{"id":"https://openalex.org/W4413145650","doi":"https://doi.org/10.1109/cvpr52734.2025.01071","title":"Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video","display_name":"Joint Optimization of Neural Radiance Fields and Continuous Camera Motion from a Monocular Video","publication_year":2025,"publication_date":"2025-06-10","ids":{"openalex":"https://openalex.org/W4413145650","doi":"https://doi.org/10.1109/cvpr52734.2025.01071"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr52734.2025.01071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.01071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039196868","display_name":"Hoang Chuong Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hoang Chuong Nguyen","raw_affiliation_strings":["Australian National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Australian National University","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103020372","display_name":"Wei Mao","orcid":"https://orcid.org/0000-0002-8876-8983"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Mao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101540588","display_name":"Jose M. \u00c1lvarez","orcid":"https://orcid.org/0000-0002-7535-6322"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jose M. Alvarez","raw_affiliation_strings":["NVIDIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100379983","display_name":"Miaomiao Liu","orcid":"https://orcid.org/0000-0001-6485-3510"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Miaomiao Liu","raw_affiliation_strings":["Australian National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Australian National University","institution_ids":["https://openalex.org/I118347636"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"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":"11472","last_page":"11481"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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":0.9785000085830688,"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/T10427","display_name":"Visual perception and processing mechanisms","score":0.9700000286102295,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/radiance","display_name":"Radiance","score":0.7257532477378845},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7034056186676025},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6872082948684692},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6854608654975891},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.6719613671302795},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4789881706237793},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4561573266983032},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.38177359104156494},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.1577185094356537},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.13873013854026794},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08012929558753967}],"concepts":[{"id":"https://openalex.org/C23690007","wikidata":"https://www.wikidata.org/wiki/Q1411145","display_name":"Radiance","level":2,"score":0.7257532477378845},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7034056186676025},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6872082948684692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6854608654975891},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.6719613671302795},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4789881706237793},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4561573266983032},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.38177359104156494},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.1577185094356537},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.13873013854026794},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08012929558753967},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr52734.2025.01071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr52734.2025.01071","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2896728493","https://openalex.org/W2392142157","https://openalex.org/W2043512367","https://openalex.org/W4321518006","https://openalex.org/W2331836163","https://openalex.org/W2024462231","https://openalex.org/W2005276308","https://openalex.org/W1994657804","https://openalex.org/W1970182911","https://openalex.org/W2347721387"],"abstract_inverted_index":{"Neural":[0],"Radiance":[1],"Fields":[2],"(NeRF)":[3],"has":[4],"demonstrated":[5],"its":[6],"superior":[7,168],"capability":[8],"to":[9,58,105,153,180],"represent":[10,154],"3D":[11],"geometry":[12,134],"but":[13],"require":[14],"accurately":[15],"precomputed":[16],"camera":[17,29,57,75,94,122,169],"poses":[18,30,95],"during":[19],"training.":[20],"To":[21],"mitigate":[22],"this":[23],"requirement,":[24],"existing":[25],"methods":[26],"jointly":[27],"optimize":[28],"and":[31,81,135,162,171,174],"NeRF":[32,152],"often":[33],"relying":[34],"on":[35,160],"good":[36],"pose":[37,170],"initialisation":[38],"or":[39],"depth":[40,172],"priors.":[41],"However,":[42],"these":[43],"approaches":[44],"struggle":[45],"in":[46],"challenging":[47],"scenarios,":[48],"such":[49,101],"as":[50,53,77],"large":[51],"rotations,":[52],"they":[54],"map":[55],"each":[56,143],"a":[59,65,106,112,127],"world":[60,107],"co-ordinate":[61],"system.":[62],"We":[63],"propose":[64],"novel":[66],"method":[67],"that":[68],"eliminates":[69],"prior":[70],"dependencies":[71],"by":[72,99,137],"modeling":[73],"continuous":[74,121],"motions":[76,84,103,148],"time-dependent":[78,128],"angular":[79],"velocity":[80,91],"velocity.":[82],"Relative":[83],"between":[85],"cameras":[86],"are":[87,124],"learned":[88,125,147],"first":[89],"via":[90],"integration,":[92],"while":[93],"can":[96],"be":[97],"obtained":[98],"aggregating":[100],"relative":[102],"up":[104],"coordinate":[108],"system":[109],"defined":[110],"at":[111,187],"single":[113],"time":[114,144],"step":[115],"within":[116],"the":[117,151,155],"video.":[118],"Specifically,":[119],"accurate":[120],"movements":[123],"through":[126],"NeRF,":[129],"which":[130],"captures":[131],"local":[132],"scene":[133,157],"motion":[136],"training":[138],"from":[139],"neighboring":[140],"frames":[141],"for":[142],"step.":[145],"The":[146],"enable":[149],"fine-tuning":[150],"full":[156],"geometry.":[158],"Experiments":[159],"Co3D":[161],"Scannet":[163],"show":[164],"our":[165],"approach":[166],"achieves":[167],"estimation":[173],"comparable":[175],"novel-view":[176],"synthesis":[177],"performance":[178],"compared":[179],"state-of-the-art":[181],"methods.":[182],"Our":[183],"code":[184],"is":[185],"available":[186],"https:":[188],"//github.com/HoangChuongNguyen/cope-nerf.":[189]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
