{"id":"https://openalex.org/W7124800948","doi":"https://doi.org/10.48550/arxiv.2601.11396","title":"SUG-Occ: Explicit Semantics and Uncertainty Guided Sparse Learning for Efficient 3D Occupancy Prediction","display_name":"SUG-Occ: Explicit Semantics and Uncertainty Guided Sparse Learning for Efficient 3D Occupancy Prediction","publication_year":2026,"publication_date":"2026-01-16","ids":{"openalex":"https://openalex.org/W7124800948","doi":"https://doi.org/10.48550/arxiv.2601.11396"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.11396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11396","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.2601.11396","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123335279","display_name":"Hanlin Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Hanlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123333009","display_name":"Pengfei Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Pengfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123334779","display_name":"Ehsan Javanmardi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Javanmardi, Ehsan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123313044","display_name":"Nanren Bao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bao, Naren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123323846","display_name":"Bo Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123321401","display_name":"Hao Si","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Si, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123333482","display_name":"Manabu Tsukada","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsukada, Manabu","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":0.656000018119812,"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":0.656000018119812,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.17509999871253967,"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":0.054099999368190765,"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/semantics","display_name":"Semantics (computer science)","score":0.6643999814987183},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5918999910354614},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.5212000012397766},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5004000067710876},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5002999901771545},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4964999854564667},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4862000048160553},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4740000069141388},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4562999904155731},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.4307999908924103}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7590000033378601},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6643999814987183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6189000010490417},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5918999910354614},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.5212000012397766},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5004000067710876},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5002999901771545},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4964999854564667},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4862000048160553},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.4307999908924103},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.42890000343322754},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4174000024795532},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.39399999380111694},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3718999922275543},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.3458999991416931},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.34060001373291016},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.33379998803138733},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.33219999074935913},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.3043999969959259},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.29089999198913574},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.28839999437332153},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.2768000066280365},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2750999927520752},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2720000147819519},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.2653999924659729}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.11396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11396","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.2601.11396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11396","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"3D":[0,72],"semantic":[1,21,82,88],"occupancy":[2,64],"prediction":[3],"has":[4],"emerged":[5],"as":[6],"a":[7,30,40,111,118],"critical":[8],"perception":[9],"task":[10],"for":[11,33,62],"autonomous":[12],"driving":[13],"due":[14],"to":[15,18,43,74,92,105,123],"its":[16],"ability":[17],"offer":[19],"voxel-level":[20],"and":[22,56,81,89,139,174,191],"geometric":[23,80,107],"understanding":[24],"of":[25,71],"the":[26,68,129,155,179,183],"environment.":[27],"However,":[28],"such":[29],"refined":[31],"representation":[32,131,148],"large-scale":[34],"scenes":[35,73],"incurs":[36],"prohibitive":[37],"computation,":[38],"posing":[39],"significant":[41],"challenge":[42],"practical":[44],"real-time":[45],"deployment.":[46],"To":[47],"address":[48],"this,":[49],"we":[50,85,116,143],"propose":[51,144],"SUGOcc,":[52],"an":[53,145],"explicit":[54,101],"semantics":[55],"uncertainty":[57,90],"guided":[58],"sparse":[59,113,120,130,135],"learning":[60],"framework":[61],"efficient":[63,125],"prediction,":[65],"which":[66],"exploits":[67],"inherent":[69],"sparsity":[70],"reduce":[75],"redundant":[76],"computation":[77],"while":[78,99],"maintaining":[79],"integrity.":[83],"Specifically,":[84],"first":[86],"utilize":[87],"priors":[91],"suppress":[93],"image":[94],"projections":[95],"from":[96],"free":[97],"space":[98],"employing":[100],"unsigned":[102],"distance":[103],"encoding":[104],"enhance":[106],"consistency,":[108],"thereby":[109,162],"producing":[110],"structurally":[112],"representation.":[114],"Secondly,":[115],"introduce":[117],"cascade":[119],"completion":[121],"module":[122],"enable":[124],"coarse-to-fine":[126],"reasoning":[127],"over":[128,167],"via":[132],"hyper":[133],"cross":[134],"convolution,":[136],"generative":[137],"upsampling":[138],"adaptive":[140],"pruning.":[141],"Finally,":[142],"object":[146],"contextual":[147],"(OCR)":[149],"based":[150],"mask":[151],"decoder":[152],"that":[153,178],"refines":[154],"voxel-wise":[156],"predictions":[157],"through":[158],"lightweight":[159],"query-context":[160],"interactions,":[161],"avoiding":[163],"expensive":[164],"attention":[165],"operations":[166],"volumetric":[168],"features.":[169],"Extensive":[170],"experiments":[171],"on":[172],"SemanticKITTI":[173],"Occ3D-Nuscenes":[175],"benchmark":[176],"demonstrate":[177],"proposed":[180],"approach":[181],"outperforms":[182],"baselines,":[184],"achieving":[185],"notable":[186],"improvements":[187],"in":[188],"both":[189],"accuracy":[190],"efficiency":[192],"across":[193],"datasets.":[194]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-01-20T00:00:00"}
