{"id":"https://openalex.org/W2885324189","doi":"https://doi.org/10.1145/3341156","title":"Neural Importance Sampling","display_name":"Neural Importance Sampling","publication_year":2019,"publication_date":"2019-10-10","ids":{"openalex":"https://openalex.org/W2885324189","doi":"https://doi.org/10.1145/3341156","mag":"2885324189"},"language":"en","primary_location":{"id":"doi:10.1145/3341156","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341156","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1808.03856","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100661927","display_name":"Thomas M\u00fcller","orcid":"https://orcid.org/0000-0001-7577-755X"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Thomas M\u00fcller","raw_affiliation_strings":["Disney Research 8 ETH Z\u00fcrich","Disney Research 8 ETH Z\u00fcrich#TAB#"],"raw_orcid":"https://orcid.org/0000-0001-7577-755X","affiliations":[{"raw_affiliation_string":"Disney Research 8 ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I4210142140"]},{"raw_affiliation_string":"Disney Research 8 ETH Z\u00fcrich#TAB#","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I4210142140"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041664883","display_name":"Brian McWilliams","orcid":"https://orcid.org/0009-0002-7433-1702"},"institutions":[{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brian Mcwilliams","raw_affiliation_strings":["Disney Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Disney Research","institution_ids":["https://openalex.org/I4210142140"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037043205","display_name":"Fabrice Rousselle","orcid":"https://orcid.org/0009-0003-2978-2130"},"institutions":[{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fabrice Rousselle","raw_affiliation_strings":["Disney Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Disney Research","institution_ids":["https://openalex.org/I4210142140"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033076979","display_name":"Markus Gro\u00df","orcid":"https://orcid.org/0009-0003-9324-779X"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Markus Gross","raw_affiliation_strings":["Disney Research 8 ETH Z\u00fcrich","ETH Z\u00fcrich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Disney Research 8 ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088","https://openalex.org/I4210142140"]},{"raw_affiliation_string":"ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050969104","display_name":"Jan Nov\u00e1k","orcid":"https://orcid.org/0000-0002-8320-9584"},"institutions":[{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jan Nov\u00e1k","raw_affiliation_strings":["Disney Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Disney Research","institution_ids":["https://openalex.org/I4210142140"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.8154,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.97362167,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"38","issue":"5","first_page":"1","last_page":"19"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9988999962806702,"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":0.9988999962806702,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9987000226974487,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.655420184135437},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5865591764450073},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.5847823023796082},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5403223037719727},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5387455224990845},{"id":"https://openalex.org/keywords/path-tracing","display_name":"Path tracing","score":0.5185049772262573},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5145394206047058},{"id":"https://openalex.org/keywords/monte-carlo-integration","display_name":"Monte Carlo integration","score":0.49106550216674805},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4673507511615753},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.4584992825984955},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4541941285133362},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4498044550418854},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4457095265388489},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.39039936661720276},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3457493782043457},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24517840147018433},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.20749890804290771}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.655420184135437},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5865591764450073},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.5847823023796082},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5403223037719727},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5387455224990845},{"id":"https://openalex.org/C110541219","wikidata":"https://www.wikidata.org/wiki/Q72948","display_name":"Path tracing","level":3,"score":0.5185049772262573},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5145394206047058},{"id":"https://openalex.org/C132725507","wikidata":"https://www.wikidata.org/wiki/Q39879","display_name":"Monte Carlo integration","level":5,"score":0.49106550216674805},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4673507511615753},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.4584992825984955},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4541941285133362},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4498044550418854},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4457095265388489},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.39039936661720276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3457493782043457},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24517840147018433},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.20749890804290771},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.0},{"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":4,"locations":[{"id":"doi:10.1145/3341156","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341156","pdf_url":null,"source":{"id":"https://openalex.org/S185367456","display_name":"ACM