{"id":"https://openalex.org/W3090975042","doi":"https://doi.org/10.1109/icra40945.2020.9197123","title":"Omnidirectional Depth Extension Networks","display_name":"Omnidirectional Depth Extension Networks","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3090975042","doi":"https://doi.org/10.1109/icra40945.2020.9197123","mag":"3090975042"},"language":"en","primary_location":{"id":"doi:10.1109/icra40945.2020.9197123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9197123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5032552883","display_name":"Xinjing Cheng","orcid":"https://orcid.org/0000-0003-2927-4243"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinjing Cheng","raw_affiliation_strings":["Robotics and Auto-driving Lab (RAL), Baidu Research, Baidu Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics and Auto-driving Lab (RAL), Baidu Research, Baidu Inc","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395977","display_name":"Peng Wang","orcid":"https://orcid.org/0000-0002-1265-0233"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Wang","raw_affiliation_strings":["currently working on Bytedance US AI Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"currently working on Bytedance US AI Lab","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081567725","display_name":"Yanqi Zhou","orcid":"https://orcid.org/0000-0003-2051-7616"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanqi Zhou","raw_affiliation_strings":["Robotics and Auto-driving Lab (RAL), Baidu Research, Baidu Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics and Auto-driving Lab (RAL), Baidu Research, Baidu Inc","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063064276","display_name":"Chenye Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenye Guan","raw_affiliation_strings":["Robotics and Auto-driving Lab (RAL), Baidu Research, Baidu Inc"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics and Auto-driving Lab (RAL), Baidu Research, Baidu Inc","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076524203","display_name":"Ruigang Yang","orcid":"https://orcid.org/0000-0001-5296-6307"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruigang Yang","raw_affiliation_strings":["Kentucky university"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kentucky university","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"589","last_page":"595"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9998999834060669,"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/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/T10638","display_name":"Optical measurement and interference techniques","score":0.9966999888420105,"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/omnidirectional-antenna","display_name":"Omnidirectional antenna","score":0.7733830213546753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7399914264678955},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7103477716445923},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6485998034477234},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6382378935813904},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5305690765380859},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4963229298591614},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44579389691352844}],"concepts":[{"id":"https://openalex.org/C24027999","wikidata":"https://www.wikidata.org/wiki/Q2176348","display_name":"Omnidirectional antenna","level":3,"score":0.7733830213546753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7399914264678955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7103477716445923},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6485998034477234},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6382378935813904},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5305690765380859},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4963229298591614},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44579389691352844},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C21822782","wikidata":"https://www.wikidata.org/wiki/Q131214","display_name":"Antenna (radio)","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra40945.2020.9197123","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9197123","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1665214252","https://openalex.org/W1803059841","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1915250530","https://openalex.org/W1918878870","https://openalex.org/W1987648924","https://openalex.org/W1989787194","https://openalex.org/W2028249942","https://openalex.org/W2056898157","https://openalex.org/W2115579991","https://openalex.org/W2125416623","https://openalex.org/W2160398734","https://openalex.org/W2165114467","https://openalex.org/W2171740948","https://openalex.org/W2194775991","https://openalex.org/W2296228853","https://openalex.org/W2550402137","https://openalex.org/W2557465155","https://openalex.org/W2586114507","https://openalex.org/W2593745480","https://openalex.org/W2594519801","https://openalex.org/W2601564443","https://openalex.org/W2605344185","https://openalex.org/W2738767782","https://openalex.org/W2748043525","https://openalex.org/W2754770188","https://openalex.org/W2782278879","https://openalex.org/W2795404569","https://openalex.org/W2883505290","https://openalex.org/W2885093229","https://openalex.org/W2895250390","https://openalex.org/W2895696451","https://openalex.org/W2950559486","https://openalex.org/W2951234442","https://openalex.org/W2955954081","https://openalex.org/W2959581809","https://openalex.org/W2962716998","https://openalex.org/W2962907394","https://openalex.org/W2963277791","https://openalex.org/W2963446712","https://openalex.org/W2963549785","https://openalex.org/W2963609011","https://openalex.org/W2964339842","https://openalex.org/W2969202876","https://openalex.org/W2980467688","https://openalex.org/W3102536158","https://openalex.org/W6605121731","https://openalex.org/W6637242042","https://openalex.org/W6638667902","https://openalex.org/W6683208986","https://openalex.org/W6685261749","https://openalex.org/W6729541841","https://openalex.org/W6733367512","https://openalex.org/W6744291033","https://openalex.org/W6746156960","https://openalex.org/W6747612483","https://openalex.org/W6749538343","https://openalex.org/W6753230735","https://openalex.org/W6754969897","https://openalex.org/W6763349780","https://openalex.org/W6765472646","https://openalex.org/W6769516661"],"related_works":["https://openalex.org/W2337415362","https://openalex.org/W4312857205","https://openalex.org/W121273120","https://openalex.org/W2740820121","https://openalex.org/W317572212","https://openalex.org/W2002009170","https://openalex.org/W2048790666","https://openalex.org/W2034462085","https://openalex.org/W2141888456","https://openalex.org/W2164918837"],"abstract_inverted_index":{"Omnidirectional":[0],"360\u00b0":[1,24],"camera":[2,56],"proliferates":[3],"rapidly":[4],"for":[5,31],"autonomous":[6],"robots":[7],"since":[8],"it":[9],"significantly":[10,178,200],"enhances":[11],"the":[12,17,32,64,67,75,84,108,125,134,140,144,149,155,165,168,174,180,186,192],"perception":[13,33],"ability":[14],"by":[15],"widening":[16],"field":[18],"of":[19,77,110,127,136,151,188],"view":[20],"(FoV).":[21],"However,":[22],"corresponding":[23],"depth":[25,61,65,91,206],"sensors,":[26],"which":[27,98,147,177],"are":[28,35],"also":[29],"critical":[30],"system,":[34],"still":[36],"difficult":[37],"or":[38],"expensive":[39],"to":[40,74,143,162],"have.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45,87,184],"propose":[46],"a":[47,58,99,115,159],"low-cost":[48],"3D":[49],"sensing":[50],"system":[51],"that":[52,198],"combines":[53],"an":[54,89],"omnidirectional":[55,79,90,141],"with":[57],"calibrated":[59],"projective":[60,145],"camera,":[62],"where":[63],"from":[66],"limited":[68],"FoV":[69],"can":[70],"be":[71],"automatically":[72,157],"extended":[73],"rest":[76],"recorded":[78],"image.":[80],"To":[81],"accurately":[82],"recover":[83],"missing":[85],"depths,":[86],"design":[88],"extension":[92],"convolutional":[93,117],"neural":[94],"network":[95,120],"(ODE-CNN),":[96],"in":[97,139,167,205],"spherical":[100],"feature":[101,111,128,152],"transform":[102],"layer":[103],"(SFTL)":[104],"is":[105,122],"embedded":[106],"at":[107,124],"end":[109,126],"encoding":[112],"layers,":[113],"and":[114,154,196],"deformable":[116],"spatial":[118],"propagation":[119],"(D-CSPN)":[121],"appended":[123],"decoding":[129],"layers.":[130],"The":[131],"former":[132],"re-samples":[133],"neighborhood":[135],"each":[137],"pixel":[138],"coordination":[142],"coordination,":[146],"reduce":[148],"difficulty":[150],"learning,":[153],"later":[156],"finds":[158],"proper":[160],"context":[161],"well":[163],"align":[164],"structures":[166],"estimated":[169],"depths":[170],"via":[171],"CNN":[172],"w.r.t.":[173],"reference":[175],"image,":[176],"improves":[179],"visual":[181],"quality.":[182],"Finally,":[183],"demonstrate":[185],"effectiveness":[187],"proposed":[189],"ODE-CNN":[190,199],"over":[191],"popular":[193],"360D":[194],"dataset,":[195],"show":[197],"outperforms":[201],"(relatively":[202],"33%":[203],"reduction":[204],"error)":[207],"other":[208],"state-of-the-art":[209],"(SoTA)":[210],"methods.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
