{"id":"https://openalex.org/W2530836950","doi":"https://doi.org/10.2312/pg.20161328","title":"Modified Filtered Importance Sampling for Virtual Spherical Gaussian Lights","display_name":"Modified Filtered Importance Sampling for Virtual Spherical Gaussian Lights","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2530836950","doi":"https://doi.org/10.2312/pg.20161328","mag":"2530836950"},"language":"en","primary_location":{"id":"doi:10.2312/pg.20161328","is_oa":true,"landing_page_url":"https://doi.org/10.2312/pg.20161328","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/pg.20161328","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000082719","display_name":"Yusuke Tokuyoshi","orcid":"https://orcid.org/0000-0002-6612-1812"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tokuyoshi, Yusuke","raw_affiliation_strings":["Square Enix Co., Ltd., Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Square Enix Co., Ltd., Japan#TAB#","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5000082719"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":"4","first_page":"13","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10638","display_name":"Optical measurement and interference techniques","score":0.998199999332428,"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/T11408","display_name":"Advanced Optical Imaging Technologies","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/kernel","display_name":"Kernel (algebra)","score":0.8692452311515808},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.6157740354537964},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5440106391906738},{"id":"https://openalex.org/keywords/kernel-embedding-of-distributions","display_name":"Kernel embedding of distributions","score":0.5266420841217041},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5061666369438171},{"id":"https://openalex.org/keywords/gaussian-function","display_name":"Gaussian function","score":0.4998924732208252},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.49598774313926697},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.4942610263824463},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.45349323749542236},{"id":"https://openalex.org/keywords/kernel-smoother","display_name":"Kernel smoother","score":0.4317344129085541},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4269251227378845},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3891424834728241},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3810630142688751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26697924733161926},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2543526589870453},{"id":"https://openalex.org/keywords/radial-basis-function-kernel","display_name":"Radial basis function kernel","score":0.24230629205703735},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.24194088578224182},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22555434703826904},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10055390000343323},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.07589581608772278}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.8692452311515808},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.6157740354537964},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5440106391906738},{"id":"https://openalex.org/C134517425","wikidata":"https://www.wikidata.org/wiki/Q16000131","display_name":"Kernel embedding of distributions","level":4,"score":0.5266420841217041},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5061666369438171},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.4998924732208252},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49598774313926697},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.4942610263824463},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.45349323749542236},{"id":"https://openalex.org/C27406209","wikidata":"https://www.wikidata.org/wiki/Q6394203","display_name":"Kernel smoother","level":5,"score":0.4317344129085541},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4269251227378845},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3891424834728241},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3810630142688751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26697924733161926},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2543526589870453},{"id":"https://openalex.org/C75866337","wikidata":"https://www.wikidata.org/wiki/Q7280263","display_name":"Radial basis function kernel","level":4,"score":0.24230629205703735},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.24194088578224182},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22555434703826904},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10055390000343323},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.07589581608772278},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.2312/pg.20161328","is_oa":true,"landing_page_url":"https://doi.org/10.2312/pg.20161328","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null},{"id":"mag:2530836950","is_oa":false,"landing_page_url":"https://link.springer.com/content/pdf/10.1007%2Fs41095-016-0063-3.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.2312/pg.20161328","is_oa":true,"landing_page_url":"https://doi.org/10.2312/pg.20161328","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1486787228","https://openalex.org/W1539735158","https://openalex.org/W1559769428","https://openalex.org/W1589761358","https://openalex.org/W1900889178","https://openalex.org/W1965458840","https://openalex.org/W1966701964","https://openalex.org/W1976544585","https://openalex.org/W1985999741","https://openalex.org/W2015211965","https://openalex.org/W2017086619","https://openalex.org/W2022552637","https://openalex.org/W2024913359","https://openalex.org/W2028497605","https://openalex.org/W2029704386","https://openalex.org/W2030459990","https://openalex.org/W2033331939","https://openalex.org/W2035653341","https://openalex.org/W2036750154","https://openalex.org/W2037418484","https://openalex.org/W2038327694","https://openalex.org/W2058018255","https://openalex.org/W2060995201","https://openalex.org/W2066334759","https://openalex.org/W2085151796","https://openalex.org/W2109289698","https://openalex.org/W2112146509","https://openalex.org/W2117029017","https://openalex.org/W2117731961","https://openalex.org/W2134158653","https://openalex.org/W2138624212","https://openalex.org/W2161446118","https://openalex.org/W2169859419","https://openalex.org/W2170371392","https://openalex.org/W2257275871","https://openalex.org/W2258826119","https://openalex.org/W2289682477","https://openalex.org/W2613999877","https://openalex.org/W2912424718"],"related_works":["https://openalex.org/W2102706100","https://openalex.org/W2902455184","https://openalex.org/W2758914693","https://openalex.org/W280736364","https://openalex.org/W2360601193","https://openalex.org/W2068359544","https://openalex.org/W2832712367","https://openalex.org/W2099114783","https://openalex.org/W2959696386","https://openalex.org/W1953192676","https://openalex.org/W2063392308","https://openalex.org/W3101376812","https://openalex.org/W2117687077","https://openalex.org/W1702794521","https://openalex.org/W2739234443","https://openalex.org/W2295511631","https://openalex.org/W2069151040","https://openalex.org/W3003751474","https://openalex.org/W2840957353","https://openalex.org/W3115146140"],"abstract_inverted_index":{"This":[0,49],"paper":[1,77],"proposes":[2],"a":[3,138],"modification":[4],"of":[5,16,37,54,67,85],"the":[6,14,52,55,61,64,68,79,83,86,89,124],"filtered":[7],"importance":[8],"sampling":[9],"(FIS)":[10],"method,":[11],"and":[12,38,74],"improves":[13],"quality":[15],"virtual":[17],"spherical":[18],"Gaussian":[19],"light":[20],"(VSGL)":[21],"based":[22],"real-time":[23],"glossy":[24],"indirect":[25],"illumination":[26],"using":[27,60,82,107],"this":[28,76,120],"modification.":[29],"The":[30],"original":[31],"FIS":[32],"method":[33],"produces":[34],"large":[35],"overlaps":[36,73],"gaps":[39],"between":[40],"filtering":[41,56,90],"kernels":[42],"for":[43,128],"high-frequency":[44],"probability":[45],"density":[46],"functions":[47],"(PDFs).":[48],"is":[50,58,95],"because":[51],"size":[53,81,114,127],"kernel":[57,80,103,113,126],"determined":[59],"PDF":[62,87],"at":[63],"sampled":[65,106],"center":[66],"kernel.":[69,91],"To":[70],"reduce":[71],"those":[72],"gaps,":[75],"determines":[78],"integral":[84,121],"in":[88],"Our":[92],"key":[93],"insight":[94],"that":[96],"these":[97],"integrals":[98],"are":[99,105,134],"approximately":[100],"constant,":[101],"if":[102],"centers":[104],"stratified":[108],"sampling.":[109],"Therefore,":[110],"an":[111],"appropriate":[112],"can":[115],"be":[116],"obtained":[117],"by":[118],"solving":[119],"equation.":[122],"Using":[123],"proposed":[125],"FIS-based":[129],"VSGL":[130],"generation,":[131],"undesirable":[132],"artifacts":[133],"significantly":[135],"reduced":[136],"with":[137],"negligibly":[139],"small":[140],"overhead.":[141]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}