{"id":"https://openalex.org/W7138855160","doi":"https://doi.org/10.48550/arxiv.2603.16816","title":"WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation","display_name":"WildDepth: A Multimodal Dataset for 3D Wildlife Perception and Depth Estimation","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7138855160","doi":"https://doi.org/10.48550/arxiv.2603.16816"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.16816","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16816","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.2603.16816","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130028710","display_name":"Muhammad Aamir","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Aamir, Muhammad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075852261","display_name":"Naoya Muramatsu","orcid":"https://orcid.org/0000-0002-8976-2292"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muramatsu, Naoya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130166956","display_name":"Sangyun Shin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shin, Sangyun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032162712","display_name":"Matthew Wijers","orcid":"https://orcid.org/0000-0002-5097-8758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wijers, Matthew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhong, Jia-Xing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Jia-Xing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079703326","display_name":"Xinyu Hou","orcid":"https://orcid.org/0000-0001-8988-2422"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Xinyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130105412","display_name":"Amir Patel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patel, Amir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Loveridge, Andrew","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Loveridge, Andrew","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5060183988","display_name":"Andrew Markham","orcid":"https://orcid.org/0000-0001-5716-3941"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Markham, Andrew","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5130028710"],"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":0.3237000107765198,"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":0.3237000107765198,"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":0.24089999496936798,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10989999771118164,"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/suite","display_name":"Suite","score":0.5843999981880188},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5562000274658203},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5097000002861023},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4668999910354614},{"id":"https://openalex.org/keywords/3d-reconstruction","display_name":"3D reconstruction","score":0.4404999911785126},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.42489999532699585},{"id":"https://openalex.org/keywords/photogrammetry","display_name":"Photogrammetry","score":0.3953000009059906},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.37700000405311584},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3490000069141388}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7020999789237976},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6789000034332275},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.5843999981880188},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5562000274658203},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5097000002861023},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4668999910354614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4602999985218048},{"id":"https://openalex.org/C109950114","wikidata":"https://www.wikidata.org/wiki/Q4464732","display_name":"3D reconstruction","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43050000071525574},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C117455697","wikidata":"https://www.wikidata.org/wiki/Q190149","display_name":"Photogrammetry","level":2,"score":0.3953000009059906},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.37700000405311584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2703999876976013},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C197947376","wikidata":"https://www.wikidata.org/wiki/Q5155608","display_name":"Comparability","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.16816","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16816","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.2603.16816","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.16816","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":[{"display_name":"Life in Land","score":0.4953424036502838,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Depth":[0],"estimation":[1],"and":[2,42,79,87,104,138],"3D":[3,88,127],"reconstruction":[4,89,128],"have":[5],"been":[6],"extensively":[7],"studied":[8],"as":[9,25,40],"core":[10],"topics":[11],"in":[12,48,132],"computer":[13],"vision.":[14],"Starting":[15],"from":[16,90,96],"rigid":[17],"objects":[18],"with":[19,101],"relatively":[20],"simple":[21],"geometric":[22],"shapes,":[23],"such":[24,39],"vehicles,":[26],"the":[27,46,50,110],"research":[28],"has":[29],"expanded":[30],"to":[31,98,120,143],"address":[32,70],"general":[33],"objects,":[34,38],"including":[35],"challenging":[36],"deformable":[37],"humans":[41],"animals.":[43],"However,":[44],"for":[45,82],"animal,":[47],"particular,":[49],"majority":[51],"of":[52,93,112],"existing":[53],"models":[54],"are":[55],"trained":[56],"based":[57],"on":[58],"datasets":[59],"without":[60],"metric":[61],"scale,":[62],"which":[63],"can":[64],"help":[65],"validate":[66],"image-only":[67],"models.":[68],"To":[69],"this":[71],"limitation,":[72],"we":[73,141],"present":[74],"WildDepth,":[75],"a":[76],"multimodal":[77,146],"dataset":[78],"benchmark":[80],"suite":[81],"depth":[83,116],"estimation,":[84],"behavior":[85],"detection,":[86],"diverse":[91],"categories":[92],"animals":[94],"ranging":[95],"domestic":[97],"wild":[99],"environments":[100],"synchronized":[102],"RGB":[103],"LiDAR.":[105],"Experimental":[106],"results":[107],"show":[108],"that":[109,149],"use":[111],"multi-modal":[113],"data":[114],"improves":[115],"reliability":[117],"by":[118,130],"up":[119],"10%":[121],"RMSE,":[122],"while":[123],"RGB-LiDAR":[124],"fusion":[125],"enhances":[126],"fidelity":[129],"12%":[131],"Chamfer":[133],"distance.":[134],"By":[135],"releasing":[136],"WildDepth":[137],"its":[139],"benchmarks,":[140],"aim":[142],"foster":[144],"robust":[145],"perception":[147],"systems":[148],"generalize":[150],"across":[151],"domains.":[152]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2026-03-20T00:00:00"}