{"id":"https://openalex.org/W7166861538","doi":"https://doi.org/10.48550/arxiv.2606.30754","title":"Streaming Gaussian Encoding for 4D Panoptic Occupancy Tracking","display_name":"Streaming Gaussian Encoding for 4D Panoptic Occupancy Tracking","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166861538","doi":"https://doi.org/10.48550/arxiv.2606.30754"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.30754","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30754","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.2606.30754","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085701469","display_name":"Maximilian Luz","orcid":"https://orcid.org/0000-0002-1123-6604"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luz, Maximilian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064152637","display_name":"Thomas N\u00fcrnberg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N\u00fcrnberg, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090463628","display_name":"Yakov Miron","orcid":"https://orcid.org/0000-0002-2582-5098"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miron, Yakov","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139807046","display_name":"Abhinav Valada","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valada, Abhinav","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/T10531","display_name":"Advanced Vision and Imaging","score":0.3813999891281128,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.3813999891281128,"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/T11448","display_name":"Face recognition and analysis","score":0.07479999959468842,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.06419999897480011,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5515999794006348},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.46369999647140503},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.4629000127315521},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.45190000534057617},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.41760000586509705},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.38749998807907104},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.359499990940094},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.35920000076293945},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3580999970436096},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.35040000081062317}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.766700029373169},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.609499990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5529999732971191},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5515999794006348},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.46369999647140503},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.4629000127315521},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.45190000534057617},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.38749998807907104},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3472000062465668},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.3093000054359436},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.30000001192092896},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C166550679","wikidata":"https://www.wikidata.org/wiki/Q263400","display_name":"Gaussian network model","level":3,"score":0.28279998898506165},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C83248878","wikidata":"https://www.wikidata.org/wiki/Q344000","display_name":"Active appearance model","level":3,"score":0.27880001068115234},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2540000081062317},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.25360000133514404},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.30754","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30754","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.2606.30754","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30754","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Camera-based":[0],"4D":[1],"panoptic":[2],"occupancy":[3],"tracking":[4,165],"(4D-POT)":[5],"is":[6],"a":[7,69,75,88,104,127,137,158],"promising":[8],"paradigm":[9],"for":[10,59,80,120,161],"holistic":[11],"scene":[12,61,78,86,133],"understanding":[13],"from":[14],"multi-view":[15],"imagery,":[16],"enabling":[17,122],"joint":[18],"reasoning":[19],"about":[20],"geometry,":[21],"semantics,":[22],"and":[23,58,101,155,182],"object":[24],"identities":[25],"across":[26,38],"time.":[27],"Recent":[28],"mask-based":[29,177],"pipelines":[30],"achieve":[31],"strong":[32],"performance":[33],"by":[34],"propagating":[35],"instance":[36],"queries":[37,94],"frames.":[39],"However,":[40],"their":[41],"underlying":[42],"volumetric":[43,77],"representations":[44],"are":[45,96],"typically":[46],"recomputed":[47],"at":[48,184],"each":[49],"timestep,":[50],"limiting":[51],"geometric":[52],"temporal":[53,145],"consistency,":[54],"particularly":[55],"under":[56,103],"occlusion":[57],"static":[60],"elements.":[62],"To":[63],"address":[64],"this":[65,142],"limitation,":[66],"we":[67,109],"propose":[68],"streaming":[70],"Gaussian":[71,93,111],"encoder":[72],"that":[73,95],"maintains":[74],"persistent":[76,132],"representation":[79],"4D-POT.":[81],"Our":[82],"method":[83],"models":[84,183],"the":[85],"as":[87,118,126],"fixed-size":[89],"set":[90],"of":[91,131],"latent":[92],"propagated":[97],"via":[98],"ego-motion":[99],"compensation":[100],"refreshed":[102],"confidence-guided":[105],"budget":[106],"constraint.":[107],"Crucially,":[108],"shape":[110],"opacities":[112],"through":[113],"depth-based":[114],"supervision":[115],"to":[116,124],"serve":[117],"proxy":[119],"visibility,":[121],"confidence":[123],"accumulate":[125],"temporally":[128],"aggregated":[129],"measure":[130],"support.":[134],"Together":[135],"with":[136,167,175],"warmup-based":[138],"multi-frame":[139],"training":[140],"strategy,":[141],"yields":[143],"representation-level":[144],"coherence":[146],"beyond":[147],"decoder-only":[148],"tracking.":[149],"Extensive":[150],"experiments":[151],"on":[152],"Occ3D-extended":[153],"nuScenes":[154],"Waymo":[156],"establish":[157],"new":[159],"state-of-the-art":[160],"camera-based":[162],"4D-POT,":[163],"improving":[164],"consistency":[166],"negligible":[168],"computational":[169],"overhead":[170],"while":[171],"remaining":[172],"fully":[173],"compatible":[174],"existing":[176],"pipelines.":[178],"We":[179],"provide":[180],"code":[181],"https://sge.cs.uni-freiburg.de.":[185]},"counts_by_year":[],"updated_date":"2026-07-02T06:18:51.028212","created_date":"2026-07-02T00:00:00"}
