{"id":423504,"date":"2017-09-06T12:41:05","date_gmt":"2017-09-06T19:41:05","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=423504"},"modified":"2018-10-16T20:03:14","modified_gmt":"2018-10-17T03:03:14","slug":"modeling-subsurface-light-transport-kernel-nystrom-method","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/modeling-subsurface-light-transport-kernel-nystrom-method\/","title":{"rendered":"Modeling Subsurface Light Transport with the Kernel Nystr\u00c3\u00b6m Method"},"content":{"rendered":"

Chapter 6 presents a kernel Nystr\u00f6m method for reconstructing the light transport matrix, which models light transport from each light source to each camera pixel, from a relatively small number of acquired images. This work is based on the generalized Nystr\u00f6m method for low rank matrices. The light transport kernel is introduced and incorporated into the Nystr\u00f6m method to exploit the nonlinear coherence of the light transport matrix data. An adaptive scheme is also developed for efficiently capturing the sparsely sampled images from the scene. Experiments indicate that the kernel Nystr\u00f6m method can achieve good reconstruction of the light transport matrix with a few hundred images and produce high quality relighting results. The kernel Nystr\u00f6m method is effective for modeling scenes with complex lighting effects and occlusions which have been challenging for existing techniques.<\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 6 presents a kernel Nystr\u00f6m method for reconstructing the light transport matrix, which models light transport from each light source to each camera pixel, from a relatively small number of acquired images. This work is based on the generalized Nystr\u00f6m method for low rank matrices. The light transport kernel is introduced and incorporated into […]<\/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":[13562],"msr-publication-type":[193721],"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-423504","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2013-05-04","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"Material Appearance Modeling: A Data-Coherent 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