{"id":"https://openalex.org/W7140101150","doi":"https://doi.org/10.48550/arxiv.2603.20176","title":"LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis","display_name":"LagerNVS: Latent Geometry for Fully Neural Real-time Novel View Synthesis","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7140101150","doi":"https://doi.org/10.48550/arxiv.2603.20176"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20176","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.2603.20176","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087707344","display_name":"Stanis\u0142aw Szymanowicz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Szymanowicz, Stanislaw","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130402607","display_name":"Minghao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Minghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130359099","display_name":"Jianyuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130407163","display_name":"Christian Rupprecht","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rupprecht, Christian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130400196","display_name":"Andrea Vedaldi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vedaldi, Andrea","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":0.41819998621940613,"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":0.41819998621940613,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.2736000120639801,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.07259999960660934,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/view-synthesis","display_name":"View synthesis","score":0.7400000095367432},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6054999828338623},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.550599992275238},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.43230000138282776},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4230000078678131},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.35589998960494995}],"concepts":[{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.7400000095367432},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696000099182129},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6054999828338623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5995000004768372},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.550599992275238},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4230000078678131},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40639999508857727},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.35589998960494995},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3513999879360199},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20176","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.2603.20176","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20176","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":{"Recent":[0],"work":[1],"has":[2],"shown":[3],"that":[4,25,52],"neural":[5,48],"networks":[6],"can":[7,112],"perform":[8],"3D":[9,19,27,64,70],"tasks":[10],"such":[11,37],"as":[12],"Novel":[13,90],"View":[14,91],"Synthesis":[15,92],"(NVS)":[16],"without":[17,100],"explicit":[18,69],"reconstruction.":[20],"Even":[21],"so,":[22],"we":[23],"argue":[24],"strong":[26],"inductive":[28],"biases":[29],"are":[30],"still":[31],"helpful":[32],"in":[33,104],"the":[34],"design":[35],"of":[36],"networks.":[38],"We":[39],"show":[40],"this":[41],"point":[42],"by":[43],"introducing":[44],"LagerNVS,":[45],"an":[46],"encoder-decoder":[47],"network":[49,66],"for":[50,119],"NVS":[51],"builds":[53],"on":[54,96],"`3D-aware'":[55],"latent":[56],"features.":[57],"The":[58],"encoder":[59],"is":[60,73],"initialized":[61],"from":[62],"a":[63,76,116],"reconstruction":[65],"pre-trained":[67],"using":[68],"supervision.":[71],"This":[72],"paired":[74,114],"with":[75,82,98,115],"lightweight":[77],"decoder,":[78],"and":[79,99,111],"trained":[80],"end-to-end":[81],"photometric":[83],"losses.":[84],"LagerNVS":[85],"achieves":[86],"state-of-the-art":[87],"deterministic":[88],"feed-forward":[89],"(including":[93],"31.4":[94],"PSNR":[95],"Re10k),":[97],"known":[101],"cameras,":[102],"renders":[103],"real":[105],"time,":[106],"generalizes":[107],"to":[108],"in-the-wild":[109],"data,":[110],"be":[113],"diffusion":[117],"decoder":[118],"generative":[120],"extrapolation.":[121]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-24T00:00:00"}
