{"id":"https://openalex.org/W7147466110","doi":"https://doi.org/10.1109/cnml68938.2026.11452487","title":"Geometric-Aware Dynamic Sampling and Feature Fidelity Compensation for Structure-Preserving Point Cloud Abstraction","display_name":"Geometric-Aware Dynamic Sampling and Feature Fidelity Compensation for Structure-Preserving Point Cloud Abstraction","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7147466110","doi":"https://doi.org/10.1109/cnml68938.2026.11452487"},"language":null,"primary_location":{"id":"doi:10.1109/cnml68938.2026.11452487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","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/A5132679706","display_name":"Chen Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chen Yang","raw_affiliation_strings":["Southwest University,College of Electronic and Information Engineering,Chongqing,China,400715"],"affiliations":[{"raw_affiliation_string":"Southwest University,College of Electronic and Information Engineering,Chongqing,China,400715","institution_ids":["https://openalex.org/I142108993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102732620","display_name":"Huiwei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I50180762","display_name":"Chongqing Three Gorges University","ror":"https://ror.org/05rs3pv16","country_code":"CN","type":"education","lineage":["https://openalex.org/I50180762"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huiwei Wang","raw_affiliation_strings":["Chongqing Three Gorges University,Key Laboratory of Intelligent Information Processing,Chongqing,China,404100"],"affiliations":[{"raw_affiliation_string":"Chongqing Three Gorges University,Key Laboratory of Intelligent Information Processing,Chongqing,China,404100","institution_ids":["https://openalex.org/I50180762"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5132679706"],"corresponding_institution_ids":["https://openalex.org/I142108993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.89978932,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"672","last_page":"675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9664000272750854,"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":0.9664000272750854,"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":0.007600000128149986,"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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.002300000051036477,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.7335000038146973},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6848999857902527},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5931000113487244},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.48010000586509705},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.47029998898506165},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42719998955726624},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.37619999051094055},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.3677000105381012},{"id":"https://openalex.org/keywords/decimation","display_name":"Decimation","score":0.3540000021457672},{"id":"https://openalex.org/keywords/alias","display_name":"Alias","score":0.34360000491142273}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.7335000038146973},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6848999857902527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6107000112533569},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5931000113487244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4900999963283539},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.48010000586509705},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.47029998898506165},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46619999408721924},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4438999891281128},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C173642442","wikidata":"https://www.wikidata.org/wiki/Q1253346","display_name":"Decimation","level":3,"score":0.3540000021457672},{"id":"https://openalex.org/C46681722","wikidata":"https://www.wikidata.org/wiki/Q4725589","display_name":"Alias","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.34049999713897705},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28760001063346863},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.27649998664855957},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.266400009393692},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26489999890327454},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C2779521785","wikidata":"https://www.wikidata.org/wiki/Q5535529","display_name":"Geometry processing","level":3,"score":0.25780001282691956},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.2574999928474426},{"id":"https://openalex.org/C89720835","wikidata":"https://www.wikidata.org/wiki/Q1531701","display_name":"Global illumination","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cnml68938.2026.11452487","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cnml68938.2026.11452487","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 International Conference on Communication Networks and Machine Learning (CNML)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1920022804","https://openalex.org/W2563408008","https://openalex.org/W2979750740","https://openalex.org/W4378191150","https://openalex.org/W4390991753","https://openalex.org/W4400445967","https://openalex.org/W4408862100","https://openalex.org/W4416111533","https://openalex.org/W7133207924"],"related_works":[],"abstract_inverted_index":{"Point":[0,18],"cloud":[1,180],"downsampling":[2,63],"is":[3],"a":[4,55,65,104,128,156,173],"fundamental":[5],"precursor":[6],"for":[7,177],"efficient":[8],"3D":[9],"scene":[10],"understanding,":[11],"yet":[12],"existing":[13],"heuristic-based":[14],"methods,":[15],"particularly":[16],"Farthest":[17],"Sampling":[19,59,77],"(FPS),":[20],"often":[21],"overlook":[22],"the":[23,40,109,124,168],"intrinsic":[24],"manifold":[25],"structure":[26],"and":[27,39,79,164],"local":[28,99],"geometric":[29,44,133],"variations.":[30],"This":[31],"leads":[32],"to":[33,92,123],"redundant":[34],"sampling":[35,95],"in":[36,113,149],"flat":[37],"regions":[38],"suppression":[41],"of":[42],"critical":[43],"primitives,":[45],"especially":[46],"under":[47],"extreme":[48,150],"sparsity.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53],"propose":[54],"novel":[56],"Geometric-Aware":[57],"Dynamic":[58],"framework":[60,144],"that":[61,142],"treats":[62],"as":[64],"structure-preserving":[66],"abstraction":[67],"task.":[68],"Our":[69],"approach":[70],"comprises":[71],"two":[72],"synergistic":[73],"modules:":[74],"Segmented":[75],"Adaptive-step":[76],"(SASS)":[78],"Geometric":[80],"Feature":[81],"Fidelity":[82],"Compensation":[83],"(GFFC).":[84],"SASS":[85],"leverages":[86],"Graph":[87],"Signal":[88],"Processing":[89],"(GSP)":[90],"principles":[91],"adaptively":[93],"allocate":[94],"density":[96],"based":[97],"on":[98,162,167],"smoothness":[100],"scores":[101],"derived":[102],"from":[103,120],"proximity":[105],"graph.":[106],"To":[107],"mitigate":[108],"information":[110],"loss":[111],"inherent":[112],"discarding":[114],"points,":[115],"GFFC":[116],"redistributes":[117],"latent":[118],"features":[119],"dropped":[121],"points":[122],"sampled":[125],"subset":[126],"via":[127],"smoothness-guided":[129],"gating":[130],"mechanism,":[131],"ensuring":[132],"context":[134],"preservation.":[135],"Experiments":[136],"across":[137],"six":[138],"representative":[139],"backbones":[140],"demonstrate":[141],"our":[143,153],"consistently":[145],"outperforms":[146],"FPS.":[147],"Notably,":[148],"sparsity":[151],"scenarios,":[152],"method":[154],"achieves":[155],"13.5%":[157],"accuracy":[158],"lead":[159],"over":[160],"FPS":[161],"ModelNet40":[163],"exhibits":[165],"robustness":[166],"real-world":[169],"ScanObjectNN":[170],"dataset,":[171],"providing":[172],"universal":[174],"\"plug-and-play\"":[175],"solution":[176],"geometry-aware":[178],"point":[179],"processing.":[181]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-04-02T00:00:00"}