{"id":"https://openalex.org/W2736683165","doi":"https://doi.org/10.1145/3105762.3105778","title":"Fast maximal Poisson-disk sampling by randomized tiling","display_name":"Fast maximal Poisson-disk sampling by randomized tiling","publication_year":2017,"publication_date":"2017-07-26","ids":{"openalex":"https://openalex.org/W2736683165","doi":"https://doi.org/10.1145/3105762.3105778","mag":"2736683165"},"language":"en","primary_location":{"id":"doi:10.1145/3105762.3105778","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3105762.3105778","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of High Performance Graphics","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/A5101606947","display_name":"Tong Wang","orcid":"https://orcid.org/0000-0001-9698-7054"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tong Wang","raw_affiliation_strings":["University of Tokyo"],"affiliations":[{"raw_affiliation_string":"University of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009108314","display_name":"Reiji Suda","orcid":"https://orcid.org/0000-0001-8797-6011"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Reiji Suda","raw_affiliation_strings":["University of Tokyo"],"affiliations":[{"raw_affiliation_string":"University of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101606947"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.2731,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63200263,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9902999997138977,"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/T10057","display_name":"Face and Expression Recognition","score":0.9902999997138977,"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/T11448","display_name":"Face recognition and analysis","score":0.9854999780654907,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9832000136375427,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.5808025598526001},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5333787202835083},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4676468074321747},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.43030795454978943},{"id":"https://openalex.org/keywords/poisson-sampling","display_name":"Poisson sampling","score":0.41339898109436035},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31406310200691223},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.20615935325622559},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.123758465051651},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09111177921295166},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.07386428117752075}],"concepts":[{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.5808025598526001},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5333787202835083},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4676468074321747},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43030795454978943},{"id":"https://openalex.org/C82152865","wikidata":"https://www.wikidata.org/wiki/Q7208505","display_name":"Poisson sampling","level":5,"score":0.41339898109436035},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31406310200691223},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.20615935325622559},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.123758465051651},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09111177921295166},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.07386428117752075},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3105762.3105778","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3105762.3105778","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of High Performance Graphics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W52736401","https://openalex.org/W1564611446","https://openalex.org/W1969253908","https://openalex.org/W1975498219","https://openalex.org/W1984609374","https://openalex.org/W2005197983","https://openalex.org/W2010269264","https://openalex.org/W2011172006","https://openalex.org/W2016165013","https://openalex.org/W2020457555","https://openalex.org/W2043101875","https://openalex.org/W2046250075","https://openalex.org/W2049153601","https://openalex.org/W2054057322","https://openalex.org/W2056797335","https://openalex.org/W2057443286","https://openalex.org/W2059058071","https://openalex.org/W2071106779","https://openalex.org/W2087027299","https://openalex.org/W2099594591","https://openalex.org/W2099909906","https://openalex.org/W2107699494","https://openalex.org/W2109673564","https://openalex.org/W2115876670","https://openalex.org/W2123732640","https://openalex.org/W2132282325","https://openalex.org/W2144337074","https://openalex.org/W2153935912","https://openalex.org/W3137702954","https://openalex.org/W4241614188","https://openalex.org/W4250638048","https://openalex.org/W4254657132"],"related_works":["https://openalex.org/W3107697994","https://openalex.org/W2301330492","https://openalex.org/W4238714840","https://openalex.org/W2381050925","https://openalex.org/W3047864323","https://openalex.org/W3212973961","https://openalex.org/W2994963386","https://openalex.org/W2970859251","https://openalex.org/W2041859396","https://openalex.org/W1567644694"],"abstract_inverted_index":{"It":[0],"is":[1,47,109],"generally":[2],"accepted":[3],"that":[4,76],"Poisson":[5],"disk":[6],"sampling":[7],"provides":[8],"great":[9],"properties":[10],"in":[11,14,89],"various":[12],"applications":[13],"computer":[15],"graphics.":[16],"We":[17],"present":[18],"KD-tree":[19],"based":[20],"randomized":[21],"tiling":[22],"(KDRT),":[23],"an":[24,56],"efficient":[25],"method":[26,46,78,108,113],"to":[27,67,117,126],"generate":[28,81],"maximal":[29,82],"Poisson-disk":[30,58,83,105],"samples":[31,84],"by":[32],"replicating":[33],"and":[34,61,103],"conquering":[35,63],"tiles":[36,54,65],"clipped":[37],"from":[38,55],"a":[39,48,101,128,136],"pattern":[40],"of":[41,97,121,140],"very":[42,86],"small":[43,87],"size.":[44],"Our":[45],"two-step":[49],"process:":[50],"first,":[51],"randomly":[52],"clipping":[53],"MPS(Maximal":[57],"Sample)":[59],"pattern,":[60],"second,":[62],"these":[64],"together":[66],"form":[68,127],"the":[69],"whole":[70],"sample":[71,106,124],"plane.":[72],"The":[73],"results":[74],"showed":[75],"this":[77,98,112],"can":[79,114,132],"efficiently":[80],"with":[85],"trade-off":[88],"bias":[90],"error.":[91],"There":[92],"are":[93],"two":[94],"main":[95],"contributions":[96],"paper:":[99],"First,":[100],"fast":[102],"robust":[104],"generation":[107],"presented;":[110],"Second,":[111],"be":[115,133],"used":[116],"combine":[118],"several":[119],"groups":[120],"independently":[122],"generated":[123],"patterns":[125],"larger":[129],"one,":[130],"thus":[131],"applied":[134],"as":[135],"general":[137],"parallelization":[138],"scheme":[139],"any":[141],"MPS":[142],"methods.":[143]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
