CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine
  Context-Guided Motion Reasoning
Azin Jahedi Maximilian Luz Marc Rivinius Andrés Bruhn
Attention-based motion aggregation concepts have recently shown their
usefulness in optical flow estimation, in particular when it comes to handling
occluded regions. However, due to their complexity, such concepts have been
mainly restricted to coarse-resolution single-scale approaches that fail to
provide the detailed outcome of high-resolution multi-scale networks. In this
paper, we hence propose CCMR: a high-resolution coarse-to-fine approach that
leverages attention-based motion grouping concepts to multi-scale optical flow
estimation. CCMR relies on a hierarchical two-step attention-based
context-motion grouping strategy that first computes global multi-scale context
features and then uses them to guide the actual motion grouping. As we iterate
both steps over all coarse-to-fine scales, we adapt cross covariance image
transformers to allow for an efficient realization while maintaining
scale-dependent properties. Experiments and ablations demonstrate that our
efforts of combining multi-scale and attention-based concepts pay off. By
providing highly detailed flow fields with strong improvements in both occluded
and non-occluded regions, our CCMR approach not only outperforms both the
corresponding single-scale attention-based and multi-scale attention-free
baselines by up to 23.0% and 21.6%, respectively, it also achieves
state-of-the-art results, ranking first on KITTI 2015 and second on MPI Sintel
Clean and Final. Code and trained models are available at
https://github.com/cv-stuttgart /CCMR.