{"id":"https://openalex.org/W4402722193","doi":"https://doi.org/10.1145/3641825.3689528","title":"Object-Specific and Generic Difference Detection for Non-Destructive Testing Methods","display_name":"Object-Specific and Generic Difference Detection for Non-Destructive Testing Methods","publication_year":2024,"publication_date":"2024-09-22","ids":{"openalex":"https://openalex.org/W4402722193","doi":"https://doi.org/10.1145/3641825.3689528"},"language":"en","primary_location":{"id":"doi:10.1145/3641825.3689528","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641825.3689528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"30th ACM Symposium on Virtual Reality Software and Technology","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/A5027253951","display_name":"Andreas Dietze","orcid":null},"institutions":[{"id":"https://openalex.org/I107257983","display_name":"Darmstadt University of Applied Sciences","ror":"https://ror.org/047wbd030","country_code":"DE","type":"education","lineage":["https://openalex.org/I107257983"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Andreas Dietze","raw_affiliation_strings":["Darmstadt University of Applied Sciences, Germany"],"raw_orcid":"https://orcid.org/0009-0001-1539-4079","affiliations":[{"raw_affiliation_string":"Darmstadt University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I107257983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109924378","display_name":"Yvonne Jung","orcid":null},"institutions":[{"id":"https://openalex.org/I107257983","display_name":"Darmstadt University of Applied Sciences","ror":"https://ror.org/047wbd030","country_code":"DE","type":"education","lineage":["https://openalex.org/I107257983"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yvonne Jung","raw_affiliation_strings":["Darmstadt University of Applied Sciences, Germany"],"raw_orcid":"https://orcid.org/0009-0007-8560-3298","affiliations":[{"raw_affiliation_string":"Darmstadt University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I107257983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011930847","display_name":"Paul Grimm","orcid":"https://orcid.org/0000-0003-4189-2642"},"institutions":[{"id":"https://openalex.org/I107257983","display_name":"Darmstadt University of Applied Sciences","ror":"https://ror.org/047wbd030","country_code":"DE","type":"education","lineage":["https://openalex.org/I107257983"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Paul Grimm","raw_affiliation_strings":["Darmstadt University of Applied Sciences, Germany"],"raw_orcid":"https://orcid.org/0000-0003-4189-2642","affiliations":[{"raw_affiliation_string":"Darmstadt University of Applied Sciences, Germany","institution_ids":["https://openalex.org/I107257983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027253951"],"corresponding_institution_ids":["https://openalex.org/I107257983"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13698779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10320","display_name":"Neural Networks and Applications","score":0.991599977016449,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/computer-science","display_name":"Computer science","score":0.6388183236122131},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4500831961631775},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4233907461166382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39312469959259033},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2569785714149475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6388183236122131},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4500831961631775},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4233907461166382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39312469959259033},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2569785714149475}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3641825.3689528","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641825.3689528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"30th ACM Symposium on Virtual Reality Software and Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1993947467","https://openalex.org/W2798880724","https://openalex.org/W3108389598","https://openalex.org/W4200056384","https://openalex.org/W4236805344","https://openalex.org/W4384936630"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W4312842780","https://openalex.org/W2883677709","https://openalex.org/W2922421953","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"In":[0,29,297],"this":[1,72,121,299],"arcticle,":[2],"we":[3],"present":[4,225],"a":[5,8,13,30,58,74,79,100,243,253,280,361,370,396,402],"concept":[6],"of":[7,42,57,71,103,118,149,173,218,222,249,269,272,320,344,365,374],"tool":[9],"suite":[10],"that":[11,165,258,305],"combines":[12],"generic":[14],"and":[15,22,37,61,126,132,135,156,159,188,197,230,340,369,386,394],"object-specific":[16],"difference":[17,213,263],"detection":[18,214,264,268],"between":[19,35,336],"3D":[20,23,52,59,63,89,101,112,367,391,409],"measurment":[21],"planning":[24,38,64,113,157,204,231,239,329,338,392],"data":[25,53,65,114,158,205,210,232,240,330,339,393,407],"(cp.":[26],"Figure":[27],"1).":[28],"Generic":[31],"Difference":[32,144],"Detection":[33,145],"(GDD)":[34],"measurement":[36,155,209,229,237],"data,":[39],"the":[40,50,55,62,104,138,147,154,171,185,203,220,228,236,247,294,306,313,318,328,337,341,345,349,390],"objects":[41,148,368],"interest":[43,150,223],"to":[44,167,303,333,360],"be":[45,168,178,286],"compared":[46,108],"are":[47,151,160,200,351],"represented":[48,161],"by":[49,162,261,290],"acquired":[51,208],"(often":[54],"solid":[56],"reconstruction)":[60],"in":[66,98,120,141,183,226,235,331,347],"their":[67],"entirety.":[68],"An":[69],"example":[70],"is":[73,107,123,224,259,271,308,316,358],"shape":[75,362],"analysis":[76,364],"used":[77],"as":[78,180,194],"non-destructive":[80],"testing":[81],"method":[82,378],"for":[83,356,379],"products":[84],"from":[85],"an":[86,142,181,266],"additive":[87],"(e.g.":[88,93,241,283,405],"printing)":[90],"or":[91,233,238,255,288,324,408],"subtractive":[92],"CNC":[94],"milling)":[95],"manufacturing":[96],"process,":[97],"which":[99,184],"reconstruction":[102],"produced":[105],"object":[106,221],"with":[109],"its":[110],"orignial":[111],"[2].":[115],"The":[116],"range":[117],"applications":[119],"area":[122],"very":[124],"broad":[125],"includes":[127],"quality":[128,250],"controls,":[129],"plagiarism":[130],"checks":[131],"measuring":[133],"wear":[134],"tear.":[136],"On":[137],"other":[139],"hand,":[140],"Object-Specific":[143],"(OSDD)":[146],"located":[152],"within":[153],"specific":[163,190],"components":[164],"have":[166],"compared.":[169],"Here,":[170],"sector":[172],"digital":[174],"construction":[175,186,191,256,281,295],"monitoring":[176],"can":[177,285,300,381],"mentioned":[179],"example,":[182,276],"process":[187,257,282],"associated":[189],"components,":[192],"such":[193],"walls,":[195],"passages":[196],"window":[198],"openings,":[199],"checked":[201],"against":[202],"based":[206,400],"on":[207,312,401],"[1].":[211],"Both":[212],"techniques":[215],"work,":[216],"regardless":[217],"whether":[219],"both":[227,262],"only":[234],"after":[242,279],"building":[244],"refurbishment).":[245],"Besides":[246],"benefit":[248],"assurance":[251],"following":[252],"production":[254],"covered":[260],"concepts,":[265],"early":[267],"errors":[270,292,325,387],"great":[273],"advantage.":[274],"For":[275],"follow-up":[277],"costs":[278],"building)":[284],"reduced":[287],"avoided":[289],"identifying":[291],"during":[293],"process.":[296],"addition,":[298],"also":[301,317],"help":[302],"ensure":[304],"schedule":[307],"adhered":[309],"to.":[310],"Depending":[311],"application,":[314],"there":[315],"option":[319],"feeding":[321],"identified":[322],"deviations":[323,385],"back":[326,388],"into":[327,389],"order":[332],"achieve":[334],"synchronization":[335],"actual":[342],"state":[343],"object,":[346],"case":[348],"differences":[350],"intentional.":[352],"While":[353],"our":[354,377],"approach":[355],"GDD":[357],"limited":[359],"similarity":[363],"two":[366],"subsequent":[371],"real-time":[372],"visualization":[373,399],"detected":[375,384],"differences,":[376],"OSDD":[380],"already":[382],"feed":[383],"allows":[395],"collaborative":[397],"result":[398],"multi-codal":[403],"presentation":[404],"tabular":[406],"rendering).":[410]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}