{"id":"https://openalex.org/W7105682090","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227193","title":"DepthCNE: Contrastive Neighbor Embedding in Self-Supervised Learning for Point Clouds","display_name":"DepthCNE: Contrastive Neighbor Embedding in Self-Supervised Learning for Point Clouds","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105682090","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227193"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Mingyu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Mingyu Liu","raw_affiliation_strings":["Technical University of Munich,Garching b. München,Germany,85748"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Garching b. München,Germany,85748","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhijie Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Zhijie Yang","raw_affiliation_strings":["Technical University of Munich,Garching b. München,Germany,85748"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Garching b. München,Germany,85748","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ekim Yurtsever","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ekim Yurtsever","raw_affiliation_strings":["The Ohio State University,Columbus,USA,OH 43212"],"affiliations":[{"raw_affiliation_string":"The Ohio State University,Columbus,USA,OH 43212","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuning Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yuning Cui","raw_affiliation_strings":["Technical University of Munich,Garching b. München,Germany,85748"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Garching b. München,Germany,85748","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":null,"display_name":"Alois Knoll","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alois Knoll","raw_affiliation_strings":["Technical University of Munich,Garching b. München,Germany,85748"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich,Garching b. München,Germany,85748","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I62916508"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.57465933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9437000155448914,"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":0.9437000155448914,"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":0.006200000178068876,"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/T11448","display_name":"Face recognition and analysis","score":0.004699999932199717,"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/embedding","display_name":"Embedding","score":0.6704000234603882},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6412000060081482},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6195999979972839},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6144999861717224},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5340999960899353},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.46140000224113464},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4187000095844269},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4065000116825104},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4052000045776367}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6704000234603882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6563000082969666},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6412000060081482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6295999884605408},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6195999979972839},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6144999861717224},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5340999960899353},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4052000045776367},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3463999927043915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3441999852657318},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C13670688","wikidata":"https://www.wikidata.org/wiki/Q3500548","display_name":"Space partitioning","level":2,"score":0.2630999982357025}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":30,"referenced_works":["https://openalex.org/W1920022804","https://openalex.org/W2089468765","https://openalex.org/W2211722331","https://openalex.org/W2501704887","https://openalex.org/W2553307952","https://openalex.org/W2962887844","https://openalex.org/W2963158438","https://openalex.org/W2963719584","https://openalex.org/W2963906250","https://openalex.org/W2979750740","https://openalex.org/W2981440248","https://openalex.org/W3034459762","https://openalex.org/W3035524453","https://openalex.org/W3116959466","https://openalex.org/W3119708198","https://openalex.org/W3167071962","https://openalex.org/W3167095230","https://openalex.org/W3197097949","https://openalex.org/W3202611145","https://openalex.org/W4214755140","https://openalex.org/W4312270234","https://openalex.org/W4312317653","https://openalex.org/W4312500967","https://openalex.org/W4312788538","https://openalex.org/W4385245566","https://openalex.org/W4386075705","https://openalex.org/W4387436633","https://openalex.org/W4390872658","https://openalex.org/W4393147949","https://openalex.org/W4401414285"],"related_works":[],"abstract_inverted_index":{"Contrastive":[0],"learning,":[1],"as":[2],"one":[3],"of":[4,22,106],"the":[5,26,96,103],"self-supervised":[6,48],"learning":[7,49,57,76],"paradigms,":[8],"has":[9],"demonstrated":[10],"substantial":[11],"effectiveness":[12],"in":[13,32,124],"boosting":[14],"various":[15,115],"downstream":[16,116],"tasks.":[17,117],"However,":[18],"its":[19],"inherent":[20],"lack":[21],"explicit":[23],"control":[24],"over":[25],"embedding":[27],"space":[28,93],"structure":[29],"often":[30],"results":[31],"suboptimal":[33],"performance":[34,105,123],"and":[35,68,111],"limited":[36],"feature":[37],"visualization.":[38,126],"To":[39],"address":[40],"these":[41],"challenges,":[42],"we":[43,80],"propose":[44],"DepthCNE,":[45],"an":[46],"innovative":[47],"framework":[50],"for":[51],"point":[52],"clouds":[53],"that":[54],"integrates":[55],"contrastive":[56,75],"with":[58,73],"dimensionality":[59,83],"reduction.":[60],"Specifically,":[61],"DepthCNE":[62,107],"utilizes":[63],"a":[64,69,82,91],"3D":[65],"point-based":[66],"encoder":[67],"projection":[70],"head,":[71,85],"consistent":[72],"conventional":[74],"designs.":[77],"In":[78],"addition,":[79],"introduce":[81],"reduction":[84],"which":[86],"projects":[87],"high-dimensional":[88],"features":[89],"into":[90],"two-dimensional":[92],"to":[94],"improve":[95],"model\u2019s":[97],"representation":[98],"capability.":[99],"Extensive":[100],"experiments":[101],"demonstrate":[102],"competitive":[104],"on":[108],"both":[109],"synthetic":[110],"real-world":[112],"datasets":[113],"across":[114],"Moreover,":[118],"our":[119],"method":[120],"exhibits":[121],"promising":[122],"unsupervised":[125],"The":[127],"codes":[128],"are":[129],"available":[130],"at":[131],"https://github.com/MingyuLiu1/DepthCNE.git.":[132]},"counts_by_year":[],"updated_date":"2026-02-23T20:09:44.859080","created_date":"2025-11-14T00:00:00"}