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<h1 class="title is-1 publication-title">Rethinking Optical Flow from Geometric Matching Consistent Perspective</h1>
<h2><font color="gray" size="5">CVPR 2023</font></h2>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/DQiaole">Qiaole Dong*</a>,</span>
<span class="author-block">
<a href="https://github.com/ewrfcas">Chenjie Cao*</a>,
</span>
<span class="author-block">
<a href="http://yanweifu.github.io/">Yanwei Fu</a>
</span>
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<span class="author-block">Fudan University</span>
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<h2 class="subtitle has-text-centered">
Overview of our MatchFlow. The simplified training pipeline is shown at the top, while details of each stage
are specifically listed below. H,W indicate 1/8 height and width of the input image respectively. Here the
GMA means global motion aggregation module.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
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 pretrained
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-theart
performance on Sintel clean and final pass compared
to published approaches with comparable computation and
memory footprint.
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<h2 class="title is-3">Compared with Other Methods</h2>
<h2 class="subtitle has-text-centered">
Quantitative comparison on standard benchmark. ‘A’ indicates the AutoFlow dataset. ‘C+T’: Succeeding training on FlyingChairs (C) and FlyingThings3D (T), we evaluate the capacity of generalization on Sintel (S) and KITTI (K) training sets. ‘C+T+S+K+H’: Training samples from T, S, K, and HD1K (H) are included in our training set for further finetuning. Results on training set are shown in parentheses. The top and second-place results are bolded and underlined, respectively. † indicates tile technique. And ⋆ indicates evaluating with RAFT’s “warm-start” strategy.
</h2>
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class="interpolation-image"
alt="Interpolate start reference image."/>
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</section>>
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<h2 class="title is-3">Qualitative Results</h2>
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<img src="./imgs/quanlitative.png"
class="interpolation-image"
alt="Interpolate start reference image."/>
</div>
<h2 class="subtitle has-text-centered">
Qualitative comparison on Sintel test set. First two rows are from clean pass, and the last two from final pass.
Notable areas are pointed out by arrows. Please zoom in for details.
</h2>
</div>
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</div>
</section>>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>
@inproceedings{dong2023rethinking,
title={Rethinking Optical Flow from Geometric Matching Consistent Perspective},
author={Dong, Qiaole and Cao, Chenjie and Fu, Yanwei},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023}
}
</code></pre>
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