{"id":"https://openalex.org/W2978963728","doi":"https://doi.org/10.1109/ijcnn.2019.8851883","title":"Deeper Monocular Depth Prediction via Long and Short Skip Connection","display_name":"Deeper Monocular Depth Prediction via Long and Short Skip Connection","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978963728","doi":"https://doi.org/10.1109/ijcnn.2019.8851883","mag":"2978963728"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5060872432","display_name":"Zhaokai Wang","orcid":"https://orcid.org/0009-0007-4357-7802"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhaokai Wang","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101586078","display_name":"Limin Xiao","orcid":"https://orcid.org/0000-0001-9438-9181"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Limin Xiao","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057303331","display_name":"Rongbin Xu","orcid":"https://orcid.org/0000-0001-7726-8193"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongbin Xu","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027597845","display_name":"Shubin Su","orcid":"https://orcid.org/0000-0003-1395-4578"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shubin Su","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068024080","display_name":"Shupan Li","orcid":"https://orcid.org/0000-0002-5823-2037"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shupan Li","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100581430","display_name":"Song Yao","orcid":"https://orcid.org/0000-0002-9474-7281"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Song","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5060872432"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.54358278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":1.0,"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":1.0,"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/T10638","display_name":"Optical measurement and interference techniques","score":0.9980000257492065,"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":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.8839964270591736},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7738834619522095},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7718555927276611},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7397813200950623},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6344773769378662},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6242215037345886},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6012679934501648},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.5092900991439819},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5055193901062012},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5016522407531738},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.48562875390052795},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43742451071739197},{"id":"https://openalex.org/keywords/connection","display_name":"Connection (principal bundle)","score":0.4331654906272888},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39074236154556274},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22119030356407166},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12725722789764404}],"concepts":[{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.8839964270591736},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7738834619522095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7718555927276611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7397813200950623},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6344773769378662},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6242215037345886},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6012679934501648},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.5092900991439819},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5055193901062012},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5016522407531738},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.48562875390052795},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43742451071739197},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.4331654906272888},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39074236154556274},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22119030356407166},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12725722789764404},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/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/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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851883","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851883","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W87843015","https://openalex.org/W125693051","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1849277567","https://openalex.org/W1905829557","https://openalex.org/W1915250530","https://openalex.org/W1923779427","https://openalex.org/W1992178727","https://openalex.org/W2074254947","https://openalex.org/W2083047701","https://openalex.org/W2116626343","https://openalex.org/W2117539524","https://openalex.org/W2118304946","https://openalex.org/W2124907686","https://openalex.org/W2125416623","https://openalex.org/W2132947399","https://openalex.org/W2158211626","https://openalex.org/W2163605009","https://openalex.org/W2171740948","https://openalex.org/W2194775991","https://openalex.org/W2436453945","https://openalex.org/W2546302380","https://openalex.org/W2606794968","https://openalex.org/W2608018946","https://openalex.org/W2609883120","https://openalex.org/W2949117887","https://openalex.org/W2951234442","https://openalex.org/W2951261569","https://openalex.org/W2963591054","https://openalex.org/W6605121731","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6639204139","https://openalex.org/W6678569853","https://openalex.org/W6683067110","https://openalex.org/W6684191040","https://openalex.org/W6685261749","https://openalex.org/W6736260464"],"related_works":["https://openalex.org/W587735977","https://openalex.org/W1978142926","https://openalex.org/W3157395178","https://openalex.org/W2384475851","https://openalex.org/W2000444236","https://openalex.org/W2353602216","https://openalex.org/W2367078749","https://openalex.org/W2381798600","https://openalex.org/W1910583078","https://openalex.org/W184455116"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3],"fully":[4],"convolutional":[5],"neural":[6],"network":[7],"to":[8,54,62],"tackle":[9],"the":[10,22,56,64,69,90],"mapping":[11],"between":[12],"single":[13],"view":[14],"RGB":[15],"images":[16],"and":[17,88,93],"depth":[18,23,76],"maps.":[19],"To":[20,67],"regress":[21],"maps":[24,58],"from":[25],"monocular":[26,75],"images,":[27],"we":[28,79],"leverage":[29],"deep":[30],"short":[31],"skip":[32,49],"connections":[33,50],"in":[34,51,74,116],"residual":[35],"learning":[36],"for":[37],"extracting":[38],"features":[39],"rather":[40],"than":[41,111],"using":[42],"hand-crafted":[43],"features.":[44],"We":[45],"further":[46],"propose":[47],"long":[48],"up-sampling":[52],"stage":[53],"reuse":[55],"feature":[57],"which":[59],"is":[60],"proved":[61],"enhance":[63],"result":[65],"experimentally.":[66],"show":[68],"impact":[70],"of":[71,85,113],"loss":[72,86],"functions":[73,87],"map":[77],"predictions,":[78],"train":[80],"our":[81],"model":[82,97],"with":[83,103],"kind":[84],"compare":[89],"results":[91,102],"qualitatively":[92],"quantitatively.":[94],"The":[95],"proposed":[96],"outperforms":[98],"all":[99],"current":[100],"state-of-the-art":[101],"less":[104,110],"training":[105,114],"data":[106,120],"as":[107,109],"well":[108],"half":[112],"epochs":[115],"two":[117],"standard":[118],"benchmark":[119],"sets":[121],"without":[122],"any":[123],"post-processing":[124],"procedures":[125],"or":[126],"other":[127],"refinement":[128],"steps.":[129]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
