{"id":"https://openalex.org/W7137339890","doi":"https://doi.org/10.48550/arxiv.2603.12903","title":"Spectral-Geometric Neural Fields for Pose-Free LiDAR View Synthesis","display_name":"Spectral-Geometric Neural Fields for Pose-Free LiDAR View Synthesis","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7137339890","doi":"https://doi.org/10.48550/arxiv.2603.12903"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12903","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.2603.12903","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049402483","display_name":"Yinuo Jiang","orcid":"https://orcid.org/0009-0009-5658-3413"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yinuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129528741","display_name":"Jun Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129612457","display_name":"Yiran Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129631856","display_name":"Cheng Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Cheng","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":0.7321000099182129,"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":0.7321000099182129,"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":0.0674000009894371,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.05999999865889549,"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/lidar","display_name":"Lidar","score":0.8381999731063843},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4699999988079071},{"id":"https://openalex.org/keywords/radiance","display_name":"Radiance","score":0.4169999957084656},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4025000035762787},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.36399999260902405},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.34769999980926514},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.33629998564720154}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8381999731063843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7164000272750854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6509000062942505},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6424999833106995},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C23690007","wikidata":"https://www.wikidata.org/wiki/Q1411145","display_name":"Radiance","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4025000035762787},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.37869998812675476},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.36399999260902405},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.34769999980926514},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3346000015735626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C2776449333","wikidata":"https://www.wikidata.org/wiki/Q7928781","display_name":"View synthesis","level":3,"score":0.30550000071525574},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26350000500679016},{"id":"https://openalex.org/C108882727","wikidata":"https://www.wikidata.org/wiki/Q2991685","display_name":"Solid modeling","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12903","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.2603.12903","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12903","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":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6312282681465149}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neural":[0],"Radiance":[1],"Fields":[2],"(NeRF)":[3],"have":[4],"shown":[5],"remarkable":[6],"success":[7],"in":[8,124],"image":[9],"novel":[10,150],"view":[11,154],"synthesis":[12],"(NVS),":[13],"inspiring":[14],"extensions":[15],"to":[16,44,79,95,107,129],"LiDAR":[17,37,59,153],"NVS.":[18],"However,":[19],"most":[20],"methods":[21],"heavily":[22],"rely":[23],"on":[24,76,92],"accurate":[25],"camera":[26],"poses":[27],"for":[28,152],"scene":[29],"reconstruction.":[30],"The":[31],"sparsity":[32],"and":[33,47,137,143],"textureless":[34],"nature":[35],"of":[36,120],"data":[38],"also":[39],"present":[40],"distinct":[41],"challenges,":[42],"leading":[43],"geometric":[45,67],"holes":[46],"discontinuous":[48],"surfaces.":[49],"To":[50],"address":[51],"these":[52],"issues,":[53],"we":[54,70,86],"propose":[55],"SG-NLF,":[56],"a":[57,72,88,149],"pose-free":[58],"NeRF":[60],"framework":[61],"that":[62],"integrates":[63],"spectral":[64,77],"information":[65],"with":[66],"consistency.":[68],"Specifically,":[69],"design":[71],"hybrid":[73],"representation":[74],"based":[75,91],"priors":[78],"reconstruct":[80],"smooth":[81],"geometry.":[82],"For":[83],"pose":[84,138],"optimization,":[85],"construct":[87],"confidence-aware":[89],"graph":[90],"feature":[93],"compatibility":[94],"achieve":[96],"global":[97],"alignment.":[98],"In":[99],"addition,":[100],"an":[101],"adversarial":[102],"learning":[103],"strategy":[104],"is":[105],"introduced":[106],"enforce":[108],"cross-frame":[109],"consistency,":[110],"thereby":[111],"enhancing":[112],"reconstruction":[113,135],"quality.":[114],"Comprehensive":[115],"experiments":[116],"demonstrate":[117],"the":[118],"effectiveness":[119],"our":[121],"framework,":[122],"especially":[123],"challenging":[125],"low-frequency":[126],"scenarios.":[127],"Compared":[128],"previous":[130],"state-of-the-art":[131],"methods,":[132],"SG-NLF":[133],"improves":[134],"quality":[136],"accuracy":[139],"by":[140],"over":[141],"35.8%":[142],"68.8%.":[144],"Our":[145],"work":[146],"can":[147],"provide":[148],"perspective":[151],"synthesis.":[155]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-17T00:00:00"}