{"id":"https://openalex.org/W7133909482","doi":"https://doi.org/10.48550/arxiv.2603.04277","title":"VehAnchor: Metadata-Free Metric Scale Recovery from Vehicle Cues in Aerial Imagery","display_name":"VehAnchor: Metadata-Free Metric Scale Recovery from Vehicle Cues in Aerial Imagery","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7133909482","doi":"https://doi.org/10.48550/arxiv.2603.04277"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.04277","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128159930","display_name":"Yifei Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yifei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5142782813","display_name":"Chenqian Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Chenqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123608527","display_name":"Jiayi Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Jiayi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5053943810","display_name":"Xupeng Chen","orcid":"https://orcid.org/0009-0003-3519-1863"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xupeng","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34487017,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9388999938964844,"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":0.9388999938964844,"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":0.01640000008046627,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.006800000090152025,"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/bounding-overwatch","display_name":"Bounding overwatch","score":0.6182000041007996},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5443000197410583},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4925999939441681},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.4862000048160553},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4675999879837036},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.4616999924182892},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.45910000801086426},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.4075999855995178},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.3926999866962433},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.3718999922275543}],"concepts":[{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.6182000041007996},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5414999723434448},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.4862000048160553},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4675999879837036},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.45910000801086426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41690000891685486},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.414000004529953},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.397599995136261},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.3718999922275543},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.35740000009536743},{"id":"https://openalex.org/C196070930","wikidata":"https://www.wikidata.org/wiki/Q598190","display_name":"Correlogram","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.34950000047683716},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C102168758","wikidata":"https://www.wikidata.org/wiki/Q7321258","display_name":"Beacon","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C2781271823","wikidata":"https://www.wikidata.org/wiki/Q138081","display_name":"Waypoint","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.29170000553131104},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.2913999855518341},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.271699994802475},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2572999894618988},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25459998846054077},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.25209999084472656},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.04277","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.04277","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.04277","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.04277","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":{"Autonomous":[0],"aerial":[1],"robots":[2],"operating":[3],"in":[4],"GPS-denied":[5],"or":[6,154],"communication-degraded":[7],"environments":[8],"frequently":[9],"lose":[10],"access":[11],"to":[12,22,45,93,123,146,150,157],"camera":[13],"metadata":[14],"and":[15,121,137,196],"telemetry,":[16],"leaving":[17],"onboard":[18],"perception":[19],"systems":[20],"unable":[21],"recover":[23,94],"the":[24,29,143,152,161,180,202],"absolute":[25],"metric":[26],"scale":[27,64],"of":[28],"scene.":[30],"As":[31],"LLM/VLM-based":[32],"planners":[33],"are":[34],"increasingly":[35],"adopted":[36],"as":[37,83],"high-level":[38],"agents":[39,210],"for":[40,176,217],"embodied":[41],"systems,":[42],"their":[43],"ability":[44],"reason":[46],"about":[47],"physical":[48],"dimensions":[49],"becomes":[50],"safety-critical":[51],"--":[52,190,206],"yet":[53],"our":[54],"experiments":[55],"show":[56],"that":[57,87,208],"five":[58],"state-of-the-art":[59],"VLMs":[60],"suffer":[61],"from":[62,99],"\\emph{spatial":[63],"hallucinations},":[65],"with":[66,173,191,211],"median":[67,167,184],"area":[68,178],"estimation":[69,120],"errors":[70],"exceeding":[71],"50\\%.":[72],"We":[73],"propose":[74],"VehAnchor,":[75],"a":[76,84,126,134,138,187],"lightweight,":[77],"deterministic":[78,212],"\\emph{Geometric":[79],"Perception":[80],"Skill}":[81],"designed":[82],"callable":[85],"tool":[86,131],"any":[88],"LLM-based":[89],"agent":[90,145],"can":[91],"invoke":[92],"Ground":[95],"Sample":[96],"Distance":[97],"(GSD)":[98],"ubiquitous":[100],"environmental":[101],"anchors:":[102],"small":[103],"vehicles":[104],"detected":[105],"via":[106],"oriented":[107],"bounding":[108],"boxes,":[109],"whose":[110],"modal":[111],"pixel":[112],"length":[113],"is":[114,215],"robustly":[115],"estimated":[116],"through":[117],"kernel":[118],"density":[119],"converted":[122],"GSD":[124,135,168],"using":[125],"pre-calibrated":[127],"reference":[128],"length.":[129],"The":[130],"returns":[132],"both":[133],"estimate":[136],"composite":[139],"confidence":[140],"score,":[141],"enabling":[142],"calling":[144],"autonomously":[147],"decide":[148],"whether":[149],"trust":[151],"measurement":[153],"fall":[155],"back":[156],"alternative":[158],"strategies.":[159],"On":[160],"DOTA~v1.5":[162],"benchmark,":[163],"VehAnchor":[164],"achieves":[165],"6.87\\%":[166],"error":[169,185],"on":[170,186],"306~images.":[171],"Integrated":[172],"SAM-based":[174],"segmentation":[175],"downstream":[177],"measurement,":[179],"pipeline":[181],"yields":[182],"19.7\\%":[183],"100-entry":[188],"benchmark":[189],"2.6$\\times$":[192],"lower":[193],"category":[194],"dependence":[195],"4$\\times$":[197],"fewer":[198],"catastrophic":[199],"failures":[200],"than":[201],"best":[203],"VLM":[204],"baseline":[205],"demonstrating":[207],"equipping":[209],"geometric":[213],"tools":[214],"essential":[216],"safe":[218],"autonomous":[219],"spatial":[220],"reasoning.":[221]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-06T00:00:00"}