{"id":"https://openalex.org/W7127152993","doi":"https://doi.org/10.48550/arxiv.2601.22729","title":"GaussianOcc3D: A Gaussian-Based Adaptive Multi-modal 3D Occupancy Prediction","display_name":"GaussianOcc3D: A Gaussian-Based Adaptive Multi-modal 3D Occupancy Prediction","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7127152993","doi":"https://doi.org/10.48550/arxiv.2601.22729"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2601.22729","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124768801","display_name":"A. Enes Doruk","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Doruk, A. Enes","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124851451","display_name":"Hasan F. Ates","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ates, Hasan F.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5124768801"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.34860000014305115,"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":0.34860000014305115,"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":0.21780000627040863,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.10480000078678131,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6060000061988831},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5773000121116638},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5146999955177307},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4287000000476837},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4180000126361847},{"id":"https://openalex.org/keywords/usable","display_name":"USable","score":0.4162999987602234},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.3953000009059906},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.3939000070095062},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3758000135421753},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.36340001225471497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214000225067139},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6686000227928162},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6060000061988831},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5773000121116638},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5695000290870667},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5146999955177307},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.3546000123023987},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.30889999866485596},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C139002025","wikidata":"https://www.wikidata.org/wiki/Q3001212","display_name":"Lift (data mining)","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.28360000252723694},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.28189998865127563},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26989999413490295},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.2522999942302704},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2601.22729","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2601.22729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.22729","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2601.22729","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":[0,69],"semantic":[1],"occupancy":[2],"prediction":[3],"is":[4],"a":[5,12,58,66,115],"pivotal":[6],"task":[7],"in":[8],"autonomous":[9],"driving,":[10],"providing":[11],"dense":[13],"and":[14,29,42,50,63,113,133,145,155],"fine-grained":[15],"understanding":[16],"of":[17,142],"the":[18,43],"surrounding":[19],"environment,":[20],"yet":[21],"single-modality":[22],"methods":[23],"face":[24],"trade-offs":[25],"between":[26],"camera":[27,62],"semantics":[28],"LiDAR":[30,64,77],"geometry.":[31],"Existing":[32],"multi-modal":[33,59],"frameworks":[34],"often":[35],"struggle":[36],"with":[37,107,126],"modality":[38],"heterogeneity,":[39],"spatial":[40],"misalignment,":[41],"representation":[44],"crisis--where":[45],"voxels":[46],"are":[47,53],"computationally":[48],"heavy":[49],"BEV":[51],"alternatives":[52],"lossy.":[54],"We":[55,72],"present":[56],"GaussianOcc3D,":[57],"framework":[60],"bridging":[61],"through":[65],"memory-efficient,":[67],"continuous":[68],"Gaussian":[70,91],"representation.":[71],"introduce":[73],"four":[74],"modules:":[75],"(1)":[76],"Depth":[78],"Feature":[79,95],"Aggregation":[80],"(LDFA),":[81],"using":[82],"depth-wise":[83],"deformable":[84],"sampling":[85],"to":[86,98],"lift":[87],"sparse":[88],"signals":[89],"onto":[90],"primitives;":[92],"(2)":[93],"Entropy-Based":[94],"Smoothing":[96],"(EBFS)":[97],"mitigate":[99],"domain":[100],"noise;":[101],"(3)":[102],"Adaptive":[103],"Camera-LiDAR":[104],"Fusion":[105],"(ACLF)":[106],"uncertainty-aware":[108],"reweighting":[109],"for":[110,123],"sensor":[111],"reliability;":[112],"(4)":[114],"Gauss-Mamba":[116],"Head":[117],"leveraging":[118],"Selective":[119],"State":[120],"Space":[121],"Models":[122],"global":[124],"context":[125],"linear":[127],"complexity.":[128],"Evaluations":[129],"on":[130],"Occ3D,":[131],"SurroundOcc,":[132],"SemanticKITTI":[134],"benchmarks":[135],"demonstrate":[136],"state-of-the-art":[137],"performance,":[138],"achieving":[139],"mIoU":[140],"scores":[141],"49.4%,":[143],"28.9%,":[144],"25.2%":[146],"respectively.":[147],"GaussianOcc3D":[148],"exhibits":[149],"superior":[150],"robustness":[151],"across":[152],"challenging":[153],"rainy":[154],"nighttime":[156],"conditions.":[157]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-03T00:00:00"}