{"id":"https://openalex.org/W4416850282","doi":"https://doi.org/10.1109/wacv61042.2026.00322","title":"VOCAL: Visual Odometry via ContrAstive Learning","display_name":"VOCAL: Visual Odometry via ContrAstive Learning","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W4416850282","doi":"https://doi.org/10.1109/wacv61042.2026.00322"},"language":"en","primary_location":{"id":"doi:10.1109/wacv61042.2026.00322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2507.00243","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chi-Yao Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chi-Yao Huang","raw_affiliation_strings":["Arizona State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049496557","display_name":"Zeel Bhatt","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zeel Bhatt","raw_affiliation_strings":["Arizona State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102031937","display_name":"Yezhou Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yezhou Yang","raw_affiliation_strings":["Arizona State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I55732556"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00174716,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3297","last_page":"3309"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.7354000210762024,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.7354000210762024,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.16290000081062317,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.012500000186264515,"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/interpretability","display_name":"Interpretability","score":0.9696000218391418},{"id":"https://openalex.org/keywords/visual-odometry","display_name":"Visual odometry","score":0.8478000164031982},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44190001487731934},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4318999946117401},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43050000071525574},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4083999991416931},{"id":"https://openalex.org/keywords/odometry","display_name":"Odometry","score":0.4050000011920929}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9696000218391418},{"id":"https://openalex.org/C5799516","wikidata":"https://www.wikidata.org/wiki/Q4110915","display_name":"Visual odometry","level":3,"score":0.8478000164031982},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7666000127792358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6460000276565552},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5335999727249146},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44190001487731934},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4083999991416931},{"id":"https://openalex.org/C49441653","wikidata":"https://www.wikidata.org/wiki/Q2014717","display_name":"Odometry","level":4,"score":0.4050000011920929},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.27790001034736633},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/wacv61042.2026.00322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.00243","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.00243","pdf_url":"https://arxiv.org/pdf/2507.00243","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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.2507.00243","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.00243","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:2507.00243","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2507.00243","pdf_url":"https://arxiv.org/pdf/2507.00243","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Breakthroughs":[0],"in":[1,40],"visual":[2,86],"odometry":[3],"(VO)":[4],"have":[5],"fundamentally":[6],"reshaped":[7],"the":[8,108,117,120,134],"landscape":[9],"of":[10,119],"robotics,":[11],"enabling":[12],"ultra-precise":[13],"camera":[14,90,97],"state":[15],"estimation":[16],"that":[17,67],"is":[18],"crucial":[19],"for":[20],"modern":[21],"autonomous":[22],"systems.":[23],"Despite":[24],"these":[25,54],"advances,":[26],"many":[27],"learning-based":[28],"VO":[29,69,144],"techniques":[30],"rely":[31],"on":[32,133],"rigid":[33],"geometric":[34],"assumptions,":[35],"which":[36],"often":[37],"fall":[38],"short":[39],"interpretability":[41,118,140],"and":[42,103,141,148],"lack":[43],"a":[44,64,71,80],"solid":[45],"theoretical":[46],"basis":[47],"within":[48,107],"fully":[49],"data-driven":[50],"frameworks.":[51],"To":[52],"overcome":[53],"limitations,":[55],"we":[56],"introduce":[57],"VOCAL":[58,84],"(Visual":[59],"Odometry":[60],"via":[61],"ContrAstive":[62],"Learning),":[63],"novel":[65],"framework":[66],"reimagines":[68],"as":[70],"label":[72],"ranking":[73,93],"challenge.":[74],"By":[75],"integrating":[76],"Bayesian":[77],"inference":[78],"with":[79,127],"representation":[81],"learning":[82],"framework,":[83],"organizes":[85],"features":[87,122],"to":[88,99],"mirror":[89],"states.":[91],"The":[92],"mechanism":[94],"compels":[95],"similar":[96],"states":[98],"converge":[100],"into":[101],"consistent":[102],"spatially":[104],"coherent":[105],"representations":[106],"latent":[109],"space.":[110],"This":[111],"strategic":[112],"alignment":[113],"not":[114],"only":[115],"bolsters":[116],"learned":[121],"but":[123],"also":[124],"ensures":[125],"compatibility":[126],"multimodal":[128],"data":[129],"sources.":[130],"Extensive":[131],"evaluations":[132],"KITTI":[135],"dataset":[136],"highlight":[137],"VOCAL's":[138],"enhanced":[139],"flexibility,":[142],"pushing":[143],"toward":[145],"more":[146],"general":[147],"explainable":[149],"spatial":[150],"intelligence.":[151]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}