"https://doi.org/10.48550/arxiv.2512.06058"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.06058","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.06058","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2512.06058","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yan, Siming","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yan, Siming","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"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":0.8687999844551086,"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":0.8687999844551086,"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":0.020400000736117363,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.008799999952316284,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.635699987411499},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.633400022983551},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5827000141143799},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5562000274658203},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4569999873638153},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.4124999940395355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7333999872207642},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.635699987411499},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.633400022983551},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5827000141143799},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5562000274658203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4934999942779541},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4731000065803528},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4260999858379364},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.4124999940395355},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.36970001459121704},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.27720001339912415},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.06058","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.06058","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":"doi:10.48550/arxiv.2512.06058","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.06058","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.436928927898407}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,120],"rapid":[2],"advancement":[3],"of":[4,57,122],"technology,":[5],"3D":[6,25,32,50,105,110],"data":[7,51],"acquisition":[8],"and":[9,22,35,42,68,89],"utilization":[10],"have":[11],"become":[12],"increasingly":[13],"prevalent":[14],"across":[15],"various":[16],"fields,":[17],"including":[18],"computer":[19],"vision,":[20],"robotics,":[21,65],"geospatial":[23],"analysis.":[24],"data,":[26],"captured":[27],"through":[28],"methods":[29],"such":[30],"as":[31],"scanners,":[33],"LiDARs,":[34],"RGB-D":[36],"cameras,":[37],"provides":[38],"rich":[39],"geometric,":[40],"shape,":[41],"scale":[43],"information.":[44],"When":[45],"combined":[46],"with":[47],"2D":[48,93,101,115,137],"images,":[49],"offers":[52],"machines":[53],"a":[54],"comprehensive":[55],"understanding":[56,111],"their":[58,126],"environment,":[59],"benefiting":[60],"applications":[61],"like":[62],"autonomous":[63],"driving,":[64],"remote":[66],"sensing,":[67],"medical":[69],"treatment.":[70],"This":[71],"dissertation":[72],"focuses":[73],"on":[74],"three":[75],"main":[76],"areas:":[77],"supervised":[78],"representation":[79,132],"learning":[80,87,91,133],"for":[81],"point":[82,130],"cloud":[83,131],"primitive":[84],"segmentation,":[85],"self-supervised":[86],"methods,":[88,124],"transfer":[90],"from":[92],"to":[94,103,128],"3D.":[95],"Our":[96],"approach,":[97],"which":[98],"integrates":[99],"pre-trained":[100],"models":[102],"support":[104],"network":[106],"training,":[107],"significantly":[108],"improves":[109],"without":[112],"merely":[113],"transforming":[114],"data.":[116],"Extensive":[117],"experiments":[118],"validate":[119],"effectiveness":[121],"our":[123],"showcasing":[125],"potential":[127],"advance":[129],"by":[134],"effectively":[135],"integrating":[136],"knowledge.":[138]},"counts_by_year":[],"updated_date":"2025-12-10T02:49:46.989445","created_date":"2025-12-10T00:00:00"}
