Optical flow estimation is a challenging problem remaining unsolved. Recent
deep learning based optical flow models have achieved considerable success.
However, these models often train networks from the scratch on standard optical
flow data, which restricts their ability to robustly and geometrically match
image features. In this paper, we propose a rethinking to previous optical flow
estimation. We particularly leverage Geometric Image Matching (GIM) as a
pre-training task for the optical flow estimation (MatchFlow) with better
feature representations, as GIM shares some common challenges as optical flow
estimation, and with massive labeled real-world data. Thus, matching static
scenes helps to learn more fundamental feature correlations of objects and
scenes with consistent displacements. Specifically, the proposed MatchFlow
model employs a QuadTree attention-based network pre-trained on MegaDepth to
extract coarse features for further flow regression. Extensive experiments show
that our model has great cross-dataset generalization. Our method achieves
11.5% and 10.1% error reduction from GMA on Sintel clean pass and KITTI test
set. At the time of anonymous submission, our MatchFlow(G) enjoys
state-of-the-art performance on Sintel clean and final pass compared to
published approaches with comparable computation and memory footprint. Codes
and models will be released in this https URL

本研究提出了一种基于深度学习和几何图像匹配的光学流估计方法 MatchFlow，通过先使用几何图像匹配（GIM）作为预训练任务，再进行光流估计，从而提高了图像特征的匹配性能，实验结果表明该模型性能优异，相较于已发表的方法，拥有最优的表现。