  Many classical and learning-based optical flow methods rely on hierarchical
concepts to improve both accuracy and robustness. However, one of the currently
most successful approaches -- RAFT -- hardly exploits such concepts. In this
work, we show that multi-scale ideas are still valuable. More precisely, using
RAFT as a baseline, we propose a novel multi-scale neural network that combines
several hierarchical concepts within a single estimation framework. These
concepts include (i) a partially shared coarse-to-fine architecture, (ii)
multi-scale features, (iii) a hierarchical cost volume and (iv) a multi-scale
multi-iteration loss. Experiments on MPI Sintel and KITTI clearly demonstrate
the benefits of our approach. They show not only substantial improvements
compared to RAFT, but also state-of-the-art results -- in particular in
non-occluded regions. Code will be available at
https://github.com/cv-stuttgart/MS_RAFT.
