{"id":"https://openalex.org/W7084747528","doi":"https://doi.org/10.1109/lsp.2025.3616643","title":"ASRL: Adaptive Sparse Representation Learning for LiDAR Point Cloud Geometry Compression","display_name":"ASRL: Adaptive Sparse Representation Learning for LiDAR Point Cloud Geometry Compression","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7084747528","doi":"https://doi.org/10.1109/lsp.2025.3616643"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2025.3616643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3616643","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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":"Xinjie Wang","orcid":"https://orcid.org/0009-0001-9392-3802"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinjie Wang","raw_affiliation_strings":["College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China"],"raw_orcid":"https://orcid.org/0009-0001-9392-3802","affiliations":[{"raw_affiliation_string":"College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ke Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Xu","raw_affiliation_strings":["College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bin Deng","orcid":"https://orcid.org/0000-0002-0289-3469"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Deng","raw_affiliation_strings":["College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0289-3469","affiliations":[{"raw_affiliation_string":"College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yulan Guo","orcid":"https://orcid.org/0000-0003-0952-476X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulan Guo","raw_affiliation_strings":["School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-0952-476X","affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hanyun Wang","orcid":"https://orcid.org/0000-0002-8320-4230"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanyun Wang","raw_affiliation_strings":["School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-8320-4230","affiliations":[{"raw_affiliation_string":"School of Electronics and Communication Engineering, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.50267437,"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":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10141","display_name":"Probiotics and Fermented Foods","score":0.4422999918460846,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10141","display_name":"Probiotics and Fermented Foods","score":0.4422999918460846,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10066","display_name":"Gut microbiota and health","score":0.07199999690055847,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.048700001090765,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6955000162124634},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5030999779701233},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.48890000581741333},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4465999901294708},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4462999999523163},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4447999894618988},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.42890000343322754},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4032999873161316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39320001006126404}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.703000009059906},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6955000162124634},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.491100013256073},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4447999894618988},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39320001006126404},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37400001287460327},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.37130001187324524},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3643999993801117},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.3578000068664551},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34869998693466187},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2025.3616643","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2025.3616643","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Octree-structured":[0],"context":[1,72,81],"entropy":[2],"models":[3],"with":[4,159],"Transformers":[5],"have":[6],"demonstrated":[7],"strong":[8,177],"performance":[9,175],"in":[10,70],"LiDAR":[11,74,155],"point":[12,156],"cloud":[13,157],"geometry":[14],"compression":[15,174],"(PCGC)":[16],"by":[17],"capturing":[18],"long-range":[19],"dependencies,":[20],"which":[21,89,118,138],"are":[22],"crucial":[23],"for":[24,73],"superior":[25],"rate-distortion":[26],"(R-D)":[27],"performance.":[28],"While":[29],"existing":[30],"efficient":[31,102],"attention":[32,123],"mechanisms":[33],"reduce":[34],"computational":[35],"cost,":[36],"they":[37],"often":[38],"process":[39,84],"irrelevant":[40,64],"nodes,":[41],"introducing":[42],"redundancy":[43,69],"and":[44,66,121,132,144,166,176],"noise.":[45],"In":[46],"this":[47],"work,":[48],"we":[49,77],"propose":[50],"an":[51],"Adaptive":[52,95],"Sparse":[53,96,114],"Representation":[54],"Learning":[55],"(ASRL)":[56],"framework":[57],"to":[58,83,100,128],"mitigate":[59],"the":[60,85],"noisy":[61],"interactions":[62],"of":[63],"nodes":[65],"remove":[67],"feature":[68,104],"cross-spatial-channel":[71],"PCGC.":[75],"Specifically,":[76],"employ":[78],"a":[79],"sliding":[80],"window":[82],"resulting":[86],"1D":[87],"sequence,":[88],"is":[90],"then":[91],"fed":[92],"into":[93],"multiple":[94],"Transformer":[97],"(AST)":[98],"blocks":[99],"enable":[101],"contextual":[103],"representation":[105],"learning.":[106],"Each":[107],"AST":[108],"block":[109],"contains":[110],"two":[111],"components:":[112],"Adaptively-enhanced":[113],"Spatial":[115],"Interaction":[116],"(ASSI),":[117],"combines":[119],"dense":[120],"sparse":[122],"branches":[124],"through":[125],"adaptive":[126],"weighting":[127],"model":[129],"global-local":[130],"interactions,":[131],"Locally-enhanced":[133],"Harmonized":[134],"Channel":[135],"Mixer":[136],"(LHCM),":[137],"aggregates":[139],"features":[140],"across":[141,179],"refinement":[142],"scales":[143],"harmonizes":[145],"channel-wise":[146],"information":[147],"via":[148],"complementary":[149],"interactions.":[150],"Experiments":[151],"on":[152],"large-scale":[153],"real-world":[154],"datasets":[158],"varying":[160],"sparsity":[161],"levels":[162],"including":[163],"SemanticKITTI":[164],"(64-beam)":[165],"Ford":[167],"(32-beam)":[168],"show":[169],"that":[170],"ASRL":[171],"achieves":[172],"state-of-the-art":[173],"generalization":[178],"beam":[180],"configurations.":[181]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}