Image based rendering using disparity compensated interpolation
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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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Image based rendering using disparity compensated interpolation

Image-Based Rendering using Disparity Compensated Interpolation

EE362/PSYCH221 Class Project

(Winter Quarter 2005-2006)

Aditya Mavlankar

Information Systems Laboratory

Stanford University


Outline

Outline

  • Virtual view synthesis using Image-Based Rendering (IBR)

  • Brief survey of IBR techniques

  • Goal of the project

  • Disparity Compensated Interpolation (DCI)

  • Results

  • Summary


Virtual view synthesis

Virtual View Synthesis

Background

Foreground

IBR techniques generate novel views from input images

Camera 1

Virtual camera (novel view)

Camera 2


Brief survey of ibr techniques

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.


Goal of the project

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?


Disparity compensated interpolation

Disparity Compensated Interpolation

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5

0

4

1

3

2

View from Camera 1

Novel view, in the making

View from Camera 2


Design parameters in block matching

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?


Results cartoon

Results: Cartoon

Results: Cartoon


Results cartoon1

Results: Cartoon

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


Results cartoon2

Results: Cartoon

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


Results cartoon3

Results: Cartoon

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


Future directions

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


Conclusion

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


The end

The End


Results cartoon4

Results: Cartoon

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


Results cartoon5

Results: Cartoon

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


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