{"id":"https://openalex.org/W4283260605","doi":"https://doi.org/10.1145/3529836.3529907","title":"Self-supervised Depth Estimation with High Resolution Features and Non-local Information","display_name":"Self-supervised Depth Estimation with High Resolution Features and Non-local Information","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4283260605","doi":"https://doi.org/10.1145/3529836.3529907"},"language":"en","primary_location":{"id":"doi:10.1145/3529836.3529907","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000662590","display_name":"Rongying Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Rongying Jing","raw_affiliation_strings":["School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100654081","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-9814-638X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, China"],"affiliations":[{"raw_affiliation_string":"School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5000662590"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0478764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"245","last_page":"250"},"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":0.9988999962806702,"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":0.9958999752998352,"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/computer-science","display_name":"Computer science","score":0.7856912612915039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7382708787918091},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.616289496421814},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.592785120010376},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5499942302703857},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5378244519233704},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5299590229988098},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.516163170337677},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.4779134690761566},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4723200500011444},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46595248579978943},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4625588655471802},{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.42741912603378296},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4179564118385315},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2692995071411133},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2650730311870575},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12622612714767456},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.09866899251937866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7856912612915039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7382708787918091},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.616289496421814},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.592785120010376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5499942302703857},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5378244519233704},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5299590229988098},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.516163170337677},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.4779134690761566},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4723200500011444},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46595248579978943},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4625588655471802},{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.42741912603378296},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4179564118385315},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2692995071411133},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2650730311870575},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12622612714767456},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.09866899251937866},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529836.3529907","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3529836.3529907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2083047701","https://openalex.org/W2117228865","https://openalex.org/W2150066425","https://openalex.org/W2151103935","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2520707372","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2609883120","https://openalex.org/W2916798096","https://openalex.org/W2934279571","https://openalex.org/W2963091558","https://openalex.org/W2963412495","https://openalex.org/W2963654727","https://openalex.org/W2963906250","https://openalex.org/W2964014680","https://openalex.org/W2985775862","https://openalex.org/W3034428934","https://openalex.org/W3060975791"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W4249847449","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W44395729","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W2561132942","https://openalex.org/W2765338038","https://openalex.org/W4328027016"],"abstract_inverted_index":{"Depth":[0],"estimation":[1,24],"is":[2,68],"one":[3],"of":[4,19,116],"the":[5,47,54,124,136,146,150,165],"most":[6,174],"challenging":[7],"tasks":[8],"in":[9,13,141],"computer":[10],"vision,":[11],"especially":[12],"self-supervised":[14],"learning":[15],"ways":[16],"without":[17],"restrictions":[18],"high-cost":[20],"labels.":[21],"Self-supervised":[22],"depth":[23,56,86,181],"aims":[25],"to":[26,78,96,109,154],"infer":[27],"three-dimensional":[28],"space":[29],"structures":[30],"from":[31,58],"two-dimensional":[32],"planar":[33],"images,":[34],"only":[35],"taking":[36],"image":[37],"pairs":[38],"or":[39],"sequences":[40],"as":[41],"supervision.":[42],"Most":[43],"existing":[44,175],"methods":[45,176],"adopt":[46],"encoder-decoder":[48],"framework":[49],"with":[50,149],"skip-connection":[51],"and":[52,61,104,129,177],"recover":[53],"high-resolution":[55,59,71,125,131],"maps":[57,100],"low-level":[60],"low-resolution":[62],"high-level":[63,72,98,132],"feature":[64,73,99,126],"maps.":[65,182],"However,":[66],"it":[67],"proved":[69],"that":[70,170],"maps,":[74],"which":[75],"are":[76,83,120],"sensitive":[77],"illumination,":[79],"color,":[80],"texture,":[81],"etc.,":[82],"necessary":[84],"for":[85],"estimation.":[87],"In":[88],"this":[89],"paper,":[90],"we":[91,122,144],"present":[92],"a":[93,106],"novel":[94],"approach":[95],"extract":[97,130],"at":[101,158],"all":[102],"scales":[103],"introduce":[105],"self-attention":[107,147],"mechanism":[108],"consider":[110],"non-local":[111],"features.":[112,160],"The":[113,161],"main":[114],"improvements":[115],"our":[117,171],"proposed":[118],"method":[119],"two-fold:1)":[121],"combined":[123],"extraction":[127],"sub-network":[128],"features":[133,151],"by":[134],"connecting":[135],"high-to-low":[137],"resolution":[138],"convolution":[139],"streams":[140],"parallel;":[142],"2)":[143],"embed":[145],"module":[148],"pyramid":[152],"module(FPA)":[153],"obtain":[155],"general":[156],"context":[157],"large-scale":[159],"experiments":[162],"evaluated":[163],"on":[164],"KITTI":[166],"benchmark":[167],"have":[168],"demonstrated":[169],"network":[172],"outperforms":[173],"produces":[178],"more":[179],"accurate":[180]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}