Variational Large Displacement Optical Flow Without Feature Matches

The optical flow within a scene can be an arbitrarily complex composition of motion patterns that typically differ regarding their scale. Hence, using a single algorithm with a single set of parameters is often not sufficient to capture the variety of these motion patterns. In particular, the estimation of large displacements of small objects poses a problem. In order to cope with this problem, many recent methods estimate the optical flow by a fusion of flow candidates obtained either from different algorithms or from the same algorithm using different parameters. This, however, typically results in a pipeline of methods for estimating and fusing the candidate flows, each requiring an individual model with a dedicated solution strategy. In this paper, we investigate what results can be achieved with a pure variational approach based on a standard coarse-to-fine optimization. To this end, we propose a novel variational method for the simultaneous estimation and fusion of flow candidates. By jointly using multiple smoothness weights within a single energy functional, we are able to capture different motion patterns and hence to estimate large displacements even without additional feature matches. In the same functional, an intrinsic model-based fusion allows to integrate all these candidates into a single flow field, combining sufficiently smooth overall motion with locally large displacements. Experiments on large displacement sequences and the Sintel benchmark demonstrate the feasibility of our approach and show improved results compared to a single-smoothness baseline method.