{"id":"https://openalex.org/W7154386006","doi":"https://doi.org/10.48550/arxiv.2604.11401","title":"GS4City: Hierarchical Semantic Gaussian Splatting via City-Model Priors","display_name":"GS4City: Hierarchical Semantic Gaussian Splatting via City-Model Priors","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154386006","doi":"https://doi.org/10.48550/arxiv.2604.11401"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11401","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11401","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.11401","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133617652","display_name":"Qilin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133576941","display_name":"Jinyu Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Jinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071722566","display_name":"Olaf Wysocki","orcid":"https://orcid.org/0000-0002-0016-0229"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wysocki, Olaf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067135033","display_name":"Benjamin Busam","orcid":"https://orcid.org/0000-0002-0620-5774"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Busam, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133623834","display_name":"Boris Jutzi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jutzi, Boris","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/T12698","display_name":"3D Modeling in Geospatial Applications","score":0.5853999853134155,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12698","display_name":"3D Modeling in Geospatial Applications","score":0.5853999853134155,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.3443000018596649,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental 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":0.01600000075995922,"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/citygml","display_name":"CityGML","score":0.6363000273704529},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5649999976158142},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5570999979972839},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5467000007629395},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4546999931335449},{"id":"https://openalex.org/keywords/identity","display_name":"Identity (music)","score":0.44440001249313354},{"id":"https://openalex.org/keywords/3d-city-models","display_name":"3D city models","score":0.427700012922287},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.3540000021457672}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7982000112533569},{"id":"https://openalex.org/C2776744651","wikidata":"https://www.wikidata.org/wiki/Q1093900","display_name":"CityGML","level":3,"score":0.6363000273704529},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5570999979972839},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5460000038146973},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4546999931335449},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C2778597888","wikidata":"https://www.wikidata.org/wiki/Q172169","display_name":"3D city models","level":3,"score":0.427700012922287},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4101000130176544},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3540000021457672},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2660999894142151},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11401","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11401","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11401","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11401","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.768662691116333}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"semantic":[1,52,146,169],"3D":[2,120],"Gaussian":[3,45,53,113,140,178],"Splatting":[4,54],"(3DGS)":[5],"methods":[6],"primarily":[7],"rely":[8],"on":[9,124],"2D":[10,116],"foundation":[11],"models,":[12],"often":[13],"yielding":[14],"ambiguous":[15],"boundaries":[16],"and":[17,86,105,119,127,151,163,176,185],"limited":[18],"support":[19],"for":[20,60,111],"structured":[21,136,173],"urban":[22,61,187],"semantics.":[23],"While":[24],"city":[25,174],"models":[26,76,175],"such":[27],"as":[28],"CityGML":[29,75],"encode":[30],"hierarchically":[31],"organized":[32],"semantics":[33,138],"together":[34],"with":[35,97],"building":[36,137,161],"geometry,":[37],"these":[38,94],"labels":[39],"cannot":[40],"be":[41],"directly":[42],"mapped":[43],"to":[44,84,100,155],"primitives.":[46],"We":[47],"present":[48],"GS4City,":[49],"a":[50,107],"hierarchical":[51],"method":[55],"that":[56,132],"incorporates":[57,135],"city-model":[58],"priors":[59],"scene":[62,141,179],"understanding.":[63],"GS4City":[64,133,181],"derives":[65],"reliable":[66],"image-aligned":[67],"masks":[68,96],"from":[69],"Level":[70],"of":[71],"Detail":[72],"(LoD)":[73],"3":[74],"via":[77],"two-pass":[78],"raycasting,":[79],"explicitly":[80],"using":[81],"parent-child":[82],"relations":[83],"validate":[85],"recover":[87],"fine-grained":[88,168],"facade":[89],"elements.":[90],"It":[91],"then":[92],"fuses":[93],"geometry-grounded":[95],"foundation-model":[98],"predictions":[99],"establish":[101],"scene-consistent":[102],"instance":[103],"correspondences,":[104],"learns":[106],"compact":[108],"identity":[109,117],"encoding":[110],"each":[112],"under":[114],"joint":[115],"supervision":[118],"spatial":[121],"regularization.":[122],"Experiments":[123],"the":[125],"TUM2TWIN":[126],"Gold":[128],"Coast":[129],"datasets":[130],"show":[131],"effectively":[134],"into":[139],"representations,":[142,180],"outperforming":[143],"existing":[144],"2D-driven":[145],"3DGS":[147],"baselines,":[148],"including":[149],"LangSplat":[150],"Gaga,":[152],"by":[153],"up":[154],"15.8":[156],"IoU":[157],"points":[158,166],"in":[159,167],"coarse":[160],"segmentation":[162],"14.2":[164],"mIoU":[165],"segmentation.":[170],"By":[171],"bridging":[172],"photorealistic":[177],"enables":[182],"semantically":[183],"queryable":[184],"structure-aware":[186],"reconstruction.":[188],"Code":[189],"is":[190],"available":[191],"at":[192],"https://github.com/Jinyzzz/GS4City.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}