MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame
  Optical Flow Estimation
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
https://github.com/msu-video-group/memfof.
686399bc588cea0da970c98f
Vladislav Bargatin
686399bc588cea0da970c990
Egor Chistov
claimed_verified
2025-07-02T07:40:49.662Z
67c6fcea10e0483cf431012e
https://cdn-avatars.huggingface.co/v1/production/uploads/67c6fcea10e0483cf431012e/yk_ng5qKtppQQ7LaH5IOl.jpeg
Egor Chistov
user
egorchistov
686399bc588cea0da970c991
Alexander Yakovenko
claimed_verified
2025-07-01T10:12:16.036Z
663692c75f67f8da32723bf8
/avatars/258264afe2ea5048a4a7a8e9945d2f5b.svg
Alexander Yakovenko
user
a-yakovenko
686399bc588cea0da970c992
Dmitriy Vatolin
MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame
  Optical Flow Estimation
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
https://github.com/msu-video-group/memfof.
MEMFOF is a memory-efficient multi-frame optical flow method that achieves state-of-the-art performance with reduced GPU memory usage for high-resolution inputs.
optical flow estimation MEMFOF GPU memory multi-frame estimation correlation volumes high-resolution training RAFT-like architectures Spring benchmark Sintel benchmark KITTI-2015 benchmark endpoint error Fl-all error