{"id":"https://openalex.org/W4389780979","doi":"https://doi.org/10.1145/3627341.3630370","title":"Three-Dimensional Object Segmentation Method based on YOLO, SAM, and NeRF","display_name":"Three-Dimensional Object Segmentation Method based on YOLO, SAM, and NeRF","publication_year":2023,"publication_date":"2023-08-25","ids":{"openalex":"https://openalex.org/W4389780979","doi":"https://doi.org/10.1145/3627341.3630370"},"language":"en","primary_location":{"id":"doi:10.1145/3627341.3630370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627341.3630370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Computer, Vision and Intelligent 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":null,"display_name":"Xinkai Wang","orcid":"https://orcid.org/0009-0000-8447-4411"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinkai Wang","raw_affiliation_strings":["Beijing University of Chemical Technology, China"],"raw_orcid":"https://orcid.org/0009-0000-8447-4411","affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100390896","display_name":"Liang Yuan","orcid":"https://orcid.org/0000-0002-7068-8766"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Yuan","raw_affiliation_strings":["Beijing University of Chemical Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-7068-8766","affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036994016","display_name":"Lihui Sun","orcid":"https://orcid.org/0009-0000-2612-7606"},"institutions":[{"id":"https://openalex.org/I4210148107","display_name":"Space Engineering University","ror":"https://ror.org/04rj1td02","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210148107"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihui Sun","raw_affiliation_strings":["Space Engineering University, China"],"raw_orcid":"https://orcid.org/0009-0000-2612-7606","affiliations":[{"raw_affiliation_string":"Space Engineering University, China","institution_ids":["https://openalex.org/I4210148107"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103004310","display_name":"Shangzhe Wu","orcid":"https://orcid.org/0009-0000-1848-8524"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangzhe Wu","raw_affiliation_strings":["Beijing University of Chemical Technology, China"],"raw_orcid":"https://orcid.org/0009-0000-1848-8524","affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087070904","display_name":"Ajian Liu","orcid":"https://orcid.org/0009-0009-5238-0387"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ajian Liu","raw_affiliation_strings":["Beijing University of Chemical Technology, China"],"raw_orcid":"https://orcid.org/0009-0009-5238-0387","affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16738013,"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":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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":0.998199999332428,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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":0.8259241580963135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8023979663848877},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7724763751029968},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6774431467056274},{"id":"https://openalex.org/keywords/radiance","display_name":"Radiance","score":0.5934208631515503},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5359737277030945},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5344672799110413},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5242883563041687},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4948963522911072},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.46852821111679077},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4586276412010193},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4480050206184387},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.4360651969909668},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.41214215755462646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40877842903137207},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.08694455027580261},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08567777276039124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8259241580963135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8023979663848877},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7724763751029968},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6774431467056274},{"id":"https://openalex.org/C23690007","wikidata":"https://www.wikidata.org/wiki/Q1411145","display_name":"Radiance","level":2,"score":0.5934208631515503},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5359737277030945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5344672799110413},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5242883563041687},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4948963522911072},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.46852821111679077},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4586276412010193},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4480050206184387},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.4360651969909668},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.41214215755462646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40877842903137207},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.08694455027580261},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08567777276039124},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627341.3630370","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3627341.3630370","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology","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":21,"referenced_works":["https://openalex.org/W2765811365","https://openalex.org/W2964288609","https://openalex.org/W3210613940","https://openalex.org/W3215769467","https://openalex.org/W4200150166","https://openalex.org/W4221151978","https://openalex.org/W4225544038","https://openalex.org/W4295105838","https://openalex.org/W4311994463","https://openalex.org/W4312632331","https://openalex.org/W4320487943","https://openalex.org/W4323557179","https://openalex.org/W4360886184","https://openalex.org/W4360890640","https://openalex.org/W4361021309","https://openalex.org/W4361193327","https://openalex.org/W4377130481","https://openalex.org/W4386075659","https://openalex.org/W6778485988","https://openalex.org/W6846200007","https://openalex.org/W6850687356"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W1999008862","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2551987074"],"abstract_inverted_index":{"The":[0,134],"neural":[1,36,147],"radiance":[2,37,148],"field":[3],"(NeRF)":[4],"representation":[5],"has":[6],"shown":[7],"promising":[8],"results":[9],"in":[10,67,209,219],"capturing":[11],"3D":[12,49,74,142],"scenes.":[13],"However,":[14],"when":[15],"it":[16,27],"comes":[17],"to":[18,30,111,196],"editing":[19],"or":[20],"moving":[21],"specific":[22,157],"objects":[23,33,115],"within":[24],"virtual":[25],"environments,":[26],"is":[28,137],"essential":[29],"segment":[31,161],"individual":[32],"from":[34,55,81,93],"the":[35,94,103,114,118,126,146,165,179,190,197,225,231],"field.":[38],"Existing":[39],"methods":[40],"primarily":[41],"rely":[42],"on":[43,156,230],"feature":[44,91],"computation":[45],"and":[46,59,116,185,187,199,214,238],"clustering":[47],"for":[48,131],"segmentation,":[50,70],"but":[51],"they":[52],"often":[53],"suffer":[54],"poor":[56],"segmentation":[57,75,80],"quality":[58],"low":[60],"accuracy.":[61],"Taking":[62],"advantage":[63],"of":[64,182,192,227],"recent":[65],"advancements":[66],"2D":[68],"image":[69],"we":[71,86,101,124,159,177],"propose":[72],"a":[73,141],"method":[76,229],"that":[77,164],"generates":[78],"single-object":[79],"multiple":[82],"viewpoint":[83],"images.":[84,96,153],"First,":[85],"compute":[87],"camera":[88],"poses":[89],"using":[90,145],"points":[92],"original":[95],"Then,":[97],"with":[98],"user":[99],"guidance,":[100],"use":[102],"You":[104],"Only":[105],"Look":[106],"Once":[107],"(YOLO)":[108],"network":[109],"model":[110,169],"roughly":[112],"locate":[113],"utilize":[117],"resulting":[119],"information":[120],"as":[121],"hints.":[122],"Subsequently,":[123],"employ":[125],"Segment":[127],"Anything":[128],"Model":[129],"(SAM)":[130],"fine-grained":[132],"segmentation.":[133],"processed":[135],"dataset":[136],"then":[138],"rendered":[139,152],"into":[140],"segmented":[143],"object":[144,162],"field,":[149],"producing":[150],"high-quality":[151],"By":[154],"training":[155],"objects,":[158],"can":[160],"categories":[163],"publicly":[166],"available":[167],"YOLO":[168],"cannot":[170],"recognize,":[171],"thereby":[172],"enhancing":[173],"generalization.":[174],"Through":[175],"experiments,":[176],"analyze":[178],"real-time":[180],"performance":[181,226],"each":[183],"module":[184],"qualitatively":[186],"quantitatively":[188],"evaluate":[189],"effectiveness":[191],"our":[193,202,228],"method.":[194],"Compared":[195],"N3F":[198],"ISRF":[200],"methods,":[201],"approach":[203],"achieves":[204],"an":[205,215],"average":[206,216],"17%":[207],"improvement":[208,218],"Intersection":[210],"over":[211],"Union":[212],"(IoU)":[213],"9%":[217],"Pixel":[220],"Accuracy":[221],"(Acc).":[222],"We":[223],"validate":[224],"LLFF,":[232],"mip-Nerf360":[233],"datasets,":[234],"demonstrating":[235],"its":[236],"generalizability":[237],"effectiveness.":[239]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}