Abstract
Recent advances in optical flow estimation have prioritized accuracy at the cost of growing GPU memory consumption, particularly for high-resolution (FullHD) inputs. We introduce MEMFOF, a memory-efficient multi-frame optical flow method that identifies a favorable trade-off between multi-frame estimation and GPU memory usage. Notably, MEMFOF requires only 2.09 GB of GPU memory at runtime for 1080p inputs, and 28.5 GB during training, which uniquely positions our method to be trained at native 1080p without the need for cropping or downsampling. We systematically revisit design choices from RAFT-like architectures, integrating reduced correlation volumes and high-resolution training protocols alongside multi-frame estimation, to achieve state-of-the-art performance across multiple benchmarks while substantially reducing memory overhead. Our method outperforms more resource-intensive alternatives in both accuracy and runtime efficiency, validating its robustness for flow estimation at high resolutions. At the time of submission, our method ranks first on the Spring benchmark with a 1-pixel (1px) outlier rate of 3.289, leads Sintel (clean) with an endpoint error (EPE) of 0.963, and achieves the best Fl-all error on KITTI-2015 at 2.94%. The code is available at this https URL.
Abstract (translated)
最近的光流估计进展优先考虑了精度，但代价是GPU内存消耗不断增加，尤其是在处理高分辨率（全高清）输入时。我们引入了一种名为MEMFOF的记忆高效多帧光流方法，该方法在多帧估计和GPU内存使用之间找到了一个有利的权衡点。值得注意的是，对于1080p输入，MEMFOF只需要2.09GB的GPU运行时内存，并且训练过程中的内存消耗为28.5GB，这使我们的方法能够在原生1080p分辨率下进行训练而无需裁剪或降采样。 我们系统地重新审视了与RAFT架构类似的设计选择，结合减少了的相关体积和高分辨率训练协议以及多帧估计的方法，从而在多个基准测试中达到了最先进的性能，并显著降低了内存开销。我们的方法在精度和运行时效率上都优于资源消耗更大的替代方案，验证了其在高分辨率下的稳健性。 提交时，我们的方法在Spring基准测试中以3.289的1像素（1px）异常率排名第一，在Sintel（干净版）中以0.963的终点误差（EPE）领先，并且在KITTI-2015上取得了最佳的Fl-all误差2.94%。代码可在以下链接获取：[此处插入实际链接]
URL
https://arxiv.org/abs/2506.23151
