{"id":"https://openalex.org/W7154180733","doi":"https://doi.org/10.48550/arxiv.2604.08718","title":"Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring","display_name":"Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7154180733","doi":"https://doi.org/10.48550/arxiv.2604.08718"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08718","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08718","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.08718","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025915194","display_name":"Xinmiao Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Xinmiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133490405","display_name":"Bangya Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Bangya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133475598","display_name":"Hao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133551481","display_name":"Dayou Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Dayou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133486958","display_name":"Nuo Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Nuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066604966","display_name":"Andrew Feng","orcid":"https://orcid.org/0000-0002-1675-745X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Andrew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022382771","display_name":"Mingyu Ding","orcid":"https://orcid.org/0000-0001-6556-8359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Mingyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133501464","display_name":"Suman Banerjee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Banerjee, Suman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133485189","display_name":"Yang Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133524667","display_name":"Zhiwen Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Zhiwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9793999791145325,"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":0.9793999791145325,"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":0.007000000216066837,"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":0.002400000113993883,"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/monocular","display_name":"Monocular","score":0.5641999840736389},{"id":"https://openalex.org/keywords/simultaneous-localization-and-mapping","display_name":"Simultaneous localization and mapping","score":0.4964999854564667},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48399999737739563},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.48159998655319214},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.47909998893737793},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.45579999685287476},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.42179998755455017}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7110000252723694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6836000084877014},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6646999716758728},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.5641999840736389},{"id":"https://openalex.org/C86369673","wikidata":"https://www.wikidata.org/wiki/Q1203659","display_name":"Simultaneous localization and mapping","level":4,"score":0.4964999854564667},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48399999737739563},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.48159998655319214},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.47909998893737793},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.35030001401901245},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2653000056743622},{"id":"https://openalex.org/C2986492983","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image matching","level":3,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08718","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08718","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":"doi:10.48550/arxiv.2604.08718","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08718","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Geometric":[0],"Foundation":[1],"Models":[2],"(GFMs)":[3],"have":[4],"recently":[5],"advanced":[6],"monocular":[7],"SLAM":[8,29,114],"by":[9,122],"providing":[10],"robust,":[11],"calibration-free":[12],"3D":[13],"priors.":[14],"However,":[15],"deploying":[16],"these":[17],"models":[18],"on":[19,33,112],"dense":[20,45,142],"video":[21],"streams":[22],"introduces":[23],"significant":[24],"computational":[25],"redundancy.":[26],"Current":[27],"GFM-based":[28],"systems":[30],"typically":[31],"rely":[32],"post":[34],"hoc":[35],"keyframe":[36],"selection.":[37],"Because":[38],"of":[39,108,141],"this,":[40],"they":[41],"must":[42],"perform":[43],"expensive":[44],"geometric":[46,79],"decoding":[47],"simply":[48],"to":[49,82,89],"determine":[50],"whether":[51],"a":[52,71,78,84,99,128],"frame":[53],"contains":[54],"novel":[55],"geometry,":[56],"resulting":[57],"in":[58],"late":[59],"rejection":[60],"and":[61,95,126,138],"wasted":[62],"computation.":[63],"To":[64],"mitigate":[65],"this":[66],"inefficiency,":[67],"we":[68],"propose":[69],"LeanGate,":[70],"lightweight":[72],"feed-forward":[73],"frame-gating":[74],"network.":[75],"LeanGate":[76,118],"predicts":[77],"utility":[80],"score":[81],"assess":[83],"frame's":[85],"mapping":[86,139],"value":[87],"prior":[88],"the":[90,136],"heavy":[91],"GFM":[92],"feature":[93],"extraction":[94],"matching":[96],"stages.":[97],"As":[98],"predictive":[100],"plug-and-play":[101],"module,":[102],"our":[103],"approach":[104],"bypasses":[105],"over":[106],"90%":[107],"redundant":[109],"frames.":[110],"Evaluations":[111],"standard":[113],"benchmarks":[115],"demonstrate":[116],"that":[117],"reduces":[119],"tracking":[120,137],"FLOPs":[121],"more":[123],"than":[124],"85%":[125],"achieves":[127],"5x":[129],"end-to-end":[130],"throughput":[131],"speedup.":[132],"Furthermore,":[133],"it":[134],"maintains":[135],"accuracy":[140],"baselines.":[143],"Project":[144],"page:":[145],"https://lean-gate.github.io/":[146]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-14T00:00:00"}
