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

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

0

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
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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