In spite of two decades of intensive research, the reliable estimation of optic flow continues to be a key problem in computer vision.

In the present talk we study an energy functional for computing optic flow that combines three assumptions: a brightness constancy assumption, a gradient constancy assumption, and a discontinuity-preserving spatio-temporal smoothness constraint. In order to allow for large displacements, linearisations in the two data terms are strictly avoided. We present a consistent numerical scheme based on two nested fixed point iterations. By proving that this scheme implements a coarse-to-fine warping strategy, we give a theoretical foundation for warping which has been used on a mainly experimental basis so far.

Our evaluation demonstrates that the novel method gives significantly smaller angular errors than previous techniques for optic flow estimation. We show that it is fairly insensitive to parameter variations, and we demonstrate its excellent robustness under noise.