SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow
<|reference_start|>SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow: We introduce SEA-RAFT, a more simple, efficient, and accurate RAFT for optical flow. Compared with RAFT, SEA-RAFT is trained with a new loss (mixture of Laplace). It directly regresses an initial flow for faster convergence in iterative refinements and introduces rigid-motion pre-training to improve generalization. SEA-RAFT achieves state-of-the-art accuracy on the Spring benchmark with a 3.69 endpoint-error (EPE) and a 0.36 1-pixel outlier rate (1px), representing 22.9% and 17.8% error reduction from best published results. In addition, SEA-RAFT obtains the best cross-dataset generalization on KITTI and Spring. With its high efficiency, SEA-RAFT operates at least 2.3x faster than existing methods while maintaining competitive performance. The code is publicly available at https://github.com/princeton-vl/SEA-RAFT.<|reference_end|>
@article{wang2024sea-raft:,
    title={SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow},
    author={Yihan Wang, Lahav Lipson, Jia Deng},
    journal={arXiv preprint arXiv:2405.14793},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14793},
    primaryClass={cs.CV}
}