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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2207.12163 |
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| _version_ | 1866908382126931968 |
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| author | Jahedi, Azin Mehl, Lukas Rivinius, Marc Bruhn, Andrés |
| author_facet | Jahedi, Azin Mehl, Lukas Rivinius, Marc Bruhn, Andrés |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_12163 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Multi-Scale RAFT: Combining Hierarchical Concepts for Learning-based Optical FLow Estimation Jahedi, Azin Mehl, Lukas Rivinius, Marc Bruhn, Andrés Computer Vision and Pattern Recognition 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. |
| title | Multi-Scale RAFT: Combining Hierarchical Concepts for Learning-based Optical FLow Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2207.12163 |