{"id":"https://openalex.org/W7147287556","doi":"https://doi.org/10.48550/arxiv.2603.27105","title":"UniDAC: Universal Metric Depth Estimation for Any Camera","display_name":"UniDAC: Universal Metric Depth Estimation for Any Camera","publication_year":2026,"publication_date":"2026-03-28","ids":{"openalex":"https://openalex.org/W7147287556","doi":"https://doi.org/10.48550/arxiv.2603.27105"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.27105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27105","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.2603.27105","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113315291","display_name":"Girish Chandar Ganesan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ganesan, Girish Chandar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132698203","display_name":"Yuliang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yuliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132730734","display_name":"Liu Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132578799","display_name":"Xiaoming Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaoming","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/T10531","display_name":"Advanced Vision and Imaging","score":0.9251000285148621,"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":0.9251000285148621,"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":0.027799999341368675,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.016699999570846558,"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/image-warping","display_name":"Image warping","score":0.649399995803833},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5748000144958496},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.53329998254776},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.47780001163482666},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4383000135421753},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4359000027179718}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6583999991416931},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.649399995803833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6158999800682068},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6025000214576721},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5748000144958496},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.53329998254776},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.47780001163482666},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4359000027179718},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.43070000410079956},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.39430001378059387},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.3677000105381012},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2912999987602234},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.27489998936653137}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.27105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27105","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.2603.27105","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.27105","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Monocular":[0],"metric":[1,106],"depth":[2,107,111,144],"estimation":[3,108],"(MMDE)":[4],"is":[5],"a":[6,13,98,126,134,158],"core":[7],"challenge":[8],"in":[9,16,32,89,167,181],"computer":[10],"vision,":[11],"playing":[12],"pivotal":[14],"role":[15],"real-world":[17],"applications":[18],"that":[19,85,132,162],"demand":[20],"accurate":[21],"spatial":[22,165],"understanding.":[23],"Although":[24],"prior":[25,187],"works":[26],"have":[27,51],"shown":[28],"promising":[29],"zero-shot":[30],"performance":[31,120],"MMDE,":[33],"they":[34,63],"often":[35],"struggle":[36],"with":[37],"generalization":[38,183],"across":[39,94,121,189],"diverse":[40,95],"camera":[41,56,68],"types,":[42],"such":[43],"as":[44,146],"fisheye":[45],"and":[46,92,113],"$360^\\circ$":[47],"cameras.":[48],"Recent":[49],"advances":[50],"addressed":[52],"this":[53,103],"through":[54],"unified":[55],"representations":[57],"or":[58,72],"canonical":[59],"representation":[60],"spaces,":[61],"but":[62],"require":[64],"either":[65],"including":[66],"large-FoV":[67],"data":[69],"during":[70],"training":[71],"separately":[73],"trained":[74],"models":[75],"for":[76,150],"different":[77,122],"domains.":[78,123],"We":[79,101,124],"propose":[80,125],"UniDAC,":[81],"an":[82],"MMDE":[83],"framework":[84],"presents":[86],"universal":[87],"robustness":[88],"all":[90,190],"domains":[91],"generalizes":[93],"cameras":[96],"using":[97,141],"single":[99],"model.":[100],"achieve":[102],"by":[104,184],"decoupling":[105],"into":[109],"relative":[110,143],"prediction":[112],"spatially":[114],"varying":[115],"scale":[116,136,152],"estimation,":[117],"enabling":[118],"robust":[119],"lightweight":[127],"Depth-Guided":[128],"Scale":[129],"Estimation":[130],"module":[131],"upsamples":[133],"coarse":[135],"map":[137,145],"to":[138,148],"high":[139],"resolution":[140],"the":[142,164,178],"guidance":[147],"account":[149],"local":[151],"variations.":[153],"Furthermore,":[154],"we":[155],"introduce":[156],"RoPE-$\u03d5$,":[157],"distortion-aware":[159],"positional":[160],"embedding":[161],"respects":[163],"warping":[166],"Equi-Rectangular":[168],"Projections":[169],"(ERP)":[170],"via":[171],"latitude-aware":[172],"weighting.":[173],"UniDAC":[174],"achieves":[175],"state":[176],"of":[177],"art":[179],"(SoTA)":[180],"cross-camera":[182],"consistently":[185],"outperforming":[186],"methods":[188],"datasets.":[191]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
