{"id":"https://openalex.org/W4225965278","doi":"https://doi.org/10.1109/tits.2022.3155925","title":"Cross-Modal 360\u00b0 Depth Completion and Reconstruction for Large-Scale Indoor Environment","display_name":"Cross-Modal 360\u00b0 Depth Completion and Reconstruction for Large-Scale Indoor Environment","publication_year":2022,"publication_date":"2022-03-14","ids":{"openalex":"https://openalex.org/W4225965278","doi":"https://doi.org/10.1109/tits.2022.3155925"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2022.3155925","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3155925","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-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/A5072389245","display_name":"Ruyu Liu","orcid":"https://orcid.org/0000-0003-2130-9122"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruyu Liu","raw_affiliation_strings":["School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2130-9122","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026381160","display_name":"Guodao Zhang","orcid":"https://orcid.org/0000-0002-6264-5854"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guodao Zhang","raw_affiliation_strings":["College of Computer Science, Zhejiang University of Technology, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6264-5854","affiliations":[{"raw_affiliation_string":"College of Computer Science, Zhejiang University of Technology, Hangzhou, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086344568","display_name":"Jiangming Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangming Wang","raw_affiliation_strings":["Institute of Computer Vision, College of Computer Science and Technology, East China Normal University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computer Vision, College of Computer Science and Technology, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028501979","display_name":"Shuwen Zhao","orcid":"https://orcid.org/0000-0001-8100-799X"},"institutions":[{"id":"https://openalex.org/I63072094","display_name":"University of Portsmouth","ror":"https://ror.org/03ykbk197","country_code":"GB","type":"education","lineage":["https://openalex.org/I63072094"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shuwen Zhao","raw_affiliation_strings":["Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, U.K","institution_ids":["https://openalex.org/I63072094"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.0343,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.92725338,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"23","issue":"12","first_page":"25180","last_page":"25190"},"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/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/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/omnidirectional-camera","display_name":"Omnidirectional camera","score":0.8075532913208008},{"id":"https://openalex.org/keywords/omnidirectional-antenna","display_name":"Omnidirectional antenna","score":0.6790933012962341},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6523707509040833},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.6416497826576233},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5920754671096802},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5666733980178833},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5236570239067078},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5081605911254883},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.4981198310852051},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4454711079597473},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.4284776747226715},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.35590845346450806},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1723894476890564}],"concepts":[{"id":"https://openalex.org/C2777953668","wikidata":"https://www.wikidata.org/wiki/Q684116","display_name":"Omnidirectional camera","level":4,"score":0.8075532913208008},{"id":"https://openalex.org/C24027999","wikidata":"https://www.wikidata.org/wiki/Q2176348","display_name":"Omnidirectional antenna","level":3,"score":0.6790933012962341},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523707509040833},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6416497826576233},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5920754671096802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5666733980178833},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5236570239067078},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5081605911254883},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.4981198310852051},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4454711079597473},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.4284776747226715},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.35590845346450806},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1723894476890564},{"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/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2022.3155925","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3155925","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8399999737739563,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G8797475893","display_name":"\u57fa\u4e8e\u591a\u6a21\u6001\u878d\u5408\u7684\u590d\u6742\u73af\u5883\u6df1\u5ea6\u4f30\u8ba1\u4e0e\u4e09\u7ef4\u91cd\u5efa","funder_award_id":"LQ22F030004","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"}],"funders":[{