{"id":"https://openalex.org/W4415708095","doi":"https://doi.org/10.1109/icme59968.2025.11210187","title":"SFRP: Fine-Grained Point Cloud Classification via Interaction of Spatial and Feature Representation Points","display_name":"SFRP: Fine-Grained Point Cloud Classification via Interaction of Spatial and Feature Representation Points","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708095","doi":"https://doi.org/10.1109/icme59968.2025.11210187"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11210187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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":"https://openalex.org/A5007573115","display_name":"Haoxiang Sun","orcid":"https://orcid.org/0000-0002-5622-6610"},"institutions":[{"id":"https://openalex.org/I52158045","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14","country_code":"CN","type":"education","lineage":["https://openalex.org/I52158045"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haoxiang Sun","raw_affiliation_strings":["China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083","institution_ids":["https://openalex.org/I52158045"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100427637","display_name":"Xiaomeng Li","orcid":"https://orcid.org/0000-0002-7811-3457"},"institutions":[{"id":"https://openalex.org/I52158045","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14","country_code":"CN","type":"education","lineage":["https://openalex.org/I52158045"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaomeng Li","raw_affiliation_strings":["China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083","institution_ids":["https://openalex.org/I52158045"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111434634","display_name":"Yuan Ding","orcid":"https://orcid.org/0009-0009-4956-0889"},"institutions":[{"id":"https://openalex.org/I52158045","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14","country_code":"CN","type":"education","lineage":["https://openalex.org/I52158045"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanhao Ding","raw_affiliation_strings":["China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083","institution_ids":["https://openalex.org/I52158045"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023771190","display_name":"Qian Sun","orcid":"https://orcid.org/0000-0001-6341-6036"},"institutions":[{"id":"https://openalex.org/I52158045","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14","country_code":"CN","type":"education","lineage":["https://openalex.org/I52158045"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Sun","raw_affiliation_strings":["China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083","institution_ids":["https://openalex.org/I52158045"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017050955","display_name":"Zhenbo Li","orcid":"https://orcid.org/0000-0002-9350-6769"},"institutions":[{"id":"https://openalex.org/I52158045","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14","country_code":"CN","type":"education","lineage":["https://openalex.org/I52158045"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenbo Li","raw_affiliation_strings":["China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083"],"affiliations":[{"raw_affiliation_string":"China Agricultural University,College of Information and Electrical Engineering,Beijing,China,100083","institution_ids":["https://openalex.org/I52158045"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5007573115"],"corresponding_institution_ids":["https://openalex.org/I52158045"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32716639,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.857699990272522,"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":0.857699990272522,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.019300000742077827,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.006399999838322401,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7171000242233276},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.7150999903678894},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7113999724388123},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6796000003814697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5870000123977661},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5740000009536743},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5327000021934509},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5088000297546387},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4884999990463257}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7171000242233276},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7150999903678894},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7113999724388123},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6796000003814697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6410999894142151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6258000135421753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5870000123977661},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5740000009536743},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5327000021934509},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5088000297546387},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.397599995136261},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C101814296","wikidata":"https://www.wikidata.org/wiki/Q5439685","display_name":"Feature model","level":3,"score":0.3353999853134155},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32589998841285706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32170000672340393},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.27630001306533813},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11210187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11210187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321131","display_name":"China Agricultural University","ror":"https://ror.org/04v3ywz14"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1920022804","https://openalex.org/W2960986959","https://openalex.org/W2963231572","https://openalex.org/W2979750740","https://openalex.org/W3031112623","https://openalex.org/W4214755140","https://openalex.org/W4285268677","https://openalex.org/W4361011810","https://openalex.org/W4382468867","https://openalex.org/W4386065814","https://openalex.org/W4386083138","https://openalex.org/W4391032497","https://openalex.org/W4393033549","https://openalex.org/W4402702980","https://openalex.org/W4402979820","https://openalex.org/W4402980203","https://openalex.org/W4403976960"],"related_works":[],"abstract_inverted_index":{"Point":[0,45],"cloud":[1,105],"classification":[2,106],"is":[3],"critical":[4],"for":[5,112],"understanding":[6],"three-dimensional":[7],"shapes.":[8],"While":[9],"current":[10],"methods":[11,29],"focus":[12],"on":[13,57,122,132],"coarse-grained":[14,134],"category":[15],"recognition,":[16],"real-world":[17],"applications":[18],"require":[19],"identifying":[20],"fine-grained":[21,103,118],"subcategories":[22],"with":[23],"subtle":[24,89],"differences.":[25],"Existing":[26],"deep":[27],"learning-based":[28],"struggle":[30],"to":[31,116],"effectively":[32,85],"capture":[33],"discriminative":[34],"features.":[35],"To":[36],"address":[37],"this":[38,58],"challenge,":[39],"we":[40,60,96],"developed":[41],"a":[42,62,110],"Feature":[43],"Representation":[44],"Sampling":[46],"(FRPS)":[47],"module":[48],"using":[49],"attention":[50],"mechanisms":[51],"and":[52,71,87,99,148],"feature":[53,72],"correlation":[54],"coefficients.":[55],"Based":[56],"module,":[59],"proposed":[61],"novel":[63],"two-branch":[64],"network":[65],"SFRP,":[66],"that":[67,125,138],"simultaneously":[68],"processes":[69],"spatial":[70],"representation":[73],"point":[74,93,104,119],"information.":[75],"By":[76],"integrating":[77],"these":[78],"types":[79],"of":[80],"information,":[81],"our":[82],"SFRP":[83,126,140],"model":[84,141],"captures":[86],"learns":[88],"shape":[90],"differences":[91],"in":[92],"clouds.":[94,120],"Furthermore,":[95],"have":[97],"collected":[98],"released":[100],"the":[101,133,139],"first":[102],"dataset,":[107],"FG3DPoint,":[108],"as":[109],"benchmark":[111],"evaluating":[113],"models\u2019":[114],"abilities":[115],"recognize":[117],"Experiments":[121],"FG3DPoint":[123],"demonstrate":[124],"achieves":[127],"SOTA":[128],"performance.":[129,145],"Additionally,":[130],"experiments":[131],"ModelNet40":[135],"dataset":[136],"confirm":[137],"exhibits":[142],"robust":[143],"generalization":[144],"Our":[146],"code":[147],"data":[149],"are":[150],"available":[151],"at":[152],"https://github.com/foreshadowx/SFRP.":[153]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-30T00:00:00"}
