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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.01443 |
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| _version_ | 1866909632435322880 |
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| author | Schmid, Jakob Jahedi, Azin Senn, Noah Berenguel Bruhn, Andrés |
| author_facet | Schmid, Jakob Jahedi, Azin Senn, Noah Berenguel Bruhn, Andrés |
| contents | Although multi-scale concepts have recently proven useful for recurrent network architectures in the field of optical flow and stereo, they have not been considered for image-based scene flow so far. Hence, based on a single-scale recurrent scene flow backbone, we develop a multi-scale approach that generalizes successful hierarchical ideas from optical flow to image-based scene flow. By considering suitable concepts for the feature and the context encoder, the overall coarse-to-fine framework and the training loss, we succeed to design a scene flow approach that outperforms the current state of the art on KITTI and Spring by 8.7%(3.89 vs. 4.26) and 65.8% (9.13 vs. 26.71), respectively. Our code is available at https://github.com/cv-stuttgart/MS-RAFT-3D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01443 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MS-RAFT-3D: A Multi-Scale Architecture for Recurrent Image-Based Scene Flow Schmid, Jakob Jahedi, Azin Senn, Noah Berenguel Bruhn, Andrés Computer Vision and Pattern Recognition Although multi-scale concepts have recently proven useful for recurrent network architectures in the field of optical flow and stereo, they have not been considered for image-based scene flow so far. Hence, based on a single-scale recurrent scene flow backbone, we develop a multi-scale approach that generalizes successful hierarchical ideas from optical flow to image-based scene flow. By considering suitable concepts for the feature and the context encoder, the overall coarse-to-fine framework and the training loss, we succeed to design a scene flow approach that outperforms the current state of the art on KITTI and Spring by 8.7%(3.89 vs. 4.26) and 65.8% (9.13 vs. 26.71), respectively. Our code is available at https://github.com/cv-stuttgart/MS-RAFT-3D. |
| title | MS-RAFT-3D: A Multi-Scale Architecture for Recurrent Image-Based Scene Flow |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.01443 |