Optic Flow Goes Stereo: A Variational Approach for Estimating Discontinuity-Preserving Dense Disparity Maps

problems in computer vision. Two classical problems, where this task plays
a major role are optic flow computation and stereo reconstruction. While
correspondences between two consecutive images in time (optic flow) are
not subject to any restrictions, correspondences between two images that
differ by location (stereo reconstruction) have to fullfill the geometry
of two views (epipolar constraint).

In this talk we show how stereo reconstruction methods can benefit from
recent progress in optic flow computation. To this end, we propose a novel
variational model that is based on the currently most accurate optic flow
technique (Brox et al. 2004). By incorporating the epipolar constraint in
the estimation, the proposed model allows to transfer a variety of
advantages to the field of variational stereo reconstruction: (i)
Robustness under noise. (ii) Preservaton of discontinuites in the
solution. (iii) A theoretically justified minimsation strategy.

Experiments with both synthetic and real-world data show the excellent
performance and the noise robustness of our approach.