logging in or signing up Seitz compPhoto05 Michelino Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 33 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: January 14, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Analyzing Impossible Images: Analyzing Impossible Images Steve Seitz University of Washington Computational Photography Symposium May 23, 2004Imaging Breakthroughs: Imaging Breakthroughs photography, moving pictures xray, ultrasound, MRI, etc. Etienne-Jules Marey, falling catImaging Desiderata: Imaging Desiderata Analyzing real images is a pain occlusions clutter shading focus fidelity Impossible images don’t have such problems can computational imaging make these problems go away?Removing Occlusions: Rollout Photographs © Justin Kerr http://research.famsi.org/kerrmaya.html Removing OcclusionsSlide5: The Blue Marble, NASA satellite image compositeSlide6: The Blue Marble, NASA satellite image compositeSlide7: by David DeweySlide8: by Jiwon KimOpen Questions: Open Questions How much visibility can we get? sensor design Many possible projections How do we process these images?Removing Interreflection: Removing Interreflection Images by Ward et al., SIGGRAPH 88 Bounce Images: Bounce ImagesMain Results: Main Results There exists a matrix C1 that removes all interreflections in a photograph (or lightfield) Works for any illumination There is a matrix Ck that retains only the kth bounceLight transport: Light transport TThe transport matrix: The transport matrix = T Lin Lout Accounts for interreflections, shadows, refraction, subsurface scatter, ... [Dorsey 94] [Zongker 99] [Debevec 00] [Peers 03] [Goesele 05] [Sen 05] ...The transport matrix: The transport matrix = T Lin Lout Direct illumination rendering: Direct illumination rendering = T1 Lin L1out Single bounce from light to eye no interreflectionsInverse rendering: Inverse rendering = T-1 Lin LoutRemoving Interreflections: Removing Interreflections L1out Lout T-1 T1 C1 Second derivation: from the rendering equation [Kajiya 86] [Cohen 86]Cancellation Operators: Cancellation Operators Recursively define other operators (I – C1) gives interreflected light C2 = C1 (I – C1) gives second bounce of light Ck = C1(I – C1)k-1 gives kth bounce of light Inverse ray tracing!How to compute C1: How to compute C1 Simplified case Lambertian reflectance and fixed viewpoint Lin and Lout are 2D Can capture T by scanning a laser Synthetic M Scene: Synthetic M Scene 1 2 3 4 Slide24: real dataSlide27: (x3) (x3) (x6) (x15) flash light illuminationCollaborators on Interreflections: Collaborators on Interreflections Kyros Kutulakos (U. Toronto) Yasuyuki Matsushita (MSR Asia) S. M. Seitz, Y. Matsushita, and K. Kutulakos, “A Theory of Inverse Light Transport,” Microsoft Technical Report MSR-TR-2005-66, May 2005.Conclusions: Conclusions Impossible images no occlusions, no interreflections Better sensing techniques can they solve all analysis problems? shape tracking recognition What other kinds of “impossible” images do we want? You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
Seitz compPhoto05 Michelino Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 33 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: January 14, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Analyzing Impossible Images: Analyzing Impossible Images Steve Seitz University of Washington Computational Photography Symposium May 23, 2004Imaging Breakthroughs: Imaging Breakthroughs photography, moving pictures xray, ultrasound, MRI, etc. Etienne-Jules Marey, falling catImaging Desiderata: Imaging Desiderata Analyzing real images is a pain occlusions clutter shading focus fidelity Impossible images don’t have such problems can computational imaging make these problems go away?Removing Occlusions: Rollout Photographs © Justin Kerr http://research.famsi.org/kerrmaya.html Removing OcclusionsSlide5: The Blue Marble, NASA satellite image compositeSlide6: The Blue Marble, NASA satellite image compositeSlide7: by David DeweySlide8: by Jiwon KimOpen Questions: Open Questions How much visibility can we get? sensor design Many possible projections How do we process these images?Removing Interreflection: Removing Interreflection Images by Ward et al., SIGGRAPH 88 Bounce Images: Bounce ImagesMain Results: Main Results There exists a matrix C1 that removes all interreflections in a photograph (or lightfield) Works for any illumination There is a matrix Ck that retains only the kth bounceLight transport: Light transport TThe transport matrix: The transport matrix = T Lin Lout Accounts for interreflections, shadows, refraction, subsurface scatter, ... [Dorsey 94] [Zongker 99] [Debevec 00] [Peers 03] [Goesele 05] [Sen 05] ...The transport matrix: The transport matrix = T Lin Lout Direct illumination rendering: Direct illumination rendering = T1 Lin L1out Single bounce from light to eye no interreflectionsInverse rendering: Inverse rendering = T-1 Lin LoutRemoving Interreflections: Removing Interreflections L1out Lout T-1 T1 C1 Second derivation: from the rendering equation [Kajiya 86] [Cohen 86]Cancellation Operators: Cancellation Operators Recursively define other operators (I – C1) gives interreflected light C2 = C1 (I – C1) gives second bounce of light Ck = C1(I – C1)k-1 gives kth bounce of light Inverse ray tracing!How to compute C1: How to compute C1 Simplified case Lambertian reflectance and fixed viewpoint Lin and Lout are 2D Can capture T by scanning a laser Synthetic M Scene: Synthetic M Scene 1 2 3 4 Slide24: real dataSlide27: (x3) (x3) (x6) (x15) flash light illuminationCollaborators on Interreflections: Collaborators on Interreflections Kyros Kutulakos (U. Toronto) Yasuyuki Matsushita (MSR Asia) S. M. Seitz, Y. Matsushita, and K. Kutulakos, “A Theory of Inverse Light Transport,” Microsoft Technical Report MSR-TR-2005-66, May 2005.Conclusions: Conclusions Impossible images no occlusions, no interreflections Better sensing techniques can they solve all analysis problems? shape tracking recognition What other kinds of “impossible” images do we want?