Lightweight Optical Flow Estimation Using 1D Matching

Recent advancements in optical flow estimation have led to notable performance gains, driven by the adoption of transformer architectures, enhanced data augmentation, self-supervised learning techniques, the use of multiple video frames and iterative refinement of estimate optical flows. Nonetheless, these cutting-edge methods encounter substantial challenges with surge in computational complexity and memory demands. In response, we introduce a lightweight optical flow method, called MaxFlow, to address the trade-off between computational complexity and prediction performance. By leveraging MaxViT, we design a network with a global receptive field at reduced complexity, and proposed 1D matching to alleviate the computational complexity from
$(H \times W)^{2}$
to
$H \times W(H+W)$
, wher H and W denotes height and width of input image. Consequently, our method achieves the lowest computational complexity compared to both state of the arts(SOTA) and other lightweight optical flow estimation methods, while still achieving competitive results with the SOTA techniques. We performed extensive experiments to show the effectiveness of our method, achieving about 5 to 6 times reductions in computation complexity while maintaining the prediction accuracy with only degradation of 16% in term of end point error(EPE) at Sintel test clean sequences with respect to RAFT method.