{"id":"https://openalex.org/W7134819132","doi":"https://doi.org/10.48550/arxiv.2603.07593","title":"Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification","display_name":"Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification","publication_year":2026,"publication_date":"2026-03-08","ids":{"openalex":"https://openalex.org/W7134819132","doi":"https://doi.org/10.48550/arxiv.2603.07593"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.07593","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07593","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2603.07593","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128631137","display_name":"Z. Rozsa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rozsa, Z.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128656598","display_name":"\u00c1. Madaras","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madaras, \u00c1.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128638866","display_name":"Q. Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Q.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128640610","display_name":"X. L. Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, X.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128637116","display_name":"M. Golarits","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Golarits, M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128660726","display_name":"H. Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128651374","display_name":"T. Sziranyi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sziranyi, T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128679971","display_name":"R. Hamzaoui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamzaoui, R.","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":0.3544999957084656,"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":0.3544999957084656,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.27880001068115234,"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"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.05400000140070915,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.8335000276565552},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7020999789237976},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6736000180244446},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6251000165939331},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5547999739646912},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4887999892234802},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4740000069141388},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4684000015258789}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8335000276565552},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7059999704360962},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7020999789237976},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6736000180244446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6561999917030334},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6259999871253967},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6251000165939331},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5547999739646912},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4887999892234802},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4684000015258789},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.38199999928474426},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C2778999744","wikidata":"https://www.wikidata.org/wiki/Q7208292","display_name":"Point target","level":3,"score":0.3327000141143799},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.28780001401901245},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.25940001010894775},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.07593","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07593","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.07593","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07593","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"LiDAR":[0,89],"point":[1,53,84,117],"clouds":[2],"are":[3,72],"widely":[4],"used":[5],"in":[6,150],"autonomous":[7],"driving":[8],"and":[9,29,44,107,120,132,146,149],"consist":[10],"of":[11,14],"large":[12],"numbers":[13],"3D":[15,125],"points":[16],"captured":[17],"at":[18,174],"high":[19,175],"frequency":[20],"to":[21,65,103],"represent":[22],"surrounding":[23],"objects":[24],"such":[25],"as":[26],"vehicles,":[27],"pedestrians,":[28],"traffic":[30],"signs.":[31],"While":[32],"this":[33,77],"dense":[34],"data":[35],"enables":[36],"accurate":[37,70],"perception,":[38],"it":[39,168],"also":[40],"increases":[41],"computational":[42],"cost":[43],"power":[45],"consumption,":[46],"which":[47],"can":[48],"limit":[49],"real-time":[50],"deployment.":[51],"Existing":[52],"cloud":[54,85],"sampling":[55,101,118,122,176],"methods":[56,71],"typically":[57,169],"face":[58],"a":[59,94],"trade-off:":[60],"very":[61],"fast":[62],"approaches":[63],"tend":[64],"reduce":[66],"accuracy,":[67,154],"while":[68],"more":[69,172],"computationally":[73],"expensive.":[74],"To":[75],"address":[76],"limitation,":[78],"we":[79],"propose":[80],"an":[81,99],"efficient":[82],"learned":[83],"simplification":[86],"method":[87,92,114,140],"for":[88],"data.":[90],"The":[91,139],"combines":[93],"feature":[95],"embedding":[96],"module":[97,102],"with":[98,155],"attention-based":[100],"prioritize":[104],"task-relevant":[105],"regions":[106],"is":[108],"trained":[109],"end-to-end.":[110],"We":[111],"evaluate":[112],"the":[113,129,156],"against":[115],"farthest":[116],"(FPS)":[119],"random":[121],"(RS)":[123],"on":[124,128,133],"object":[126,134],"detection":[127],"KITTI":[130],"dataset":[131],"classification":[135],"across":[136],"four":[137],"datasets.":[138],"was":[141,163],"consistently":[142],"faster":[143],"than":[144,165],"FPS":[145],"achieved":[147],"similar,":[148],"some":[151],"settings":[152],"better,":[153],"largest":[157],"gains":[158],"under":[159],"aggressive":[160],"downsampling.":[161],"It":[162],"slower":[164],"RS,":[166],"but":[167],"preserved":[170],"accuracy":[171],"reliably":[173],"ratios.":[177]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-03-11T00:00:00"}
