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Image-Based Rendering using Disparity Compensated Interpolation

Image-Based Rendering using Disparity Compensated Interpolation. EE362/PSYCH221 Class Project (Winter Quarter 2005-200 6) Aditya Mavlankar Information Systems Laboratory Stanford University. Outline. Virtual view synthesis using Image-Based Rendering (IBR) Brief survey of IBR techniques

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Image-Based Rendering using Disparity Compensated Interpolation

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  1. Image-Based Rendering using Disparity Compensated Interpolation EE362/PSYCH221 Class Project (Winter Quarter 2005-2006) Aditya Mavlankar Information Systems Laboratory Stanford University

  2. Outline • Virtual view synthesis using Image-Based Rendering (IBR) • Brief survey of IBR techniques • Goal of the project • Disparity Compensated Interpolation (DCI) • Results • Summary

  3. Virtual View Synthesis Background Foreground IBR techniques generate novel views from input images Camera 1 Virtual camera (novel view) Camera 2

  4. Brief Survey of IBR Techniques • Classification of IBR techniques according to [1] • Rendering with explicit geometry • E.g. 3-D warping, Layered Depth Image (LDI) rendering, View-dependent texture mapping • Rendering with implicit geometry • E.g. View interpolation, View morphing • Rendering without geometry • E.g. Light field rendering, Lumigraph systems [1] H. –Y. Shum, S. B. Kang and S. –C. Chan, “Survey of image-based representations and compression techniques,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, No. 11, pp 1020-1037, Nov. 2003.

  5. Goal of the project • Come up with an IBR technique which • Requires no depth information, no correspondence information • Works well when disparity between two key views is not too high • Computational complexity does not depend on scene complexity • New view-point anywhere on the line joining the two camera centers • Generate video of view-point traversal in a static natural scene • See effect of inserting novel views on viewing experience • How many intermediate novel views required for smooth view-point traversal?

  6. Disparity Compensated Interpolation 0 5 0 4 1 3 2 View from Camera 1 Novel view, in the making View from Camera 2

  7. Design Parameters in Block-Matching • Block size • small: Spurious matches • big: Cannot adapt to detail of the scene • Image resolution • small: inaccurate matching • big: computational time • Which color channel(s) to use for matching? • trust luminance or chrominance?

  8. Results: Cartoon Results: Cartoon

  9. Results: Cartoon Results: Ballet (256x192), played at 8 fps

  10. Results: Cartoon Results: Ballet (256x192), played at 10 fps

  11. Results: Cartoon Results: Ballet (512x384), played at 10 fps

  12. Future Directions • Avoid spurious matches • Enforce some sort of continuity of disparity vectors • Object segmentation might help • Adaptation of block-sizes according to image content

  13. Conclusion • HVS perspective: Novel views are critical for smooth traversal of view-point • novel views: the more the better (provided the quality of intermediate novel views is not too bad) • An IBR technique was designed which obviates the need for complex geometry

  14. The End

  15. Results: Cartoon Results: Ballet (512x384), played at 1 fps

  16. Results: Cartoon Results: Ballet (256x192), played at 1 fps

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