{"id":"https://openalex.org/W2937545488","doi":"https://doi.org/10.1109/fskd.2018.8686952","title":"Depth Completion with Edge Optimization for Depth Maps from Lidar","display_name":"Depth Completion with Edge Optimization for Depth Maps from Lidar","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2937545488","doi":"https://doi.org/10.1109/fskd.2018.8686952","mag":"2937545488"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2018.8686952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2018.8686952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5014334719","display_name":"Chengxiong Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengxiong Jin","raw_affiliation_strings":["School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100438577","display_name":"Xiao Hua Wang","orcid":"https://orcid.org/0000-0002-1774-6189"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaohua Wang","raw_affiliation_strings":["School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059497694","display_name":"Zhonghua Miao","orcid":"https://orcid.org/0000-0001-7203-0901"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhonghua Miao","raw_affiliation_strings":["School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100760805","display_name":"Shiwei Ma","orcid":"https://orcid.org/0000-0001-6039-5030"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiwei Ma","raw_affiliation_strings":["School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China"],"affiliations":[{"raw_affiliation_string":"School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, P. R. China","institution_ids":["https://openalex.org/I113940042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5014334719"],"corresponding_institution_ids":["https://openalex.org/I113940042"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.15319733,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"362","last_page":"367"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9995999932289124,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/depth-map","display_name":"Depth map","score":0.8291540145874023},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7301144003868103},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7201390266418457},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7070354223251343},{"id":"https://openalex.org/keywords/dilation","display_name":"Dilation (metric space)","score":0.6729719638824463},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.6553902626037598},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5693586468696594},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5099908113479614},{"id":"https://openalex.org/keywords/depth-perception","display_name":"Depth perception","score":0.5068688988685608},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5028085708618164},{"id":"https://openalex.org/keywords/measured-depth","display_name":"Measured depth","score":0.4907461404800415},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4797131419181824},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4434850811958313},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4296892285346985},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.4215957820415497},{"id":"https://openalex.org/keywords/depth-of-field","display_name":"Depth of field","score":0.4196254312992096},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.41188061237335205},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.38412198424339294},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.22139278054237366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1473110020160675},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11596822738647461},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.10603728890419006},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.09966447949409485}],"concepts":[{"id":"https://openalex.org/C141268832","wikidata":"https://www.wikidata.org/wiki/Q2940499","display_name":"Depth map","level":3,"score":0.8291540145874023},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7301144003868103},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7201390266418457},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7070354223251343},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.6729719638824463},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.6553902626037598},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5693586468696594},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5099908113479614},{"id":"https://openalex.org/C52672216","wikidata":"https://www.wikidata.org/wiki/Q1749840","display_name":"Depth perception","level":3,"score":0.5068688988685608},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5028085708618164},{"id":"https://openalex.org/C113346285","wikidata":"https://www.wikidata.org/wiki/Q6804193","display_name":"Measured depth","level":2,"score":0.4907461404800415},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4797131419181824},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4434850811958313},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4296892285346985},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.4215957820415497},{"id":"https://openalex.org/C183072630","wikidata":"https://www.wikidata.org/wiki/Q215932","display_name":"Depth of field","level":2,"score":0.4196254312992096},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.41188061237335205},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.38412198424339294},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.22139278054237366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1473110020160675},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11596822738647461},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.10603728890419006},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.09966447949409485},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","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},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2018.8686952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2018.8686952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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":25,"referenced_works":["https://openalex.org/W345598540","https://openalex.org/W603908379","https://openalex.org/W1678035058","https://openalex.org/W1923848918","https://openalex.org/W2026726159","https://openalex.org/W2047360232","https://openalex.org/W2089485155","https://openalex.org/W2098841537","https://openalex.org/W2102605133","https://openalex.org/W2107462260","https://openalex.org/W2117248802","https://openalex.org/W2126043703","https://openalex.org/W2150066425","https://openalex.org/W2242218935","https://openalex.org/W2254462240","https://openalex.org/W2555618208","https://openalex.org/W2558923462","https://openalex.org/W2774996270","https://openalex.org/W2787455114","https://openalex.org/W2796347433","https://openalex.org/W2963316641","https://openalex.org/W2963400571","https://openalex.org/W2963416674","https://openalex.org/W3139167831","https://openalex.org/W4293584584"],"related_works":["https://openalex.org/W2915493008","https://openalex.org/W2756963361","https://openalex.org/W2771003565","https://openalex.org/W4229030109","https://openalex.org/W2050501141","https://openalex.org/W2771419958","https://openalex.org/W2971759143","https://openalex.org/W2080890436","https://openalex.org/W4386323547","https://openalex.org/W2289073019"],"abstract_inverted_index":{"Depth":[0],"completion":[1,23,84],"is":[2,38,52,65,77],"an":[3],"important":[4],"research":[5],"field":[6],"in":[7,99,109,126],"self-driving":[8],"which":[9,113],"complements":[10],"depths":[11],"mapped":[12],"from":[13],"point":[14],"cloud":[15],"of":[16,43,107],"Lidar.":[17],"This":[18],"paper":[19],"shows":[20],"a":[21,35,41,56,60,115],"depth":[22,36,83,100,111],"method":[24,64],"combining":[25],"traditional":[26],"image":[27,30,58,119],"processing":[28],"and":[29,47,50,59,69,94],"optimization.":[31],"In":[32],"our":[33],"method,":[34],"map":[37],"completed":[39],"through":[40],"series":[42],"well-designed":[44],"morphological":[45],"dilation":[46],"filtering":[48],"methods,":[49],"then":[51],"optimized":[53],"referring":[54],"to":[55],"RGB":[57],"confidence":[61],"map.":[62],"The":[63,87],"simple,":[66],"data":[67],"independent":[68],"runs":[70],"only":[71],"relying":[72],"on":[73,79],"the":[74,80,105,110],"CPU.":[75],"It":[76],"evaluated":[78],"challenging":[81],"KITTI":[82],"benchmark":[85],"[20].":[86],"result":[88],"performs":[89],"as":[90,92],"good":[91],"IP-Basic":[93],"better":[95],"than":[96],"sparse":[97],"CNN":[98],"accuracy.":[101],"Furthermore,":[102],"it":[103],"optimizes":[104],"edges":[106],"objects":[108],"maps,":[112],"has":[114],"greater":[116],"help":[117],"for":[118],"segmentation,":[120],"obstacle":[121],"perception":[122],"or":[123],"other":[124],"tasks":[125],"self-driving.":[127]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
