{"id":"https://openalex.org/W4393023410","doi":"https://doi.org/10.48550/arxiv.2403.10860","title":"Efficient Domain Adaptation for Endoscopic Visual Odometry","display_name":"Efficient Domain Adaptation for Endoscopic Visual Odometry","publication_year":2024,"publication_date":"2024-03-16","ids":{"openalex":"https://openalex.org/W4393023410","doi":"https://doi.org/10.48550/arxiv.2403.10860"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2403.10860","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.10860","pdf_url":"https://arxiv.org/pdf/2403.10860","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2403.10860","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009447015","display_name":"Junyang Wu","orcid":"https://orcid.org/0009-0003-6010-0089"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wu, Junyang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061924699","display_name":"Yun Gu","orcid":"https://orcid.org/0000-0002-4199-0675"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Yun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5085036036","display_name":"Guang\u2010Zhong Yang","orcid":"https://orcid.org/0000-0003-4060-4020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Guang-Zhong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009447015"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9909999966621399,"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"}},"topics":[{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9909999966621399,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9822999835014343,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.975600004196167,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/adaptation","display_name":"Adaptation (eye)","score":0.6995938420295715},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6177670955657959},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.5840452909469604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5452970862388611},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5431964993476868},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.5005643367767334},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4999849796295166},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4884470999240875},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.22378015518188477},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.16646361351013184},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1043865978717804},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.05915293097496033},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.057549357414245605}],"concepts":[{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6995938420295715},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6177670955657959},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.5840452909469604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5452970862388611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5431964993476868},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.5005643367767334},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4999849796295166},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4884470999240875},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.22378015518188477},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.16646361351013184},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1043865978717804},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.05915293097496033},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.057549357414245605},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2403.10860","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.10860","pdf_url":"https://arxiv.org/pdf/2403.10860","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2403.10860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2403.10860","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2403.10860","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.10860","pdf_url":"https://arxiv.org/pdf/2403.10860","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4393023410.pdf","grobid_xml":"https://content.openalex.org/works/W4393023410.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2979950214","https://openalex.org/W87609089","https://openalex.org/W3024737167","https://openalex.org/W2414561716","https://openalex.org/W3161199934","https://openalex.org/W2303855011","https://openalex.org/W2312326526","https://openalex.org/W3105866016","https://openalex.org/W2412578866","https://openalex.org/W4312703710"],"abstract_inverted_index":{"Visual":[0],"odometry":[1,43,67,156],"plays":[2],"a":[3,20,27,96,120],"crucial":[4],"role":[5],"in":[6,51,131,154],"endoscopic":[7,65],"imaging,":[8],"yet":[9],"the":[10,32,37,52,72,115,129,160],"scarcity":[11],"of":[12,99],"realistic":[13],"images":[14,101],"with":[15,36,94],"ground":[16],"truth":[17],"poses":[18,19],"significant":[21,167],"challenge.":[22],"Therefore,":[23],"domain":[24,35,40],"adaptation":[25],"offers":[26],"promising":[28],"approach":[29],"to":[30,77,80,108,127],"bridge":[31],"pre-operative":[33,75,105],"planning":[34,76],"intra-operative":[38,170],"real":[39,100],"for":[41,64,169],"learning":[42],"information.":[44],"However,":[45],"existing":[46],"methodologies":[47],"suffer":[48],"from":[49,74],"inefficiencies":[50],"training":[53,92,111,135,162],"time.":[54],"In":[55],"this":[56,88],"work,":[57],"an":[58],"efficient":[59,86],"neural":[60],"style":[61],"transfer":[62],"framework":[63],"visual":[66,155],"is":[68],"proposed,":[69],"which":[70],"compresses":[71],"time":[73],"testing":[78,116,137],"phase":[79],"less":[81],"than":[82],"five":[83],"minutes.":[84],"For":[85],"traing,":[87],"work":[89],"focuses":[90],"on":[91,142],"modules":[93],"only":[95],"limited":[97],"number":[98],"and":[102,136],"we":[103,118],"exploit":[104],"prior":[106],"information":[107],"dramatically":[109],"reduce":[110],"duration.":[112],"Moreover,":[113],"during":[114],"phase,":[117],"propose":[119],"novel":[121],"Test":[122],"Time":[123],"Adaptation":[124],"(TTA)":[125],"method":[126,150],"mitigate":[128],"gap":[130],"lighting":[132],"conditions":[133],"between":[134],"datasets.":[138],"Experimental":[139],"evaluations":[140],"conducted":[141],"two":[143],"public":[144],"endoscope":[145],"datasets":[146],"showcase":[147],"that":[148],"our":[149],"achieves":[151],"state-of-the-art":[152],"accuracy":[153],"tasks":[157],"while":[158],"boasting":[159],"fastest":[161],"speeds.":[163],"These":[164],"results":[165],"demonstrate":[166],"promise":[168],"surgery":[171],"applications.":[172]},"counts_by_year":[],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2024-03-21T00:00:00"}