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twhui · web-flow · commit a31d5ce11c03 · 2022-05-17T09:48:57.000+08:00
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 # LiteFlowNet
-This repository (<strong>https://github.com/twhui/LiteFlowNet</strong>) is the offical release of <strong>LiteFlowNet</strong> for my paper <a href="https://arxiv.org/pdf/1805.07036.pdf"><strong>LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation</strong></a> in CVPR 2018 (Spotlight). <i>The up-to-date version of the paper is available on <a href="https://arxiv.org/pdf/1805.07036.pdf"><strong>arXiv</strong></a></i>. 
+<p align="center"><img src="./figure/LiteFlowNet.png" width="800" /></p>
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+This repository (<strong>https://github.com/twhui/LiteFlowNet</strong>) is the offical release of <strong>LiteFlowNet</strong> for my paper <a href="https://arxiv.org/pdf/1805.07036.pdf"><strong>LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation</strong></a> in CVPR 2018 (Spotlight paper, 6.6%). <i>The up-to-date version of the paper is available on <a href="https://arxiv.org/pdf/1805.07036.pdf"><strong>arXiv</strong></a></i>. 
 
 LiteFlowNet is a lightweight, fast, and accurate opitcal flow CNN. We develop several specialized modules including (1) pyramidal features, (2) cascaded flow inference (cost volume + sub-pixel refinement), (3) feature warping (f-warp) layer, and (4) flow regularization by feature-driven local convolution (f-lconv) layer. LiteFlowNet outperforms PWC-Net (CVPR 2018) on KITTI and has a smaller model size (less than PWC-Net by ~40%).
 
