CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning

• URL: http://arxiv.org/abs/2311.02661v1
• Date: Sun, 5 Nov 2023 14:14:24 GMT
• Title: CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning
• Authors: Azin Jahedi, Maximilian Luz, Marc Rivinius, Andr\'es Bruhn
• Abstract summary: We 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. Experiments and ablations demonstrate that our efforts of combining multi-scale and attention-based concepts pay off.
• Score: 1.0855602842179624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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