Transactions on Graphics","issn_l":"0730-0301","issn":["0730-0301","1557-7368"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Graphics","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1808.03856","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1808.03856","pdf_url":"https://arxiv.org/pdf/1808.03856","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.1808.03856","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1808.03856","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"Preprint"},{"id":"mag:2885324189","is_oa":false,"landing_page_url":null,"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":"pmh:oai:arXiv.org:1808.03856","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1808.03856","pdf_url":"https://arxiv.org/pdf/1808.03856","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2885324189.pdf","grobid_xml":"https://content.openalex.org/works/W2885324189.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W206338015","https://openalex.org/W1489869152","https://openalex.org/W1533861849","https://openalex.org/W1534875171","https://openalex.org/W1583912456","https://openalex.org/W1797096580","https://openalex.org/W1834627138","https://openalex.org/W1866230956","https://openalex.org/W1966011536","https://openalex.org/W2021131939","https://openalex.org/W2086913350","https://openalex.org/W2090346540","https://openalex.org/W2098077797","https://openalex.org/W2099471712","https://openalex.org/W2135596692","https://openalex.org/W2138624212","https://openalex.org/W2139189324","https://openalex.org/W2142380402","https://openalex.org/W2151047098","https://openalex.org/W2267126114","https://openalex.org/W2409550820","https://openalex.org/W2496551394","https://openalex.org/W2533864612","https://openalex.org/W2587284713","https://openalex.org/W2624086852","https://openalex.org/W2731574721","https://openalex.org/W2737635400","https://openalex.org/W2758667594","https://openalex.org/W2810980623","https://openalex.org/W2882449932","https://openalex.org/W2888943822","https://openalex.org/W2898801573","https://openalex.org/W2949382160","https://openalex.org/W2963047245","https://openalex.org/W2963090522","https://openalex.org/W2963139417","https://openalex.org/W2963755523","https://openalex.org/W2963775850","https://openalex.org/W2964121744","https://openalex.org/W2964343746","https://openalex.org/W2966144556","https://openalex.org/W3015136933"],"related_works":["https://openalex.org/W2979652999","https://openalex.org/W2964121744","https://openalex.org/W1581892430","https://openalex.org/W2902999023","https://openalex.org/W2798437152","https://openalex.org/W2807156663","https://openalex.org/W3010157578","https://openalex.org/W1824523713","https://openalex.org/W2039298643","https://openalex.org/W2117506930","https://openalex.org/W2899669189","https://openalex.org/W3128611278","https://openalex.org/W2891604924","https://openalex.org/W3115207133","https://openalex.org/W3047495759","https://openalex.org/W2275326934","https://openalex.org/W2920845113","https://openalex.org/W2583783869","https://openalex.org/W2783176791","https://openalex.org/W1494871301"],"abstract_inverted_index":{"We":[0,126],"propose":[1,58],"to":[2,30,37,59,138,167],"use":[3,164],"deep":[4],"neural":[5,64],"networks":[6,65],"for":[7,83,91,191],"generating":[8,131],"samples":[9],"in":[10,27,135,149,182],"Monte":[11,96],"Carlo":[12,97],"integration.":[13],"Our":[14,107],"work":[15],"is":[16],"based":[17],"on":[18,130],"non-linear":[19],"independent":[20],"components":[21],"estimation":[22],"(NICE),":[23],"which":[24,69],"we":[25,41,57,78,142,163,187],"extend":[26],"numerous":[28],"ways":[29],"improve":[31],"performance":[32,203],"and":[33,74,86,111,114,134,154,179,186],"enable":[34],"its":[35,128],"application":[36,94],"integration":[38,98,124],"problems.":[39],"First,":[40],"introduce":[42],"piecewise-polynomial":[43],"coupling":[44,54],"transforms":[45],"that":[46],"greatly":[47],"increase":[48],"the":[49,61,84,87,92,104,120,123,150,174,180,183,189],"modeling":[50],"power":[51],"of":[52,63,72,95,103,119,122,145,157,176],"individual":[53],"layers.":[55],"Second,":[56,162],"preprocess":[60],"inputs":[62],"using":[66],"one-blob":[67],"encoding,":[68],"stimulates":[70],"localization":[71],"computation":[73],"improves":[75],"inference.":[76],"Third,":[77],"derive":[79],"a":[80],"gradient-descent-based":[81],"optimization":[82],"Kullback-Leibler":[85],"\u03c7":[88],"2":[89],"divergence":[90],"specific":[93],"with":[99],"unnormalized":[100],"stochastic":[101],"estimates":[102],"target":[105],"distribution.":[106],"approach":[108,198],"enables":[109],"fast":[110],"accurate":[112],"inference":[113],"efficient":[115],"sample":[116,152,209],"generation":[117],"independently":[118],"dimensionality":[121],"domain.":[125],"show":[127],"benefits":[129],"natural":[132],"images":[133],"two":[136],"applications":[137],"light-transport":[139],"simulation:":[140],"first,":[141],"demonstrate":[143],"learning":[144],"joint":[146],"path-sampling":[147],"densities":[148,171,190],"primary":[151],"space":[153],"importance":[155],"sampling":[156],"multi-dimensional":[158],"path":[159,192],"prefixes":[160],"thereof.":[161],"our":[165,197],"technique":[166],"extract":[168],"conditional":[169],"directional":[170],"driven":[172],"by":[173],"product":[175],"incident":[177],"illumination":[178],"BSDF":[181],"rendering":[184],"equation,":[185],"leverage":[188],"guiding.":[193],"In":[194],"all":[195],"applications,":[196],"yields":[199],"on-par":[200],"or":[201],"higher":[202],"than":[204],"competing":[205],"techniques":[206],"at":[207],"equal":[208],"count.":[210]},"counts_by_year":[{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":6}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}