{"id":"https://openalex.org/W7147181235","doi":"https://doi.org/10.48550/arxiv.2603.27294","title":"Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving","display_name":"Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving","publication_year":2026,"publication_date":"2026-03-28","ids":{"openalex":"https://openalex.org/W7147181235","doi":"https://doi.org/10.48550/arxiv.2603.27294"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27294","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27294","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":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.2603.27294","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132645585","display_name":"Wonjune Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Wonjune","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132597636","display_name":"In-Jae Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, In-Jae","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102531112","display_name":"Sihwan Hwang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hwang, Sihwan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032573421","display_name":"Sanmin Kim","orcid":"https://orcid.org/0000-0002-4042-6570"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Sanmin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5091555350","display_name":"Dongsuk Kum","orcid":"https://orcid.org/0000-0002-2590-4845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kum, Dongsuk","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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.27129998803138733,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.27129998803138733,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.17190000414848328,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.1665000021457672,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5935999751091003},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.5437999963760376},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5418999791145325},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5354999899864197},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5072000026702881},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.47589999437332153},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3571999967098236},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.35600000619888306},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.35420000553131104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7211999893188477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6563000082969666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6132000088691711},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.5437999963760376},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5354999899864197},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5072000026702881},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.47589999437332153},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3571999967098236},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C197947376","wikidata":"https://www.wikidata.org/wiki/Q5155608","display_name":"Comparability","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.34360000491142273},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34150001406669617},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3368000090122223},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C193611912","wikidata":"https://www.wikidata.org/wiki/Q4677596","display_name":"Active vision","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.28540000319480896},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C2781411174","wikidata":"https://www.wikidata.org/wiki/Q8034662","display_name":"Workcell","level":3,"score":0.2624000012874603},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27294","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27294","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":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.2603.27294","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27294","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":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":{"3D":[0],"occupancy":[1],"prediction":[2],"provides":[3],"dense":[4],"spatial":[5],"understanding":[6],"critical":[7],"for":[8,65],"safe":[9],"autonomous":[10,72],"driving.":[11],"However,":[12],"this":[13],"task":[14],"suffers":[15],"from":[16,95],"a":[17,59,131,177],"severe":[18],"class":[19,92,123],"imbalance":[20],"due":[21],"to":[22,37,48,69,81,158],"its":[23],"volumetric":[24],"representation,":[25],"where":[26],"safety-critical":[27],"objects":[28],"(bicycles,":[29],"traffic":[30],"cones,":[31],"pedestrians)":[32],"occupy":[33],"minimal":[34],"voxels":[35],"compared":[36],"dominant":[38,49],"backgrounds.":[39],"Additionally,":[40],"voxel-level":[41,118],"annotation":[42],"is":[43,51],"costly,":[44],"yet":[45],"dedicating":[46],"effort":[47],"classes":[50,115],"inefficient.":[52],"To":[53],"address":[54],"these":[55],"challenges,":[56],"we":[57],"propose":[58],"class-distribution":[60],"guided":[61],"active":[62,163],"learning":[63,164],"framework":[64,153],"selecting":[66],"training":[67,84],"samples":[68,89],"annotate":[70],"in":[71],"driving":[73],"datasets.":[74,184],"Our":[75],"approach":[76],"combines":[77],"three":[78],"complementary":[79],"criteria":[80],"select":[82],"the":[83,98,167],"samples.":[85],"Inter-sample":[86],"diversity":[87,102],"prioritizes":[88],"whose":[90],"predicted":[91],"distributions":[93],"differ":[94],"those":[96],"of":[97,136],"labeled":[99,150],"set,":[100],"intra-set":[101],"prevents":[103],"redundant":[104],"sampling":[105],"within":[106],"each":[107],"acquisition":[108],"cycle,":[109],"and":[110,142,161],"frequency-weighted":[111],"uncertainty":[112],"emphasizes":[113],"rare":[114],"by":[116,129],"reweighting":[117],"entropy":[119],"with":[120],"inverse":[121],"per-sample":[122],"proportions.":[124],"We":[125,170],"ensure":[126],"evaluation":[127],"validity":[128],"using":[130,176],"geographically":[132],"disjoint":[133],"train/validation":[134],"split":[135],"Occ3D-nuScenes,":[137],"which":[138],"reduces":[139],"train-validation":[140],"overlap":[141],"mitigates":[143],"potential":[144],"map":[145],"memorization.":[146],"With":[147],"only":[148],"42.4%":[149],"data,":[151],"our":[152],"reaches":[154],"26.62":[155],"mIoU,":[156],"comparable":[157],"full":[159],"supervision":[160],"outperforming":[162],"baselines":[165],"at":[166],"same":[168],"budget.":[169],"further":[171],"validate":[172],"generality":[173],"on":[174],"SemanticKITTI":[175],"different":[178],"architecture,":[179],"demonstrating":[180],"consistent":[181],"effectiveness":[182],"across":[183]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
