We present a detailed analysis of FALDOI, a large displacement optical flow method proposed by P. Palomares et al. This method requires a set of discrete matches, which can be extremely sparse, and an energy functional which locally guides the interpolation from the matches. It follows a two-step minimization method at the finest scale which is very robust to the outliers of the sparse matcher and can capture large displacements of small objects. The results shown in the original paper consistently outperformed the coarse-to-fine approaches and achieved good qualitative and quantitative performance on the standard optical flow benchmarks. In this paper we revise the proposed method and the changes done to significantly reduce its execution time while reporting nearly the same accuracy. Finally, we also compare it against the current state-of-the-art to assess its performance.

The authors acknowledge support by TIN2015-70410-C2-1-R(MINECO/FEDER, UE) project and by H2020-MSCA-RISE-2017 project, reference 777826 NoMADS.