{"id":"https://openalex.org/W7160267765","doi":"https://doi.org/10.48550/arxiv.2605.02667","title":"AnchorD: Metric Grounding of Monocular Depth Using Factor Graphs","display_name":"AnchorD: Metric Grounding of Monocular Depth Using Factor Graphs","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160267765","doi":"https://doi.org/10.48550/arxiv.2605.02667"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02667","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02667","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.02667","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100494000","display_name":"Simon Dorer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dorer, Simon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002929602","display_name":"Martin B\u00fcchner","orcid":"https://orcid.org/0000-0001-8725-1213"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"B\u00fcchner, Martin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036573958","display_name":"Nick Heppert","orcid":"https://orcid.org/0000-0002-4347-2644"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heppert, Nick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135388321","display_name":"Abhinav Valada","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valada, Abhinav","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":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/T10653","display_name":"Robot Manipulation and Learning","score":0.6478999853134155,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10653","display_name":"Robot Manipulation and Learning","score":0.6478999853134155,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.14390000700950623,"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":0.10610000044107437,"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/monocular","display_name":"Monocular","score":0.718999981880188},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6685000061988831},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6215999722480774},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5274999737739563},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4514999985694885},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4374000132083893},{"id":"https://openalex.org/keywords/reflection","display_name":"Reflection (computer programming)","score":0.42500001192092896},{"id":"https://openalex.org/keywords/ground-penetrating-radar","display_name":"Ground-penetrating radar","score":0.37290000915527344},{"id":"https://openalex.org/keywords/ground","display_name":"Ground","score":0.3693000078201294}],"concepts":[{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.718999981880188},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6685000061988831},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6215999722480774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885000228881836},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5274999737739563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5062999725341797},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4374000132083893},{"id":"https://openalex.org/C65682993","wikidata":"https://www.wikidata.org/wiki/Q1056451","display_name":"Reflection (computer programming)","level":2,"score":0.42500001192092896},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42160001397132874},{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.37290000915527344},{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3528999984264374},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C52672216","wikidata":"https://www.wikidata.org/wiki/Q1749840","display_name":"Depth perception","level":3,"score":0.3411000072956085},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3319999873638153},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C159246509","wikidata":"https://www.wikidata.org/wiki/Q5428725","display_name":"Factor graph","level":3,"score":0.32339999079704285},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.3167000114917755},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.3021000027656555},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.2915000021457672},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02667","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02667","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.02667","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02667","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":"Preprint"},"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":{"Dense":[0],"and":[1,11,25,110,144,163],"accurate":[2],"depth":[3,16,35,68,74,79,85,104,129,169],"estimation":[4,36,75],"is":[5,138],"essential":[6],"for":[7],"robotic":[8],"manipulation,":[9],"grasping,":[10],"navigation,":[12],"yet":[13],"currently":[14],"available":[15,179],"sensors":[17,162],"are":[18],"prone":[19],"to":[20],"errors":[21],"on":[22,150],"transparent,":[23],"specular,":[24],"general":[26],"non-Lambertian":[27,134],"surfaces.":[28],"To":[29,112],"mitigate":[30],"these":[31],"errors,":[32],"large-scale":[33],"monocular":[34,73,99],"approaches":[37],"provide":[38],"strong":[39],"structural":[40],"priors,":[41],"but":[42],"their":[43,55],"predictions":[44,100],"can":[45],"be":[46],"potentially":[47],"skewed":[48],"or":[49],"mis-scaled":[50],"in":[51,58,61,82,101,115,130,155,168],"metric":[52,102],"units,":[53],"limiting":[54],"direct":[56],"use":[57],"robotics.":[59],"Thus,":[60],"this":[62],"work,":[63],"we":[64,119],"propose":[65],"a":[66,78,93,121],"training-free":[67],"grounding":[69,98],"framework":[70],"that":[71],"anchors":[72],"priors":[76],"from":[77],"foundation":[80],"model":[81],"raw":[83],"sensor":[84],"through":[86],"factor":[87],"graph":[88],"optimization.":[89],"Our":[90],"method":[91],"performs":[92],"patch-wise":[94],"affine":[95],"alignment,":[96],"locally":[97],"real-world":[103,117],"while":[105],"preserving":[106],"fine-grained":[107],"geometric":[108],"structure":[109],"discontinuities.":[111],"facilitate":[113],"evaluation":[114],"challenging":[116],"conditions,":[118],"introduce":[120],"benchmark":[122],"dataset":[123],"with":[124],"dense":[125],"scene-wide":[126],"ground":[127],"truth":[128,137],"the":[131,148],"presence":[132],"of":[133],"objects.":[135],"Ground":[136],"obtained":[139],"via":[140],"matte":[141],"reflection":[142],"spray":[143],"multi-camera":[145],"fusion,":[146],"overcoming":[147],"reliance":[149],"object-only":[151],"CAD-based":[152],"annotations":[153],"used":[154],"prior":[156],"datasets.":[157],"Extensive":[158],"evaluations":[159],"across":[160],"diverse":[161],"domains":[164],"demonstrate":[165],"consistent":[166],"improvements":[167],"performance":[170],"without":[171],"any":[172],"(re-)training.":[173],"We":[174],"make":[175],"our":[176],"implementation":[177],"publicly":[178],"at":[180],"https://anchord.cs.uni-freiburg.de.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}