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W566730006","https://openalex.org/W1612997784","https://openalex.org/W1803059841","https://openalex.org/W1905829557","https://openalex.org/W1989310712","https://openalex.org/W1999889655","https://openalex.org/W2152864241","https://openalex.org/W2202251471","https://openalex.org/W2557465155","https://openalex.org/W2564632156","https://openalex.org/W2586114507","https://openalex.org/W2594519801","https://openalex.org/W2738767782","https://openalex.org/W2796422723","https://openalex.org/W2798665861","https://openalex.org/W2807828983","https://openalex.org/W2809451359","https://openalex.org/W2883505290","https://openalex.org/W2886851716","https://openalex.org/W2895250390","https://openalex.org/W2895640967","https://openalex.org/W2895696451","https://openalex.org/W2899479761","https://openalex.org/W2962815982","https://openalex.org/W2963045776","https://openalex.org/W2963416674","https://openalex.org/W2963591054","https://openalex.org/W2964339842","https://openalex.org/W2967389308","https://openalex.org/W2968555557","https://openalex.org/W2968647281","https://openalex.org/W2972887266","https://openalex.org/W2982574419","https://openalex.org/W3000334335","https://openalex.org/W3034728336","https://openalex.org/W3043971245","https://openalex.org/W3088884252","https://openalex.org/W3090955079","https://openalex.org/W3090975042","https://openalex.org/W3092934936","https://openalex.org/W3100240521","https://openalex.org/W3104255671","https://openalex.org/W3118470688","https://openalex.org/W3126573238","https://openalex.org/W3131795671","https://openalex.org/W3135331138","https://openalex.org/W3166266893","https://openalex.org/W3175201472","https://openalex.org/W3194804214","https://openalex.org/W3209892767","https://openalex.org/W4293566037","https://openalex.org/W4394659040","https://openalex.org/W6733367512","https://openalex.org/W6746156960","https://openalex.org/W6747106673","https://openalex.org/W6755178723","https://openalex.org/W6756363516","https://openalex.org/W6769516661","https://openalex.org/W6773091884","https://openalex.org/W6803312610","https://openalex.org/W6864527971"],"related_works":["https://openalex.org/W2898919627","https://openalex.org/W4379536980","https://openalex.org/W2666300258","https://openalex.org/W2281433634","https://openalex.org/W2118689766","https://openalex.org/W2143648480","https://openalex.org/W1989062809","https://openalex.org/W2625795345","https://openalex.org/W2215517927","https://openalex.org/W2137384304"],"abstract_inverted_index":{"In":[0,131,198],"a":[1,15,136,163,202,230],"large-scale":[2,147,223],"epidemic,":[3],"reducing":[4],"direct":[5],"contact":[6],"among":[7],"medical":[8,54],"personnel,":[9],"attendants":[10],"and":[11,21,25,40,56,60,68,77,83,95,100,118,138,167,185,192,214,226,252],"patients":[12],"has":[13,123],"become":[14],"necessary":[16],"means":[17],"of":[18,52,64,106,116,229,249],"epidemic":[19,58],"prevention":[20,59],"control.":[22],"Intelligent":[23],"vehicles":[24,82,99],"mobile":[26],"robots":[27,85,101],"in":[28,47,86,97,145,161,194,247],"the":[29,49,53,71,110,114,125,128,146,168,188,195,238,242],"hospital":[30,232],"environment,":[31],"such":[32,87],"as":[33],"disinfection":[34],"vehicles,":[35,37],"logistics":[36],"nursing":[38],"robots,":[39,42],"guiding":[41],"play":[43],"an":[44,153],"important":[45,94],"role":[46],"improving":[48],"operational":[50],"efficiency":[51],"system":[55,140,205],"promoting":[57],"governance.":[61],"Powerful":[62],"capabilities":[63],"environmental":[65],"spatial":[66],"perception":[67,90,111],"reconstruction":[69,139,204],"are":[70,173],"keys":[72],"to":[73,127,141,175],"accurate":[74,119],"localization,":[75],"navigation,":[76],"obstacle":[78],"avoidance":[79],"for":[80],"intelligent":[81],"autonomous":[84,98],"operations.":[88],"Omnidirectional":[89],"is":[91],"becoming":[92],"increasingly":[93],"proliferative":[96],"since":[102],"its":[103],"wide":[104],"field":[105],"view":[107],"significantly":[108],"enhances":[109],"ability.":[112],"However,":[113],"lack":[115],"dense":[117,215],"360\u00b0":[120],"depth":[121,155,209,250],"datasets":[122,225],"brought":[124],"challenge":[126,144],"omnidirectional":[129,154,180,212],"perception.":[130],"this":[132,143],"paper,":[133],"we":[134,151,200],"propose":[135],"depth-sensing":[137],"address":[142],"indoor":[148,224],"environment.":[149],"First,":[150],"design":[152],"completion":[156,210,251],"convolutional":[157,166],"neural":[158],"network":[159],"model,":[160],"which":[162],"spherical":[164],"normalized":[165],"unit":[169],"sphere":[170],"area-based":[171],"loss":[172],"introduced":[174],"extract":[176],"features":[177],"from":[178],"cross-modal":[179],"input":[181],"with":[182,187],"unequal":[183],"sparsity":[184],"deal":[186],"imbalanced":[189],"data":[190],"distribution":[191],"distortion":[193],"panoramic":[196],"input.":[197],"addition,":[199],"present":[201],"3D":[203,253],"by":[206],"integrating":[207],"our":[208,219],"into":[211],"localization":[213],"mapping.":[216],"We":[217],"evaluate":[218],"method":[220,240],"on":[221],"360D":[222],"real-world":[227],"sequences":[228],"challenging":[231],"scene.":[233],"Extensive":[234],"experiments":[235],"show":[236],"that":[237],"proposed":[239],"outperforms":[241],"other":[243],"state-of-the-art":[244],"(SoTA)":[245],"approaches":[246],"terms":[248],"reconstruction.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":5}],"updated_date":"2026-07-13T07:31:44.756512","created_date":"2025-10-10T00:00:00"}
