{"id":"https://openalex.org/W7160898256","doi":"https://doi.org/10.48550/arxiv.2605.09117","title":"Rao-Blackwellized Markov chain Monte Carlo Light Transport","display_name":"Rao-Blackwellized Markov chain Monte Carlo Light Transport","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160898256","doi":"https://doi.org/10.48550/arxiv.2605.09117"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09117","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.09117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114402281","display_name":"Sascha Holl","orcid":"https://orcid.org/0009-0004-7245-7998"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Holl, Sascha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022506083","display_name":"Gurprit Singh","orcid":"https://orcid.org/0000-0003-0970-5835"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Gurprit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135910548","display_name":"Hans-Peter Seidel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seidel, Hans-Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.7450000047683716,"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":0.7450000047683716,"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/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.05429999902844429,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10427","display_name":"Visual perception and processing mechanisms","score":0.025299999862909317,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/variance-reduction","display_name":"Variance reduction","score":0.8083999752998352},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.707099974155426},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.7024000287055969},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6848000288009644},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5199999809265137},{"id":"https://openalex.org/keywords/control-variates","display_name":"Control variates","score":0.5099999904632568},{"id":"https://openalex.org/keywords/jump","display_name":"Jump","score":0.45680001378059387},{"id":"https://openalex.org/keywords/reversible-jump-markov-chain-monte-carlo","display_name":"Reversible-jump Markov chain Monte Carlo","score":0.4474000036716461},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.43209999799728394}],"concepts":[{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.8083999752998352},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.707099974155426},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.7024000287055969},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6848000288009644},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5199999809265137},{"id":"https://openalex.org/C121683094","wikidata":"https://www.wikidata.org/wiki/Q3554721","display_name":"Control variates","level":5,"score":0.5099999904632568},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4943999946117401},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48500001430511475},{"id":"https://openalex.org/C2780695682","wikidata":"https://www.wikidata.org/wiki/Q4005959","display_name":"Jump","level":2,"score":0.45680001378059387},{"id":"https://openalex.org/C2780591659","wikidata":"https://www.wikidata.org/wiki/Q17083869","display_name":"Reversible-jump Markov chain Monte Carlo","level":4,"score":0.4474000036716461},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.43209999799728394},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41290000081062317},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C89720835","wikidata":"https://www.wikidata.org/wiki/Q1531701","display_name":"Global illumination","level":3,"score":0.39169999957084656},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C187653413","wikidata":"https://www.wikidata.org/wiki/Q7135015","display_name":"Parallel tempering","level":5,"score":0.36880001425743103},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.364300012588501},{"id":"https://openalex.org/C108311543","wikidata":"https://www.wikidata.org/wiki/Q7915756","display_name":"Variance-based sensitivity analysis","level":4,"score":0.3571999967098236},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3393000066280365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3264000117778778},{"id":"https://openalex.org/C132725507","wikidata":"https://www.wikidata.org/wiki/Q39879","display_name":"Monte Carlo integration","level":5,"score":0.3093000054359436},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.3021000027656555},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28769999742507935},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09117","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"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.09117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09117","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,32,61],"light":[1,33,70,166,183],"transport":[2,167,184],"simulation,":[3,168],"Markov":[4],"chain":[5],"Monte":[6,27],"Carlo":[7,28],"methods":[8,29],"are":[9],"particularly":[10,59],"effective":[11,94],"at":[12,47],"exploring":[13],"regions":[14],"with":[15],"complex":[16],"lighting":[17],"characteristics.":[18],"However,":[19],"estimator":[20],"variance":[21,36,79,95,115,129,157],"is":[22,109],"a":[23,81,99],"central":[24],"concern":[25],"across":[26],"in":[30,69,86,125,165],"general.":[31],"transport,":[34,71],"high":[35],"directly":[37],"manifests":[38],"as":[39],"increased":[40],"noise":[41],"or,":[42],"equivalently,":[43],"longer":[44],"rendering":[45],"times":[46],"fixed":[48],"image":[49],"quality.":[50],"Variance":[51],"reduction":[52,130,158],"techniques":[53],"based":[54],"on":[55,135],"Rao-Blackwellization":[56],"have":[57],"proven":[58],"effective.":[60],"practice,":[62],"however,":[63],"the":[64,104,140,145,177],"RB":[65,101,142],"approach":[66,143],"traditionally":[67],"used":[68],"waste-recycling,":[72],"can":[73],"yield":[74],"little":[75],"to":[76,144],"no":[77],"measurable":[78],"reduction,":[80,96],"fact":[82],"we":[83,97,138,169],"empirically":[84],"confirm":[85],"this":[87,91,120,136],"work.":[88],"Motivated":[89],"by":[90],"lack":[92],"of":[93,127],"introduce":[98],"novel":[100],"technique":[102,174],"for":[103,180],"general-purpose":[105,148],"Metropolis-Hastings":[106],"algorithm":[107],"that":[108,119,171],"computationally":[110],"efficient":[111],"and":[112,131,159,186,194],"achieves":[113,155],"substantial":[114,156],"reduction.":[116],"We":[117],"show":[118],"method":[121],"consistently":[122],"outperforms":[123,176],"waste-recycling":[124],"terms":[126],"both":[128,181,192],"convergence":[132],"speed.":[133],"Building":[134],"result,":[137],"adapt":[139],"proposed":[141],"recently":[146],"introduced":[147],"Jump":[149,187],"Restore":[150,188],"algorithm,":[151],"where":[152],"it":[153],"similarly":[154],"accelerated":[160],"convergence.":[161],"Through":[162],"extensive":[163],"experiments":[164],"demonstrate":[170],"our":[172],"\\gls{rb}":[173],"significantly":[175],"traditional":[178],"approaches":[179],"MH-based":[182],"algorithms":[185],"Light":[189],"Transport,":[190],"under":[191],"equal-time":[193],"equal-sample-count":[195],"comparisons.":[196]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}