{"id":"https://openalex.org/W7159689952","doi":"https://doi.org/10.48550/arxiv.2604.27702","title":"RayFormer: Modeling Inter- and Intra-Ray Similarity for NeRF-Based Video Snapshot Compressive Imaging","display_name":"RayFormer: Modeling Inter- and Intra-Ray Similarity for NeRF-Based Video Snapshot Compressive Imaging","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159689952","doi":"https://doi.org/10.48550/arxiv.2604.27702"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27702","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.2604.27702","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062882793","display_name":"Yubo Dong","orcid":"https://orcid.org/0009-0003-4452-5140"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Yubo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103135266","display_name":"Danhua Liu","orcid":"https://orcid.org/0000-0001-6823-6691"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Danhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134960242","display_name":"Anqi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Anqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5022576370","display_name":"Zhenyuan Lin","orcid":"https://orcid.org/0000-0002-7752-9434"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Zhenyuan","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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.7480000257492065,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.7480000257492065,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.05689999833703041,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.02889999933540821,"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/snapshot","display_name":"Snapshot (computer storage)","score":0.7942000031471252},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.5461000204086304},{"id":"https://openalex.org/keywords/iterative-reconstruction","display_name":"Iterative reconstruction","score":0.49239999055862427},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46369999647140503},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3452000021934509},{"id":"https://openalex.org/keywords/3d-reconstruction","display_name":"3D reconstruction","score":0.28529998660087585}],"concepts":[{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.7942000031471252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7211999893188477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6963000297546387},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6751000285148621},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.5461000204086304},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46369999647140503},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3452000021934509},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C2986012078","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling interval","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26570001244544983},{"id":"https://openalex.org/C2781399445","wikidata":"https://www.wikidata.org/wiki/Q309254","display_name":"High-dynamic-range imaging","level":4,"score":0.2581999897956848},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27702","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.2604.27702","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27702","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":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.758037805557251}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Video":[0],"snapshot":[1,14],"compressive":[2],"imaging":[3],"(SCI)":[4],"enables":[5],"the":[6,59,75,87,97,103,107,114,133],"reconstruction":[7,22,43,139],"of":[8,61],"dynamic":[9],"scenes":[10,130],"from":[11,102],"a":[12,52],"single":[13],"measurement.":[15],"Recently,":[16],"NeRF-based":[17],"methods":[18,26],"have":[19],"shown":[20],"promising":[21],"performance.":[23,140],"However,":[24],"such":[25],"typically":[27],"adopt":[28],"random":[29],"ray":[30,54],"sampling":[31,55,105],"strategies":[32],"and":[33,69,90,121,128],"fail":[34],"to":[35,57,73,117],"capture":[36,74],"content":[37,62],"structural":[38,76],"similarities,":[39,77],"resulting":[40],"in":[41,125],"limited":[42],"quality.":[44],"To":[45],"address":[46],"these":[47],"issues,":[48],"we":[49,65],"first":[50],"propose":[51,66],"patch-level":[53,104],"strategy":[56],"enable":[58],"modeling":[60,78],"structure.":[63],"Then,":[64],"an":[67],"Inter-":[68],"Intra-Ray":[70],"Transformer":[71],"(RayFormer)":[72],"both":[79,126],"inter-ray":[80],"similarities":[81],"among":[82],"spatially":[83],"neighboring":[84],"points":[85,95],"at":[86],"same":[88],"depth":[89],"intra-ray":[91],"correlations":[92],"between":[93],"adjacent":[94],"along":[96],"viewing":[98],"ray.":[99],"Finally,":[100],"benefiting":[101],"strategy,":[106],"total":[108],"variation":[109],"prior":[110],"is":[111],"incorporated":[112],"into":[113],"objective":[115],"function":[116],"enhance":[118],"spatial":[119],"smoothness":[120],"suppress":[122],"artifacts.":[123],"Experiments":[124],"simulated":[127],"real-world":[129],"demonstrate":[131],"that":[132],"proposed":[134],"method":[135],"achieves":[136],"state-of-the-art":[137],"(SOTA)":[138]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-02T00:00:00"}
