{"id":"https://openalex.org/W4360818702","doi":"https://doi.org/10.48550/arxiv.2303.11616","title":"HRDFuse: Monocular 360\u00b0Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions","display_name":"HRDFuse: Monocular 360\u00b0Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions","publication_year":2023,"publication_date":"2023-03-21","ids":{"openalex":"https://openalex.org/W4360818702","doi":"https://doi.org/10.48550/arxiv.2303.11616"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2303.11616","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.11616","pdf_url":"https://arxiv.org/pdf/2303.11616","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2303.11616","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103228598","display_name":"Hao Ai","orcid":"https://orcid.org/0000-0003-2104-3352"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ai, Hao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054851062","display_name":"Zidong cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"cao, Zidong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088899744","display_name":"Yanpei Cao","orcid":"https://orcid.org/0000-0002-4042-1171"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Yan-pei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102004349","display_name":"Ying Shan","orcid":"https://orcid.org/0000-0001-7673-8325"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Ying","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100403195","display_name":"Lin Wang","orcid":"https://orcid.org/0000-0002-7485-4493"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103228598"],"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":0.9998999834060669,"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":0.9998999834060669,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9952999949455261,"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":0.6308857202529907},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6300816535949707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.599219024181366},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5575963854789734},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5573540925979614},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5410240292549133},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5223028063774109},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.4865990877151489},{"id":"https://openalex.org/keywords/tangent","display_name":"Tangent","score":0.4606182277202606},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4530486762523651},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45249009132385254},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4273150563240051},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40648001432418823},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.38604170083999634},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3000098466873169},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23483169078826904},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.12955448031425476}],"concepts":[{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6308857202529907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6300816535949707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.599219024181366},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5575963854789734},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5573540925979614},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5410240292549133},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5223028063774109},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.4865990877151489},{"id":"https://openalex.org/C138187205","wikidata":"https://www.wikidata.org/wiki/Q131251","display_name":"Tangent","level":2,"score":0.4606182277202606},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4530486762523651},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45249009132385254},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4273150563240051},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40648001432418823},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38604170083999634},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3000098466873169},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23483169078826904},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.12955448031425476},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2303.11616","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.11616","pdf_url":"https://arxiv.org/pdf/2303.11616","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2303.11616","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2303.11616","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":"pmh:oai:arXiv.org:2303.11616","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2303.11616","pdf_url":"https://arxiv.org/pdf/2303.11616","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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4360818702.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2107628111","https://openalex.org/W2394004323","https://openalex.org/W2398764543","https://openalex.org/W200819717","https://openalex.org/W2032269556","https://openalex.org/W2027335291","https://openalex.org/W3210711677","https://openalex.org/W4200218943","https://openalex.org/W3111845905","https://openalex.org/W3010374521"],"abstract_inverted_index":{"Depth":[0],"estimation":[1],"from":[2,58,110,118,200],"a":[3,8,17,32,46,87,124,145,151,157,186],"monocular":[4],"360\u00b0":[5],"image":[6],"is":[7],"burgeoning":[9],"problem":[10],"owing":[11],"to":[12,30,44,139,204],"its":[13],"holistic":[14],"sensing":[15],"of":[16,62,65,79,96,189],"scene.":[18],"Recently,":[19],"some":[20],"methods,":[21],"\\eg,":[22],"OmniFusion,":[23],"have":[24],"applied":[25],"the":[26,76,94,106,111,114,119,135,141,166,170,178,197,206,229],"tangent":[27],"projection":[28,51],"(TP)":[29],"represent":[31],"360\u00b0image":[33],"and":[34,101,113,137,172,202,219],"predicted":[35,184],"depth":[36,47,77,159,174,180,198,208],"values":[37,181],"via":[38],"patch-wise":[39],"regressions,":[40],"which":[41],"are":[42],"merged":[43],"get":[45],"map":[48,149],"with":[49],"equirectangular":[50],"(ERP)":[52],"format.":[53],"However,":[54],"these":[55],"methods":[56],"suffer":[57],"1)":[59],"non-trivial":[60],"process":[61],"merging":[63],"plenty":[64],"patches;":[66],"2)":[67],"capturing":[68,169],"less":[69],"holistic-with-regional":[70],"contextual":[71,108],"information":[72,109,117],"by":[73,103],"directly":[74],"regressing":[75],"value":[78],"each":[80],"pixel.":[81],"In":[82],"this":[83],"paper,":[84],"we":[85,122,155,194],"propose":[86,123,156],"novel":[88],"framework,":[89],"\\textbf{HRDFuse},":[90],"that":[91,130,164,213],"subtly":[92],"combines":[93],"potential":[95],"convolutional":[97],"neural":[98],"networks":[99],"(CNNs)":[100],"transformers":[102],"collaboratively":[104],"learning":[105],"\\textit{holistic}":[107],"ERP":[112,138,147,171,201],"\\textit{regional}":[115],"structural":[116],"TP.":[120],"Firstly,":[121],"spatial":[125],"feature":[126,132,148],"alignment":[127],"(\\textbf{SFA})":[128],"module":[129,163],"learns":[131,165],"similarities":[133],"between":[134],"TP":[136,142,173,203],"aggregate":[140],"features":[143],"into":[144],"complete":[146],"in":[150],"pixel-wise":[152],"manner.":[153],"Secondly,":[154],"collaborative":[158],"distribution":[160],"classification":[161],"(\\textbf{CDDC})":[162],"\\textbf{holistic-with-regional}":[167],"histograms":[168],"distributions.":[175],"As":[176],"such,":[177],"final":[179,207],"can":[182],"be":[183],"as":[185],"linear":[187],"combination":[188],"histogram":[190],"bin":[191],"centers.":[192],"Lastly,":[193],"adaptively":[195],"combine":[196],"predictions":[199],"obtain":[205],"map.":[209],"Extensive":[210],"experiments":[211],"show":[212],"our":[214],"method":[215],"predicts\\textbf{":[216],"more":[217],"smooth":[218],"accurate":[220],"depth}":[221],"results":[222,227],"while":[223],"achieving":[224],"\\textbf{favorably":[225],"better}":[226],"than":[228],"SOTA":[230],"methods.":[231]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2023-03-25T00:00:00"}
