{"id":576507,"date":"2019-03-29T23:51:41","date_gmt":"2019-03-30T06:51:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=576507"},"modified":"2019-04-09T23:25:41","modified_gmt":"2019-04-10T06:25:41","slug":"adaptive-sampling-for-sound-propagation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/adaptive-sampling-for-sound-propagation\/","title":{"rendered":"Adaptive Sampling For Sound Propagation"},"content":{"rendered":"

Precomputed sound propagation samples acoustics at discrete scene probe positions to support dynamic listener locations. An of\ufb02ine 3D numerical simulation is performed at each probe and the resulting \ufb01eld is encoded for runtime rendering with dynamic sources. Prior work place probes on a uniform grid, requiring high density to resolve narrow spaces. Our adaptive sampling approach varies probe density based on a novel \u201clocal diameter\u201d measure of the space surrounding a given point, evaluated by stochastically tracing paths in the scene. We apply this measure to layout probes so as to smoothly adapt resolution and eliminate undersampling in corners, narrow corridors and stairways, while coarsening appropriately in more open areas. Coupled with a new runtime interpolator based on radial weights over geodesic paths, we achieve smooth acoustic effects that respect scene boundaries as both the source or listener move, unlike existing visibility-based solutions. We consistently demonstrate quality improvement over prior work at \ufb01xed cost.<\/p>\n","protected":false},"excerpt":{"rendered":"

Precomputed sound propagation samples acoustics at discrete scene probe positions to support dynamic listener locations. An of\ufb02ine 3D numerical simulation is performed at each probe and the resulting \ufb01eld is encoded for runtime rendering with dynamic sources. Prior work place probes on a uniform grid, requiring high density to resolve narrow spaces. Our adaptive sampling […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[243062,13551],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-576507","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-audio-acoustics","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-5-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"IEEE Transactions on Visualization and Computer Graphics","msr_volume":"25","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"5","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/03\/AdaptiveSamplingSoundProp.pdf","id":"576510","title":"adaptivesamplingsoundprop","label_id":"243132","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"10.1109\/TVCG.2019.2898765","label_id":"243106","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":576510,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/03\/AdaptiveSamplingSoundProp.pdf"}],"msr-author-ordering":[{"type":"text","value":"C. R. A. Chaitanya","user_id":0,"rest_url":false},{"type":"user_nicename","value":"John Snyder","user_id":32376,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=John Snyder"},{"type":"text","value":"Keith Godin","user_id":0,"rest_url":false},{"type":"text","value":"Derek Nowrouzezahrai","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Nikunj Raghuvanshi","user_id":33106,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Nikunj Raghuvanshi"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[],"msr_project":[546345],"publication":[],"video":[],"download":[],"msr_publication_type":"article","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/576507"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/576507\/revisions"}],"predecessor-version":[{"id":576513,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/576507\/revisions\/576513"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=576507"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=576507"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=576507"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=576507"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=576507"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=576507"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=576507"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=576507"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=576507"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=576507"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=576507"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=576507"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=576507"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=576507"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=576507"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}