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Spatial and Temporal Interpolation of Multi-view Image Sequences

Part of the Lecture Notes in Computer Science book series (LNIP,volume 8753)


We propose a simple and effective framework for multi-view image sequence interpolation in space and time. For spatial view point interpolation we present a robust feature-based matching algorithm that allows for wide-baseline camera configurations. To this end, we introduce two novel filtering approaches for outlier elimination and a robust approach for match extrapolations at the image boundaries. For small-baseline and temporal interpolations we rely on an established optical flow based approach. We perform a quantitative and qualitative evaluation of our framework and present applications and results. Our method has a low runtime and results can compete with state-of-the-art methods.


  • Optical Flow
  • Image Boundary
  • Image Interpolation
  • Temporal Interpolation
  • Epipolar Constraint

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work was supported by the ERC Starting Grant “Convex Vision”.

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Gurdan, T., Oswald, M.R., Gurdan, D., Cremers, D. (2014). Spatial and Temporal Interpolation of Multi-view Image Sequences. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham.

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