{"id":"https://openalex.org/W7148248011","doi":"https://doi.org/10.48550/arxiv.2604.00862","title":"Shape Representation using Gaussian Process mixture models","display_name":"Shape Representation using Gaussian Process mixture models","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148248011","doi":"https://doi.org/10.48550/arxiv.2604.00862"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00862","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00862","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.2604.00862","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045901258","display_name":"Panagiotis Sapoutzoglou","orcid":"https://orcid.org/0000-0001-9885-3981"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sapoutzoglou, Panagiotis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026337731","display_name":"George Terzakis","orcid":"https://orcid.org/0000-0002-6959-5378"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Terzakis, George","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132776742","display_name":"Georgios Floros","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Floros, Georgios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132806272","display_name":"Maria Pateraki","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pateraki, Maria","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":0.4302999973297119,"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":0.4302999973297119,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.4083000123500824,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.018799999728798866,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6869999766349792},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5989000201225281},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5666000247001648},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5575000047683716},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.491100013256073},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.4871000051498413},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.48649999499320984},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4584999978542328},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4489000141620636}],"concepts":[{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6869999766349792},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5989000201225281},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5666000247001648},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5575000047683716},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5565000176429749},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49459999799728394},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.491100013256073},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.4871000051498413},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4848000109195709},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4584999978542328},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4478999972343445},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38260000944137573},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3203999996185303},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C29123130","wikidata":"https://www.wikidata.org/wiki/Q874709","display_name":"Computational geometry","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C7305733","wikidata":"https://www.wikidata.org/wiki/Q207961","display_name":"Geometric shape","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00862","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00862","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.2604.00862","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00862","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":[{"score":0.6568025350570679,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"explicit":[1],"3D":[2],"representations,":[3],"such":[4],"as":[5],"point":[6,79],"clouds":[7],"and":[8,18,32,113,122],"meshes,":[9],"demand":[10],"significant":[11],"storage":[12],"to":[13,70],"capture":[14,82],"fine":[15],"geometric":[16],"details":[17],"require":[19],"complex":[20,83,125],"indexing":[21],"systems":[22],"for":[23],"surface":[24,48],"lookups,":[25],"making":[26],"functional":[27,43],"representations":[28],"an":[29],"efficient,":[30],"compact,":[31],"continuous":[33,72],"alternative.":[34],"In":[35],"this":[36],"work,":[37],"we":[38],"propose":[39],"a":[40],"novel,":[41],"object-specific":[42],"shape":[44],"representation":[45],"that":[46,117],"models":[47],"geometry":[49],"with":[50],"Gaussian":[51],"Process":[52],"(GP)":[53],"mixture":[54],"models.":[55],"Rather":[56],"than":[57],"relying":[58],"on":[59,110],"computationally":[60],"heavy":[61],"neural":[62],"architectures,":[63],"our":[64,118],"method":[65,103,119],"is":[66],"lightweight,":[67],"leveraging":[68],"GPs":[69],"learn":[71],"directional":[73],"distance":[74],"fields":[75],"from":[76],"sparsely":[77],"sampled":[78],"clouds.":[80],"We":[81],"topologies":[84],"by":[85],"anchoring":[86],"local":[87],"GP":[88],"priors":[89],"at":[90],"strategic":[91],"reference":[92],"points,":[93],"which":[94],"can":[95,120],"be":[96],"flexibly":[97],"extracted":[98],"using":[99],"any":[100],"structural":[101],"decomposition":[102],"(e.g.":[104],"skeletonization,":[105],"distance-based":[106],"clustering).":[107],"Extensive":[108],"evaluations":[109],"the":[111],"ShapeNetCore":[112],"IndustryShapes":[114],"datasets":[115],"demonstrate":[116],"efficiently":[121],"accurately":[123],"represent":[124],"geometries.":[126]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-03T00:00:00"}