{"id":159362,"date":"2009-12-01T00:00:00","date_gmt":"2009-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/all-frequency-rendering-of-dynamic-spatially-varying-re%ef%ac%82ectance\/"},"modified":"2018-10-16T21:26:20","modified_gmt":"2018-10-17T04:26:20","slug":"all-frequency-rendering-of-dynamic-spatially-varying-re%ef%ac%82ectance","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/all-frequency-rendering-of-dynamic-spatially-varying-re%ef%ac%82ectance\/","title":{"rendered":"All-Frequency Rendering of Dynamic, Spatially-Varying Re\u00ef\u00ac\u201aectance"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We describe a technique for real-time rendering of dynamic,\u00a0spatially-varying BRDFs in static scenes with all-frequency shadows from environmental and point lights. The 6D SVBRDF is rep-<br \/>\nresented with a general microfacet model and spherical lobes \ufb01t to\u00a0its 4D spatially-varying normal distribution function (SVNDF). A\u00a0sum of spherical Gaussians (SGs) provides an accurate approximation with a small number of lobes. Parametric BRDFs are \ufb01t on-the-\ufb02y using simple analytic expressions; measured BRDFs are \ufb01t as a preprocess using nonlinear optimization. Our BRDF representation is compact, allows detailed textures, is closed under products\u00a0and rotations, and supports re\ufb02ectance of arbitrarily high specularity. At run-time, SGs representing the NDF are warped to align the half-angle vector to the lighting direction and multiplied by the microfacet shadowing and Fresnel factors. This yields the relevant 2D\u00a0view slice on-the-\ufb02y at each pixel, still represented in the SG basis.\u00a0We account for macro-scale shadowing using a new, nonlinear visibility representation based on spherical signed distance functions\u00a0(SSDFs). SSDFs allow per-pixel interpolation of high-frequency\u00a0visibility without ghosting and can be multiplied by the BRDF and\u00a0lighting ef\ufb01ciently on the GPU.<\/p>\n<p><iframe loading=\"lazy\" title=\"dragon rust - Precomputed Wave Simulation for Real-Time Sound Propagation\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/ux-IlJkkYkA?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<\/div>\n<p><iframe loading=\"lazy\" title=\"locals - All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/qBDpGmAOVLw?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p><iframe loading=\"lazy\" title=\"matusik03 - All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/aCyEKDarK8A?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p><iframe loading=\"lazy\" title=\"painting rust - All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/3AD1oY775C4?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p><iframe loading=\"lazy\" title=\"parametrics - All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/3K7XPg1lDos?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p><iframe loading=\"lazy\" title=\"redcard - All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/JPn36MaySXc?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p><iframe loading=\"lazy\" title=\"triange ssdf - All-Frequency Rendering of Dynamic, Spatially-Varying Reflectance\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/oSWwP03WDjw?feature=oembed&rel=0\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe a technique for real-time rendering of dynamic,\u00a0spatially-varying BRDFs in static scenes with all-frequency shadows from environmental and point lights. The 6D SVBRDF is rep- resented with a general microfacet model and spherical lobes \ufb01t to\u00a0its 4D spatially-varying normal distribution function (SVNDF). A\u00a0sum of spherical Gaussians (SGs) provides an accurate approximation with a small [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562,13563],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-159362","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"","msr_affiliation":"","msr_published_date":"2007-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"ACM Transactions on Graphics","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","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":"344864","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"sg","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/12\/sg.pdf","id":344864,"label_id":0},{"type":"file","title":"All-Frequency Rendering with Dynamic, Spatially-Varying Reflectance (Supplemental Material)","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/12\/sg_supp.pdf","id":360302,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":360302,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/12\/sg_supp.pdf"},{"id":344864,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/12\/sg.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"jiapw","user_id":32269,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jiapw"},{"type":"user_nicename","value":"mgong","user_id":32891,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mgong"},{"type":"user_nicename","value":"johnsny","user_id":32376,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=johnsny"},{"type":"user_nicename","value":"bainguo","user_id":31169,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=bainguo"},{"type":"text","value":"Peiran Ren","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/159362","targetHints":{"allow":["GET"]}}],"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":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/159362\/revisions"}],"predecessor-version":[{"id":535898,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/159362\/revisions\/535898"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=159362"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=159362"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=159362"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=159362"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=159362"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=159362"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=159362"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=159362"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=159362"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=159362"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=159362"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=159362"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=159362"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=159362"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=159362"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=159362"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}