{"id":"https://openalex.org/W4416748941","doi":"https://doi.org/10.1109/iros60139.2025.11247287","title":"EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving","display_name":"EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416748941","doi":"https://doi.org/10.1109/iros60139.2025.11247287"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11247287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5075170109","display_name":"Yining Shi","orcid":"https://orcid.org/0000-0002-2930-6164"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yining Shi","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102957468","display_name":"Kun Jiang","orcid":"https://orcid.org/0000-0001-7282-172X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Jiang","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043682012","display_name":"Jinyu Miao","orcid":"https://orcid.org/0000-0001-8558-9173"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyu Miao","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360104","display_name":"Ke Wang","orcid":"https://orcid.org/0000-0002-1240-3494"},"institutions":[{"id":"https://openalex.org/I61152923","display_name":"Cabot (United States)","ror":"https://ror.org/00ag9jk61","country_code":"US","type":"company","lineage":["https://openalex.org/I61152923"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ke Wang","raw_affiliation_strings":["Kargobot. Inc"],"affiliations":[{"raw_affiliation_string":"Kargobot. Inc","institution_ids":["https://openalex.org/I61152923"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099111609","display_name":"Kangan Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kangan Qian","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398951","display_name":"Yunlong Wang","orcid":"https://orcid.org/0000-0001-7705-3889"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunlong Wang","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091053496","display_name":"Jiusi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiusi Li","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020028921","display_name":"Tuopu Wen","orcid":"https://orcid.org/0000-0002-3093-765X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tuopu Wen","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100524885","display_name":"Mengmeng Yang","orcid":"https://orcid.org/0000-0003-4770-0648"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengmeng Yang","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101724345","display_name":"Yiliang Xu","orcid":"https://orcid.org/0000-0002-7714-0760"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiliang Xu","raw_affiliation_strings":["Zongmu Technology"],"affiliations":[{"raw_affiliation_string":"Zongmu Technology","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009072257","display_name":"Diange Yang","orcid":"https://orcid.org/0000-0003-0825-5609"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Diange Yang","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":11,"corresponding_author_ids":["https://openalex.org/A5075170109"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36931374,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"17008","last_page":"17015"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8661999702453613,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.8661999702453613,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.041099999099969864,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.015599999576807022,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/occupancy","display_name":"Occupancy","score":0.5473999977111816},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5343000292778015},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5256999731063843},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.47440001368522644},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4401000142097473},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4142000079154968},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.36649999022483826},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3578999936580658}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276999950408936},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6046000123023987},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.5473999977111816},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5343000292778015},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5260999798774719},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.335099995136261},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3292999863624573},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.32170000672340393},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.30630001425743103},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C2910001868","wikidata":"https://www.wikidata.org/wiki/Q458","display_name":"European union","level":2,"score":0.25060001015663147},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11247287","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325902","display_name":"Beijing Municipal Science and Technology Commission","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2795587607","https://openalex.org/W2897529137","https://openalex.org/W3035172746","https://openalex.org/W3035574168","https://openalex.org/W3138516171","https://openalex.org/W3215584334","https://openalex.org/W4312894406","https://openalex.org/W4386075892","https://openalex.org/W4386083035","https://openalex.org/W4390872638","https://openalex.org/W4390874058","https://openalex.org/W4390874310","https://openalex.org/W4393159587","https://openalex.org/W4394708612","https://openalex.org/W4401414288","https://openalex.org/W4402713134","https://openalex.org/W4402772482","https://openalex.org/W4402961717","https://openalex.org/W4403778905","https://openalex.org/W4404725701","https://openalex.org/W4405907309"],"related_works":[],"abstract_inverted_index":{"3D":[0,19],"occupancy":[1,20,170],"prediction":[2],"(3DOcc)":[3],"is":[4,224],"a":[5,156,199],"rapidly":[6],"rising":[7],"and":[8,27,59,67,92,112,124,142,178,217],"challenging":[9],"perception":[10],"task":[11],"in":[12,145],"the":[13,45,73,116,127,130,136,182,193,208,220],"field":[14],"of":[15,31,38,76,147,184,192,198],"autonomous":[16],"driving.":[17],"Existing":[18],"networks":[21],"(OccNets)":[22],"are":[23,35,50],"both":[24,176],"computationally":[25],"heavy":[26,39],"label-hungry.":[28],"In":[29],"terms":[30,146],"model":[32,132],"complexity,":[33],"OccNets":[34,49],"commonly":[36],"composed":[37],"Conv3D":[40],"modules":[41],"or":[42],"transformers":[43],"at":[44,226],"voxel":[46,56],"level.":[47],"Moreover,":[48],"supervised":[51],"with":[52,133,186,212],"expensive":[53],"large-scale":[54,120],"dense":[55],"labels.":[57],"Model":[58],"label":[60,68,93],"inefficiencies,":[61],"caused":[62],"by":[63],"excessive":[64],"network":[65,90],"parameters":[66,141],"annotation":[69],"requirements,":[70],"severely":[71],"hinder":[72],"onboard":[74],"deployment":[75],"OccNets.":[77,180],"This":[78],"paper":[79],"proposes":[80],"an":[81,102],"EFFicient":[82],"Occupancy":[83],"learning":[84,185],"framework,":[85],"EFFOcc,":[86],"that":[87,106],"targets":[88],"minimal":[89],"complexity":[91],"requirements":[94],"while":[95],"achieving":[96],"state-of-the-art":[97,117,173],"accuracy.":[98],"We":[99],"first":[100],"propose":[101,155],"efficient":[103],"fusion-based":[104,131,177,221],"OccNet":[105,203,210],"only":[107,213],"uses":[108],"simple":[109],"2D":[110],"operators":[111],"improves":[113],"accuracy":[114],"to":[115,160,164],"on":[118,169],"three":[119],"benchmarks:":[121],"Occ3D-nuScenes,":[122],"Occ3D-Waymo,":[123],"OpenOccupancy-nuScenes.":[125],"On":[126],"Occ3D-nuScenes":[128],"benchmark,":[129],"ResNet-18":[134],"as":[135],"image":[137],"backbone":[138],"has":[139],"21.35M":[140],"achieves":[143],"51.49":[144],"mean":[148],"Intersection":[149],"over":[150],"Union":[151],"(mIoU).":[152],"Furthermore,":[153],"we":[154,189],"multi-stage":[157],"occupancy-oriented":[158],"distillation":[159,218],"efficiently":[161],"transfer":[162],"knowledge":[163],"vision-only":[165],"OccNet.":[166,222],"Extensive":[167],"experiments":[168],"benchmarks":[171],"show":[172],"precision":[174],"for":[175],"vision-based":[179],"For":[181],"demonstration":[183],"limited":[187],"labels,":[188],"achieve":[190],"94.38%":[191],"performance":[194],"(mIoU":[195,204],"=":[196,205],"28.38)":[197],"100%":[200],"labeled":[201,215],"vision":[202],"30.07)":[206],"using":[207],"same":[209],"trained":[211],"40%":[214],"sequences":[216],"from":[219],"Code":[223],"available":[225],"https://github.com/synsin0/EFFOcc.":[227]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-28T00:00:00"}