{"id":"https://openalex.org/W4224288444","doi":"https://doi.org/10.1109/tip.2022.3167307","title":"Self-Supervised Monocular Depth Estimation With Multiscale Perception","display_name":"Self-Supervised Monocular Depth Estimation With Multiscale Perception","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4224288444","doi":"https://doi.org/10.1109/tip.2022.3167307","pmid":"https://pubmed.ncbi.nlm.nih.gov/35439134"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2022.3167307","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2022.3167307","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yourun Zhang","orcid":"https://orcid.org/0000-0003-1086-9036"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yourun Zhang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0003-1086-9036","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Maoguo Gong","orcid":"https://orcid.org/0000-0002-0415-8556"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maoguo Gong","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-0415-8556","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jianzhao Li","orcid":"https://orcid.org/0000-0002-1524-1363"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzhao Li","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-1524-1363","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mingyang Zhang","orcid":"https://orcid.org/0000-0002-9768-516X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyang Zhang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-9768-516X","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fenlong Jiang","orcid":"https://orcid.org/0000-0002-3714-0600"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fenlong Jiang","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-3714-0600","affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hongyu Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyu Zhao","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":5.3834,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":{"value":0.96946086,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"31","issue":null,"first_page":"3251","last_page":"3266"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9453999996185303,"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":0.9453999996185303,"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":0.006300000008195639,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.005100000184029341,"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/pixel","display_name":"Pixel","score":0.7914000153541565},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.6592000126838684},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.539900004863739},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.4447999894618988},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4438000023365021},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.43160000443458557},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42730000615119934},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4251999855041504}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7990999817848206},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7914000153541565},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6876000165939331},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.6592000126838684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.578499972820282},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.539900004863739},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.4447999894618988},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4438000023365021},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42730000615119934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.40059998631477356},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3628999888896942},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32330000400543213},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2856999933719635},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.28360000252723694},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C142616399","wikidata":"https://www.wikidata.org/wiki/Q5148604","display_name":"Color image","level":4,"score":0.2689000070095062},{"id":"https://openalex.org/C52672216","wikidata":"https://www.wikidata.org/wiki/Q1749840","display_name":"Depth perception","level":3,"score":0.258899986743927},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2022.3167307","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2022.3167307","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:35439134","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35439134","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1949608614","display_name":null,"funder_award_id":"62036006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1965026428","display_name":null,"funder_award_id":"61906147","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1803059841","https://openalex.org/W1901129140","https://openalex.org/W1905829557","https://openalex.org/W2074254947","https://openalex.org/W2083047701","https://openalex.org/W2101648351","https://openalex.org/W2115579991","https://openalex.org/W2117539524","https://openalex.org/W2124907686","https://openalex.org/W2125188192","https://openalex.org/W2132947399","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2300779272","https://openalex.org/W2471962767","https://openalex.org/W2520707372","https://openalex.org/W2562637781","https://openalex.org/W2593414960","https://openalex.org/W2605938684","https://openalex.org/W2609883120","https://openalex.org/W2796048785","https://openalex.org/W2803168974","https://openalex.org/W2887848798","https://openalex.org/W2890949887","https://openalex.org/W2895314356","https://openalex.org/W2896047481","https://openalex.org/W2963412495","https://openalex.org/W2963488291","https://openalex.org/W2963549785","https://openalex.org/W2963591054","https://openalex.org/W2963652981","https://openalex.org/W2963654727","https://openalex.org/W2963906250","https://openalex.org/W2964020152","https://openalex.org/W2964968086","https://openalex.org/W2982478332","https://openalex.org/W2985775862","https://openalex.org/W2990394353","https://openalex.org/W3009257710","https://openalex.org/W3034364596","https://openalex.org/W3034604951","https://openalex.org/W3118453581","https://openalex.org/W3152863269","https://openalex.org/W3153847062","https://openalex.org/W3157340408","https://openalex.org/W3173409262","https://openalex.org/W3191699310","https://openalex.org/W6631190155","https://openalex.org/W6685261749","https://openalex.org/W6685562342","https://openalex.org/W6766261854","https://openalex.org/W6767088534"],"related_works":[],"abstract_inverted_index":{"Extracting":[0],"3D":[1],"information":[2],"from":[3,62],"a":[4,44,57,81,96,112,158,207],"single":[5,117],"optical":[6],"image":[7,36,49,59,69,98,170,190],"is":[8],"very":[9],"attractive.":[10],"Recently":[11],"emerging":[12],"self-supervised":[13],"methods":[14,41,105],"can":[15],"learn":[16],"depth":[17,23,31,76,150,166,181],"representations":[18],"without":[19],"using":[20],"ground":[21],"truth":[22],"maps":[24],"as":[25],"training":[26],"data":[27],"by":[28],"transforming":[29],"the":[30,67,75,85,92,108,128,135,145,149,153,164,180,188,193,196,200,215,219],"prediction":[32],"task":[33],"into":[34,134],"an":[35],"synthesis":[37,171],"task.":[38],"However,":[39],"existing":[40,104],"rely":[42],"on":[43,238],"differentiable":[45],"bilinear":[46],"sampler":[47],"for":[48,138,199],"synthesis,":[50],"which":[51,126,175],"results":[52,230],"in":[53,56,66,74,84,179,187,223],"each":[54,72,116,177],"pixel":[55,73,118,178],"synthetic":[58,97],"being":[60],"derived":[61],"only":[63,80,106],"four":[64],"pixels":[65,83,186],"source":[68,86,154,189],"and":[70,99,119,168,191,241],"causes":[71,127],"map":[77,151,167,182],"to":[78,130,132,143,183,213,217],"perceive":[79,184],"few":[82],"image.":[87],"In":[88,141],"addition,":[89],"when":[90],"calculating":[91],"photometric":[93,109,203],"error":[94,110],"between":[95,123,221],"its":[100],"corresponding":[101],"target":[102],"image,":[103,155],"consider":[107],"within":[111],"small":[113,139],"neighborhood":[114],"of":[115,148,195,202,226],"therefore":[120],"ignore":[121],"correlations":[122],"larger":[124],"areas,":[125],"model":[129,216],"tend":[131],"fall":[133],"local":[136],"optima":[137],"patches.":[140],"order":[142],"extend":[144],"perceptual":[146],"area":[147],"over":[152],"we":[156,205],"propose":[157,206],"novel":[159],"multi-scale":[160],"method":[161,234],"that":[162,232],"downsamples":[163],"predicted":[165],"performs":[169],"at":[172],"different":[173,227],"resolutions,":[174],"enables":[176],"more":[185],"improves":[192],"performance":[194,237],"model.":[197],"As":[198],"locality":[201],"error,":[204],"structural":[208],"similarity":[209],"(SSIM)":[210],"pyramid":[211],"loss":[212],"allow":[214],"sense":[218],"difference":[220],"images":[222],"multiple":[224],"areas":[225],"sizes.":[228],"Experimental":[229],"show":[231],"our":[233],"achieves":[235],"superior":[236],"both":[239],"outdoor":[240],"indoor":[242],"benchmarks.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2022-04-26T00:00:00"}
